THESIS

 

This is to certify that the

dissertation entitled

ENERGY CONSUMPTION AND PERFORMANCE MDDELS
OF SMALL PHILIPPINE-BUILT RICE MILIS

presented by

ANACIEIU SAWAL PARAS, JR.

has been accepted towards fulfillment
of the requirements for

J); W A A‘.’ /“ -, ’) /-' t l
/ it / degreein T? E“?! [w L Ml”?

L /

 

 

MI,

K Major professor
Merle L. Esmay

 

Datel/f 0/8?

MS U is an Affirmative Action/Equal Opportunity Institution 0-12771

 

 

 

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““7

ENERGY CONSUMPTION AND PERFORMANCE MODELS

OF SMALL PHILIPPINE-BUILT RICE MILLS

BY

Anacleto Sawal Paras, Jr.

A DISSERTATION

Submitted to
Michigan State University
.111 partial fulfillment of the requirements

for the degree of

DOCTOR OF PHILOSOPHY

Department of Agricultural Engineering

1984

ABSTRACT

ENERGY CONSUMPTION AND PERFORMANCE MODELS
OF SMALL PHILIPPINE-BUILT RICE MILLS
BY

Anacleto Sawal Paras, Jr.

Two simulation models were developed for small rice
mills of the conventional disc-cone and rubber-roll
equipped designs which range from 0.3 to 1.8 tons-per-hour
capacity. These sizes comprise a large proportion of the
rice mills in the Philippines.

The first, a computer model, evaluated these two
types of mills with regard to energy consumption, total and
head grain recovery and processing time. Field and
laboratory data taken by UPLB research workers and direct
measurements by the author were compiled and employed in
the development of equations and distribution functions for
the variables that make up the subroutines for the models.
The results indicated that the energy consumption of small
rice mills in the Philippines could be reduced by five to
nineteen percent, depending on size, without loss of

quality in good performance mills by using one bigger

Anacleto Sawal Paras, Jr.

huller and an adjustable separator, and that the output
quality of poor performance mills could be improved with
just a four percent increase in energy consumption by
adding a second-stage whitener.

The second model estimated the cost of milled rice
by utilizing Kirchoff's current and voltage laws and energy
conservation principles to derive'a cost equation involving
the material energy and processing cost. The results
indicated that the cost of milling rice in 1978 was
approximately Pl.20 per kilogram of milled rice after
‘crediting for by-product cost (P0.05) with the conventional

disc-cone mill being less expensive than the rubber-roll

type by about P0.02 (1.7 percent).

Approved MML

Majk Professor

. (‘2
Appr ovedlflmflimédfl—

Department Chairman

To my late Father

ii

ACKNOWLEDGMENTS

I gratefully acknowledge the support of many
individuals and organizations who contributed to this
study:

Dr. Merle Esmay, my major professor, for his
guidance and patience in editing the various drafts of this
dissertation.

Drs. Thomas Burkhardt, Robert Wilkinson, Robert
Stevens, WarrenfiVincent and Michael Abkin who serve as
members of the Guidance Committee for their advice.

Dr. David Horner for his encouragement and support.

Dr. Dante B. de Padua for his encouragement and
support in gathering the secondary data.

Dr. Ernesto Lozada and his staff particularly
Messrs. Ruben Manalabe and Rosendo Rapusas for providing
the physical support in the gathering of the field data.
Also, to Mr. Reginaldo Gonzales for providing the
photographs.

National Grains Authority Directors Frank
Tua and Maximx> de Ramos for providing the rice mills and
paddy in the rice mill tests. Also, to Messrs. Antonio
Cruz, Steve Solitario and their staff for their help

during the rice mill tests.

iii

The University of the Philippines at Los Banos and
the IBRD for making my studies in the U.S.A. possible.

The Instructional Media Center at M.S.U. for their
help in the preparation of my illustrations.

The South East Asia Regional Center for Agriculture
for their support in the library research.

Mr. Robert Clark for editing the later drafts of
this dissertation.

Mrs. Lucille Lewsader for typing the final draft
and Miss Betty Halsted for typing the pre-final draft.

The Office of International Students and Scholars

for their support in the printing of this dissertation.

My wife Florita for typing a continually changing
manuscript.

My children Philipp, Parnee, Phenella and Pierre
for their unending patience and understanding during the

preparation of the manuscript.

iv

TABLE OF CONTENTS

Page
LIST OF TABLES o o o e o o o o o o o o o o o o o o o 0 ix
LIST OF FIGURES o o o o o o “o o o o o o o o o o o o o XiV
chapter
I. INTRODUCTION 0 o o o o o o o o o o o o o o 1
1.1 Purpose of the Study . . . . . . . . . 2
1.2 Specific Objectives . . . . . . . . . 7
1.3 Description of the Models . . . . . . 7
1.3.1 Rice Mill Perfdrmance . . . . . 7

1.3.2 Linear Network Economic Model . 12
II. REVIEW OF LITERATURE . . . . . . . . . . . 14
2.1 Nomenclature . . . . . . . . . . . . 14

2.2 Anatomy of the Rice Grain
and Its Significance . . . . . . . . 17

2.3 Rice Milling Process . . . . . . . . . 22

2.4 Input Paddy Factors . . . . . . . . . 24
2.5 Milling FaCtorS O O O O O O O O O O O 25

2.6 Ambient Relative Humidity
and Temperature . . . . . . . . . . . 26

2.7 Economic Factors . . . . . . . . . . . 30

2.8 Systems Technique Used in
the Modeling . . . . . . . . . . . . 31

2.9 Summary of Review of
Literature 0 O O O O O O O O O O O O 38

Chapter
III.

RICE MILL PERFORMANCE MODEL . . . . .
3.1 Model Definition . . . . . . . .
3.2 Grain Variables . . . . . . . .
3.2.1 Input Paddy Factors . . .
3.2.1.1 Maturity at Harvest . .
3.2.1.2 Delay in Drying . . . .

3.2.1.3 Moisture Content
at Milling . . . . . .

3.3 Grain Shape Measurements . . . .

3.4 Measurements of Energy
Consumption and
Milling Time . . . . . . . . .

3.4.1 Methodology . . . . . . .
3.4.2 Analysis of Results . . .

3.4.3 Discussion of the
Different Machine
Components . . . . . . .

3.5 Distribution Functions of
Input Paddy Factors . . . . . .

3.6 Computer Implementation of
the "Odell O O O O O O O O O O O

3.7 An Example of How to Use the
the Rice Mill Performance Model

LINEAR NETWORK ECONOMIC MODEL . . . .

4.1 Theory of Linear Network
Economic Model . . . . . . . .

4.1.1 Kirchoff's Circuit Laws .
4.2 Procedure . . . . . . . . . . .

4.2.1 Energy Notations . . . .

vi

4O
40
43
44
44
49

54
64

66
66

67

70

98

101

105
109

110
110
117
117

Chapter

V.

VI.

4.2.1.1
4.2.1.2
4.2.1.3

4.2.1.4

4.2.1.5

4.2.1.6
4.2.1.7
4.2.1.8

4.2.1.9

Precleaner Component
Huller Component . .

Plansifter and
Aspirator . . . . .

Paddy Separator
Component . . . . .

First Stage Whitener
Component . . . . .

Grader Component . .
Transport Component .

Return Huller
compo ne nt 0 I O O 0

Rice Milling System
Economic Model . . .

ANALYSIS OF THE RESULT . . . . . .

5.1 Rice Mill Performance Model .

5.2 Linear Network Economic
Model .

CONCLUSIONS AND RECOMMENDATIONS . .

IBDBLIOGRAPHY . . . .

APPENDIX
A.

B.

C.

D.

Energy Consumption Measurement

Results .

Rice Mill Performance Simulation
Model Program . . . . . . . . . .

Gamma Distribution Function . . . .

Chi-Square Analysis of Random

Variables

vii

121
122

122

124

124
124

127

129

130
148

148

171
179
184

188

194
204

206

Appendi x

H.
I.

J.

K.

L.

M.
N.
o.

.P.

Q.
R.

Validation of the Linear Network
AnalYSi S O O O O O O O O O O O O 0 O

Derivation of Linear Network
“Odel- O O O O O O O O O O O O O O 0

Values of Linear Network
Coefficients . . . . . . . . . . . .

Evaluation of the Linear
Network Equations . . . . . . . . .

Results of the Rice Mill Performance
Model Simulations . . . . . . . . .

Some Physical Properties of Rice . .

Relationship of Milling Recovery
and Moisture Content at Milling . .

Time Studies on Rice Mills . . . . .

Linear Network Economic Model

Computer Program . . . . . . . . . .

Philippine Trade Standards
for Rice . . . . . . . . . . . . .

Normal Values of Los Banos
Weather Data . . . . . . . . . . . .

Economic Variables Used in the
Network Equations . . . . . . . . .

Cost of Rubber Rolls . . . . . . . .

Paddy Planting Area in the
Philippines . . . . . . . . . . . .

Rice Mill Statistics in the
Philippines . . . . . . . . . . . .

viii

209

211

216

218

224
261

263
272

275

277

295

299
300

302

303

LIST OF TABLES
Table

1.1 Comparative Performance of Mills
Monitored in Bicol River Basin . . . . . . 5

3.1 Results of Simulation Runs . . . . . . . . 107

5.1 Rice Mill Types Manufactured
in the Philippines . . . . . . . . . . . . 149

5.2 Results of Simulation Runs . . . . . . . . 151
5.3 Rice Milling Rates of Small Disc-

Cone Mill by Region in the

Philippines 0 O O O O O O 0 O 0 O O O O O 152

5.4 Engine Sizes and Capacities of
Disc-Cone Rice Mills . . . . . . . . . . . 152

5.5 Milling Recovery and Head Grain

Percentage as Affected by

HarveSt Date 0 O O O O O O O O O O O O O O 153
5.6 Results of Simulation Runs . . . . . . . . 154
5.7 Results of Simulation Runs . . . . . . . . 155
5.8 Results of Simulation Runs . . . . . . . . 156
5.9 Results of Simulation Runs . . . . . . . . 157
5.10 Results of Simulation Runs . . . . . . . . 158
5.11 Results of Simulation Runs . . . . . . . . 159
5.12 Summary of Results of Simulation . . . . . 165
5.13 Results of Economic Modeling when

Milling Recovery of Rubber Roll

and Conventional Mill are Equal . . . . . 173

5.14 Results of Economic Modeling
of Six Different Mills . . . . . . . . . . 174

ix

Tabl e Page

5.15 Results of Economic Modeling with

Increased Rubber Roll Prices . . . . . . . 176
A1 Energy Consumption Measurements

of Three Philippine Mills . . . . . . . . 189
A2 Computed Energy Requirement

of Separators . . . . . . . . . . . . . . 191
A3 Energy Consumption of Japanese

Rice "1118 O O O O O O O O O O O O O O 0 O 191
A4 Data on Machine Capacities . . . . . . . . 192
A5 Material Reduction for Different

maChines O O O O O I O O O O O O O O O O O 193
D1 Chi-Square Analysis of Moisture

Content of Paddy Samples from

Rice Mills 0 O O O O O O O O O 0 O O O 0 O 207
D2 Chi-Square Analysis of Purity of

Paddy Samples and the Probability

Table of Drying Delay . . . . . . . . . . 208
15 Results of Simulation Runs . . . 226
16 Results of Simulation Runs . . . 227
17 Results of Simulation Runs . . . 228
18 Results of Simulation Runs . . . 229
19 Results of Simulation Runs . . . 230
110 Results of Simulation Runs . . . 231
Ill Results of Simulation Runs . . . 232
112 Results of Simulation Runs . . . 233
113 Results of Simulation Runs . . . 234
114 Results of Simulation Runs . . . 235
115 Results of Simulation Runs . . . 236
116 Results of Simulation Runs . . . 237

 

Table

1117
1113
'119
120
121
122
123
I24
125
126
127
128
129
I30
131
I32
133
I34
I35
137
138
I39
140

Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results
Results

Results

of
of
of
of
of
of
of
of
of
of
of
of
of
of
of
of
of
of
of
of
of
of
of

Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation

Simulation

xi

Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs
Runs

Runs

Page
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260

-c
I

1-.

‘0

Tabl e Page

K1. Milling Recovery and Percentages
of Different Sizes of Grain and
By—products of Rough Rice Dried
and Stored with 13.0 Percent
M01 Sture O O O O O O O O O O O O O O O O O 26 4

K2 Milling Recovery and Percentages
of Different Sizes of Grain and
By-products of Rough Rice Dried
and Stored with 13.5 Percent
MOiSture O O O O O O O O O O O O O O O O O 265

K3 Milling Recovery and Percentages
of Different Sizes of Grain and
By-products of Rough Rice Dried
and Stored with 14.0 Percent
Moisture . . . . . . . . . . . . . . . . . 266

K4 Milling Recovery and Percentages
of Different Sizes of Grain and
By-products of Rough Rice Dried
and Stored with 14.5 Percent
“Oisture O O O O O O O O O O O O O O O O O 267

K5 Milling Recovery and Percentages
of Different Sizes of Grain and
By-products of Rough Rice Dried
and Stored with 15.0 Percent
Moisture . . . . . . . . . . . . . . . . . 268

K6 Milling Recovery and Percentages
of Different Sizes of Grain and
By-products of Rough Rice Dried
and Stored with 15.5 Percent
MOiSture O O O I O O O O O O O O O I O O O 269

K7 Milling Recovery and Percentages
of Different Sizes of Grain and
By-products of Rough Rice Dried
and Stored with 16.0 Percent
' M01 Sture O O O O O 0 O O O O O O O O O I O 270

K8 Milling Recovery and Percentages
of Different Sizes of Grain and
By-products of Rough Rice Dried
and Stored with 16.5 Percent
Moisture . . . . . . . . . . . . . . . . . 271

xii

Tabl e Page

01 Normal Values of Los Banos
weather Data 0 O O O O O O O 0 O O O O O 0 0 296

P1 Capital Investment for Rubber
R011 M1118, 1976-77 0 o o o o o o o o o o o 297

P2 ‘Variable Costs per Month for
RUbber R011 M1118, 1976-77 0 o o o o o o o o 298

P3 Capital Investment for
Conventional Mills, 1976-77 . . . . . . . . 297

P4 ‘Variable Costs for Conventional
M1118, 1976-77 0 o o o o o o o o o o o o o o 298

P5 Revenue and Profit per ton for
Alternative Milling Systems,
Bicol River Basis Area, 1976-77 . . . . . . 299

01 Rice Huller Rubber Rolls
as Of may 1' 1978 O O O O O O O O O O O O I 300

02 Specifications and Prices of
Rubber Rolls (1976) . . . . . .'. . . . . . 301

R1 Palay (Rough Rice) Planting Area
in 1977 Compared with that in 1976
by Region, Philippines . . . . . . . . . . . 302

81 Statistics on the Rice Industry
in the Philippines 0 O O O O O O O O O O O O 303

xiii

Figure
2.1
2.2

2.5

3.1

3.3

3.8

LIST OF FIGURES

Anatomy of Paddy . . . . . . . .

Schematic Diagram of a Disc-Cone
(Conventional) Rice Mill . . . .

Rice Processing . . . . . . . . .

Average Deviation from Maximum
Yield with Changes in Humidity .

Effect on Temperature on Zenith
and Rexark Rice . . . . . . . .

Causal Diagram of Rice Mill
system 0 O O O O O O O O O O 0 0

Effect of Time of Harvest on
Percentages of Total Milled
Rice and Head Rice in 1R8,

IRS, C4-63 and Sigadis . . . .

Relationship Between Days after
Heading and Head Rice
Percentage O O O O O O O O O O

Drying Delay and Head Rice
Relationship . . . . . . . . . .

Drying Delay and Discolored
Kernels Relationship . . . . . .

Milling Yield Curve . . . . . . .

Rough Rice at Different Moisture
Contents was Collected during a
Drying Run using a Flat Bed
Mechanical Drier . . . . . . . .

Moisture Contents of Samples were

Measured using a Capacitance
Moisture Meter . . . . . . . . .

xiv

Page
18

19
23

28

29

42

46

48

51

52
55

57

58

Figure
13.9

3.13

3.14

3.15

3.16
3.17
3.18

3.19
3.20

3.21

3.22

3.23
3.24

The Rough Rice Grain Sample

was Dehulled by Hand . .

Pressure was Applied on the
Grain until it Cracked and the

Force and Pressure Recorded

Grain Moisture Content vs
Rigidity O O O O O O 0

An Empirical Characteristic

Curve for Rice Whitening
Machine . . . . . . . .

Grain Shape Ratio and
Head Rice Relationship .

Measurement of Voltage and
Current of Motors During
Milling Run . . . . . .

Motor Nameplate data were Noted
as to the Power Factor and

Efficiency Ratings . .
Double Sieve Precleaner .
Single Drum Precleaner .

Precleaner Capacity and
Power Requirement . . .

Disc Huller O .0 O O O O 0

Schematic Diagram of a
Rubber Roll Huller . . .

Huller Capacity vs Hulling
Time 0 O O O O 0 O O O O

Huller Capacity and Power
Requirement Relationship

V-type Sifter o o o o 0

Rice Sieve Capacity and
Power Requirement . . .

XV

59

60

61

62

65

68

69
71
73

74
75

76
78

79
80

81

Figure

3.25 Double Action Husk Aspirator . . . . . . . 83

 

3.26 Husk Aspirator and
Aspirator with Stoner
Power Requirement . . . . . . . . . . . . 84

3.27 Grain Flow through the
Paddy Separator . . . . . . . . . . . . . 86

3.28 Components of a Paddy
separator O O O O O O O O O O O O O O O O 87

3.29 Paddy Separator Capacity vs
Hulling Time 0 O O O O O O O O O O O O 0 O 88

3.30 Paddy Separator Energy

Requirement vs Capacity . 89

3 .31 Whitening cone 0 C O O O O O O O O O O O I 91

3.32 Cross-section of Abrasive
Whitener O O O O O O O O O O O O O O O O O 92

3.33 Section of a Friction Roller
Type Whitening Machine . . . . . . . . . . 93

3.34 Cone Whitener Capacity vs
Whitening Time 0 O O O O O O O O O O O O O 95

3.35 Whitener Capacity and Power
Ranired O O O O O O O O O O O O O O I O O 96

3 .36 Grading Of White Rice 0 O O O O O O O O O O 97

3.37 Rice Grader Capacity and Power .
Required 0 O O O O O O O O 0 O O O O O O 99

3.38 Probability Distribution for
Moisture Content of Paddy
Delivered for Rice Milling
in the Bicol Region . . . . . . . . . . . 100

3.39 Probability Distribution for
Purity of Paddy Delivered
for Rice Milling in the
BiCOI Region 0 O O O O O O O O O O O O O 100

xvi

Figure
3.40

3.41

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

4.12

4.13

Probability and Cumulative
Distribution of Drying Delay
for Paddy Delivered for Rice
Milling in the Bicol Region .

Computer Flow Chart of
Program Rice Mill . . . . . .

Example Circuit and Its
Linear Network . . . . . . . .

Modified Circuit and
Linear Network . . . . . . . .

A Typical Industrial
Process 0 O O O O O O O O O 0

Diagram of Precleaner Component
Process 0 O O O O O O O O O 0

Diagram of Huller Component
Process 0 O O O O O O O O O 0

Diagram of Sifter Component
Process 0 O O O O O O O O O 0

Diagram of Paddy Separator
Component Process . . . . . .

Diagram of First Stage Whitener
Component Process . . . . . .

Diagram of Second Stage Whitener

Component Process . . . . . .

Diagram of Third Stage Whitener
Component Process . . . . . .

Diagram of Grader Component
Process 0 O I O O O O O I O 0

Diagram of Return Huller
Component Process . . . . .

Schematic Diagram of a Disc-Cone

(Conventional) Rice Mill . . .

xvii

102

103

111

111

115

120

123

123

125

125

126

126

128

128

129

Figure
4.14

L1

L2

Linear Graph of a Rice Milling

System .

Different Simulation Runs on
Commercial and Preposed Mills

Comparison of Recommended
Energy Requirement and the
Simulated Values

Time Study of a 2.5 ton-per-hour
Rice Mill in the Philippines

Time Study of a 6.25 ton-per-hour

Rice Mill .

xviii

Page

131

167

170

273

274

CHAPTER I
INTRODUCTION

The introduction of high yielding varieties (HYV‘s)
of rice into Southeast Asia in the late 1960's created the
potential for the Philippines to become self-sufficient for
this vital crop and even to become a substantial exporter.
‘However, "second generation! problems - those associated
with storage, processing and marketing - confounded this
.potential.

‘When HYV‘s were first planted in 1967-68 on twenty-
two percent of the lowland rice area, an eleven percent
increase in total yield resulted (Mears, 1974). The peak
rate of growth was attained in 1969-70 with annual
increased yield of seventeen percent. Although the
following year attention was prematurely turned to other
crops and a rice crop failure occurred, the increased
production experienced the previous years was sufficient to
demonstrate the inadequacies of the traditional post-
harvest operations. Indeed, post-harvest losses were as

high as thirty-six percent (Araullo, et al., 1976).

 

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2
The National Grains Authority (NGA) initiated an

extensive effort to ameliorate the situation utilizing the
facilities of the International Rice Research Institute
(IRRI), the University of the Philippines at Los Banos
(UPLB) and related government agencies.

Substantial progress was made in selected phases of
processing and storage through the introduction of locally
produced farm dryers and locally made and imported drying
plants. Renovation of USAID-purchased grain elevator and
storage facilities was also undertaken.

Rice milling, however, experienced the least
technological improvement in the rice marketing system,
although losses using the traditional mills had been among
the most substantial, from two to ten percent of the grain
processing operation (Timmer, 1974).

Efforts to improve mills were limited both by a
'lack of research into the problems involved and the

comparably high cost of changes in these operations.

1.1 chmfitndx

Decisions regarding rice mill construction and
alteration involve a number of variables and trade-offs
reflecting the costs of construction, the costs of
operation and the quality and quantity of the output. When

the sc0pe of the "second generation" problems became

3

apparent in the early 1970's not enough information
regarding rice milling operations in the Philippines was
available to make informed decisions. There followed four
significant studies. Three of these (Andales, et al.,
1976; Camacho, et al., 1977; and Sison, et al., 1976) were
made on commercial milling equipment processes under
typical conditions, and the fourth was based on
measurements made under controlled laboratory conditions
(Manalabe et al., 1978). ‘While these studies provided
valuable theoretical and actual data about milling
operations, they failed to provide a sufficiently complete
picture for decision making because they were limited by
the number, sizes and types of mills available in the field
for monitoring. Some important variables such as energy
consumption.and time delays in milling were also
unavailable.

With the field measurements of these missing
variables made by the author in this study it was possible
to construct a systems model validated by actual experience
in the mills. This model provides a tool for evaluating at
greatly reduced cost and in minimal time the effects of
altering the variables involved (Seshuy et al., 1959),
such as: the use of single versus double hullers, one-
stage versus two-stage whitening, and energy use versus

quality of output.

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The Camacho, et al. study was a joint effort by the

Agricultural Engineering Departments of the International
Rice Research Institute and the University of the
Philippines at Los Banos. This was conducted in the Bicol
River Basin which is under development by the Bicol River
Basin Development Program with funds from a USAID grant
(Bicol Project Agreement No. 75-09, Sub-agreement No. 15).
About 334,410 hectaresl/ in the Bicol Region was planted to
rice in 1977. This represented about ten percent of the
area planted to rice in the Philippines. This area is
typical of about forty percent of the rice producing areas
in this country, particularly the eastern seaboard areas of
Cagayan Valley, Southern Tagalog and Eastern Visayas.
These areas are subjected to the seasonal typhoons
prevalent in the South Pacific Islands during the month of
September. This particular characteristic makes it an area
where special attention must be provided to minimize damage
to crops during harvesting and processing. Table 1.1
summarizes data from the eleven representative rice mills
monitored during the study period of 1976-77.

Small rice mills of the kind analyzed in this study

processed almost all of the rice in the Philippines in

 

 

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6
1984. Less than one percent of the rice was processed by
the three large-capacity (6 to 25 tons per hour) rice mills
in the country.

The two most important types of small mills, which
accounted for ninety-five percent of the milling
operations, were equipped with steel hullers (forty
percent) and the conventional disc-cone (fifty-five
percent).l/ The miscellaneous types of mills which
processed the remaining approximately five percent of the
rice included a newly introduced rice mill with rubber
rolls. This is a portable unit and favored by some as a
replacement for the steel hullers. However, the rubber
rollers are expensive and require replacement after every
15 tons of paddy (see Appendix Table 02). Another portable
mill is the centrifugal huller with a performance very
similar to that of the steel huller mills.

The NGA estimated that in 1979 the steel huller
mills numbered approximately eighteen thousand, eight
thousand of which were unregistered. These mills were one-
quarter to one—half ton per hour capacity. The conven-
tional disc-cone mills were unofficially estimated by the

NGA.to number approximately three thousand, indicating

 

”See Appendix Table S for a survey of the number of steel
hullers and conventional disc-cone mills in use in 1973.

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7
increased popularity of steel huller mills compared with

the 1973 survey estimate.

1.2 mm:
The specific objects of this study are to:

1. Develop a rice mill performance model that will
realistically simulate selected milling technologies and
provide a basis for the evaluation of performance and
energy consumption over a wide range of input and output
capacities.

2. Develop a linear network economic model of rice
milling systems for evaluating milling costs over a range
of capacities within a selected set of technologies.

3. Conduct simulation runs to show how information
may be generated to help in the decision- and policy-making
processes of planning, design and operation of milling

facilities.

1.3 Win at the Male
1.3.1. Rice Mill Performance

All of the available information on rice mill
performance was from rice mill tests. These data were
limited by the high cost of monitoring mills and by the
unavailability of the different types of mills in the

field. The capacities of Philippine manufactured mills

 

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range from 0.3 to 2.0 tons per hour. The two main
technologies were from Europe (i.e., Germany and Italy) and
Japan. Philippine mills which were based on German design
were known as conventional disc-cone mills while the
Japanese mills were known as rubber roll mills. In order
to evaluate the mills on the basis of their technical and
economic performance, it was decided to develop»two models
that would.evaluate commercial rice milling systems over a
range of capacities for a selected set of technologies.

Data that were readily available from other
researchers included input paddy factors such as: maturity
or moisture content at harvest and at milling, drying delay
and purity of grain. Milling factors included machinery
adjustments and ambient temperature and relative humidity.
Data on grain rigidity or grain pressure during milling,
grain shape, grain moisture content and purity were
available but were not readily usable due to the lack of
analyses of their relationship to rice milling.
Unavailable information which was measured by this author
was on energy consumption of individual rice mill component
machinery, and time measurements of the different delays in
the rice milling operation.

Modeling provides a convenient and low-cost means
of evaluation. The first consideration in modeling is to

choose between a dynamic and a static model. Static

in,
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models are incapable of providing informatiOn about the
future consequences of current decisions. They are
constructed with equations which do not contain past values
or rates of change of system variables. A dynamic model is
useful for analyzing the operation of an individual system
for peak labor usage and the effect of a disturbance, such
as bad weather, on the system (Tummala, et.a1., 1973).

This study, however, was designed for comparison of a
.nmmber of systems. Static models proved simpler and more
suitable for the desired comparisons.

The development of the computer model involved the
understanding of the rice grain, the rice milling process
and gathering of data related to the model. Rice grain
processing is uniquely different from the processing of
other grains such as wheat, rye, barley and corn. Rice is
marketed in whole-grain form rather than in a processed
powdered form; thus, the processing stage is more critical
than for other grains. However, all grains are living and
respiring biological products and as such are affected by
whatever previous treatment they experienced in the
growing, field handling, drying and storage stages. The
variables that affect the rice grain quality and milling
yield were incorporated in the computer model. The

physical properties of the grain such as grain shape,

10

hardness and length are another set of variables that were
analyzed and included in the model.

The rice milling process was also studied. The
stages in the process were observed and measurements made
on bin capacities, time delays and energy consumption. To
collect data on energy consumption and time delays during
rice mill operation, three mills of sizes 1.0, 2.5 and 6.25
tons per hour capacity were monitored during operation.
One observer was posted at each machine component and the
rice mill was started at a predetermined time. Time of
entry and exit of paddy or rice, as the case may be, was
noted. Figures L1 and L2 in Appendix L show the results of
time measurements. 'Voltages, amperages, power factors and
efficiencies were noted at each electric motor at three
different times during the milling run (Figures 3.14 and
3.15). The average of the three readings for each machine
component is shown in Appendix Table A1. Bin capacities
were obtained from manufacturers' plans. Material
reduction data were derived from the work of Manalabe, et
aL" (1978), and machine capacities were from Jose Bernabe
and Co., Inc. These measurements were expressed in terms
of equations and graphs which are discussed in more detail
in Sections 3.2 through 3.5.

The computer model was patterned after the

different steps in the rice milling process. Time delays

11

in the process were due to the operator's practice of
waiting until the bins were half full before opening the
feed shutters of the machine. The measurements of the
delays were obtained by dividing the bin capacities by the
handling or processing rate of the machine immediately
preceding the bin. Processing time of each machine was
obtained from a graph or from the input grain mass divided
by the machine handling rate. The power requirement which
was obtained from a graph was then multiplied by the
processing time to obtain the energy consumption. The
grain quality was expressed in terms of the mass percentage
of the whole grains and of discolored grains over the total
milled rice. These two quantities were obtained from a
quality factor which is the product of four variables
obtained from the graph of grain factors affecting quality.
This process is explained in more detail in Chapter Three.

The efficiency of the rice mill was measured by the
milling recovery which was defined as the mass percentage
of the milled rice over the pre-cleaned input paddy. The
amount of milled rice was obtained by the use of reduction
factors at each milling stage.

Input variables were of two types: first, the rice
itself, and second, the mill studied.

The first category included such factors as grain‘

shape (the length-width ratio), growing season (wet or dry)

12

and drying method (solar or mechanical) which were
determined by observation. Other grain factors such as
harvesting delay, moisture content at milling and grain
purity were generated internally by a random number
generator employing normal distribution functions. Drying
delay, by which is meant the number of days delay between
harvesting the rice and the beginning of the drying
process, was derived by employing a third order gamma
distribution function. The second set of variables
required were: the capacities of the different bins in
cubic meters; the number and capacity of the precleaner;
type, number and capacity of the whitener; type, number and
capacity of hullers; type and capacity of separators and
the mass of paddy processed.

The output provided the milling time in hours,
output in hours, total recovery, head grain recovery and
percent discolored kernels as well as the total energy

consumed in kilowatt-hours.

1.3.2 Linear Network Economic Model

The system's cost of operation was evaluated over a
range of capacities using a modeling technique in which the
system was analyzed as a set of components which were
described in terms of their mass and energy character-

istics. The components of the rice mill were classified

 

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into those that perform material transformations and those
involving transportation. A material transformation
involved conversion of the input material into an output
product while transportation components included transport
energy cost.

The model is in the form of an equation for the
cost of output milled rice in terms of input variables and
technical coefficients. The technical coefficients are
dimensionless numbers that express the ratio of output over
input or vice-versa for a particular process. A more

involved discussion of the method is in Chapter Four.

CHAPTER II

REVIEW OF LITERATURE

In order to carry out the objectives of this
research it was necessary to develop an understanding of
the complete rice milling process and the previous work

done by other researchers.

2.1 Nomenclature

The following terms are commonly encountered in
rice milling literature. Terminologies sometimes vary
depending on milling practices. Milling in Japan, for
example, does not include the husking process, and their
definition of recovery or yield is based on brown rice
rather than paddy or rough rice. The definitions found
here are those used in the Philippines.

a. .Bgngh Rice (Paddy) is unhulled grain.

b. .3123 Milling includes the process of removing

the husk from the rough.(whole) rice kernel
and bran (pericarp, testa and aleurone layers)

from the brown rice kernel.

14

Co

15
Buaking er Hulling is the operation of

removing the husks from rough rice.

Brena Riee consists of rice after the husks
have been removed and separated from the whole
kernels.

Whitening is the process of removing the bran
layer from the brown rice.

,Milled.Biee consists of the resulting white
rice after the bran layer has been removed
from the brown rice.

.Milling Degree refers to the extent the bran
layer has been removed and is expressed in
percentage of the original rough rice.
Reliehing er Refining is the process of
removing the powdered bran adhering to the
milled rice after the whitening process.
Breken.fiiee includes kernels broken into
pieces that range in size from 1/4 to 3/4 of
whole grain.

.BrexerLe.Riee er Relate include broken pieces
after milling that will pass through a 1/16
inch sieve.

Heed Biee includes kernels that are from 3/4

to whole kernel size.

foreign Matter or leakage pertains to

O.

16

impurities in rice such as: weed seeds,
stones, sand and dirt.

Chelky Kernels include milled rice kernels
which are at least half non-translucent. It
may be caused by the harvest of immature rice
or also may be genetically related.

Damaged Kennels includes milled rice kernels
damaged by insects or mechanical means.
.Dieeelered.xernele includes yellowish milled
rice kernels damaged by fermentation or heat.
metal Milling Reeeyery consists of the weight
of milled rice from the milling operation
expressed as a percentage of the original
rough rice (clean and dry) weight.

Heed Biee.3eeeyery consists of the weight of
head rice obtained from the milling operation
expressed as a percentage of the total milled
rice weight.

geefifieien; efi Hulling is the preportion of
brown rice by mass produced by a huller as
percentage of the total amount of paddy fed
into the huller.

geefifiieientyef Hheleneee is the proportion of

whole brown rice by mass as a percentage of

17
total amount of brown rice produced by a
huller. I
t. Hullingyfiffieieney is the product of the
coefficient of hulling and coefficient of

‘wholeness of grains.

2.2 Anatomy cf the Rice Grain and Its Significance
The anatomy of a rough rice kernel is shown in
Figure 2.1 and a rice mill diagram in Figure 2.2. The

following description is from Araullo, et al. (1976):

The outermost tissue of the grain is commonly known as
the husk and is formed from two specialized leaves,
the lemma covering the dorsal part of the seed and the
palea covering the ventral portion.

The palea and lemma are very loosely joined together
longitudinally by means of an interlocking fold on
each side of the seed and they are consequently very
easily separated. The husk is formed mostly of
cellulosic and fibrous tissue and is covered with very
hard glass like spines. In the dry seed there is a
distinct space between the husk and the cryopsis
(kernel).

The outermost layer of tissue of the cryopsis is a
thin sheet of fibrous cells, the pericarp. This is
sometimes called the silver skin because of its very
flimsy and fibrous appearance when it is dissected
from the grain. The thin pericarp layer is a very
hard tissue that is highly impermeable to the movement
of oxygen, carbon dioxide and water vapour. When it
is intact it provides very good protection against
mold attack and oxidative and enzymatic deterioration
of the underlying tissues. Beneath the pericarp is
the tegmen, which is a layer several cells in
thickness. These cells are also a part of the seed
coat but are less fibrous than the pericarp layer.
They are rich in oil and protein but contain little
starch. Beneath the tegmen is a layer of tissue
several cells in thickness commonly known as the

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aleurone layer or bran. This layer of cells is very
rich in protein, oil and vitamins with a relatively
small amount of starch. The shape of the cells is
somewhat hexagonal to spherical. The innermost tissue
of the caryopsis is the starchy endosperm which
contains only a very small percentage of protein. In
the central core of the grain, the starchy cells are
somewhat hexagonal in shape, but between the center
and the outside they are elongated with the long walls
radiating outwards from the center. It is the radial
walls of the starchy cells that form thousands of
potential cleavage planes that may result in breakage
of the grain as a result of mechanical impact or
thermal or moisture stress during processing.

In cross-section the total caryopsis has an undulating
shape, the undulations corresponding with those of the
husk. The undulating form is important in the
whitening process because the bran layer can only be
removed entirely when the undulations have been
completely levelled.

The paddy grain husk, which is very fibrous and
covered with spines, is very abrasive and easily
causes wear and tear in mild steel conveying ducts.
For this reason, bucket eleators and belt conveyors
are preferable.

The distinct space between the husk and the caryopsis
in dried grain allows the grain to be dehusked without
any or with very little abrasion to the pericarp.
Provided the clearance between the shearing surfaces
of the hulling machinery is precisely adjusted, the
palea and the lemma can be separated from the
caryopsis with only a minimal mechanical stress
against the surface of the caryopsis. Dehusking
between rubber rollers can generally be done without
scratching the pericarp. On the other hand, with the
emery disc type of hulling, even if very well
adjusted, it is difficult to avoid at least some
abrasion of the pericarp. When such abrasion does
occur, it usually results in damage to the oily layers
beneath the pericarp that release lipase enzyme during
storage, which results in enzymatic hydrolysis of the
oil to produce free fatty acids, that are commonly
manifested as rancidity. If the grain is to be stored
in the form of brown rice, it is extremely important
that the pericarp of the caryopsis is not damaged
during the dehusking process. In contrast to this
limitation, there are a number of advantages to be

 

21

gained from storing the grain as brown rice;

principally, the fact that brown rice occupies about

half the volume of an equivalent quantity of paddy.

It may also be useful when the grain is stored as

brown rice in large milling centrals because they are

equipped to dispose of the large quantities of husk
created. Large centrals are equipped with husk
grinders and have outlets for selling husks as filler
feed for swine and cattle.

The radial configuration of the outer cells of the
endosperm allows easy breakage of the grain kernels. It
has been observed by researchers at IRRI in 19591/that
breakage during threshing by manual beating of the grain,
even at a moisture content of about 20%, is much greater
than in certain types of mechanical threshing where the
force is applied to the ends of the grain like in spike-
tooth threshers. This indicates that breakage is greater
when impact is exerted along the side of the grain rather
than at the end. The alignment of the longitudinal walls
of the starch cells (see Figure 2.1) across the body of the
grain makes it very friable during the drying process and
when the temperature of the grain reaches 48°C: thermal
stress checks may be the cause of grain breakage during
conveying and milling.

It is very important to avoid breakage of the rice

because in the whitening stage of the milling process, the

surface area of the broken grain exposed to abrasion is

l/See the report of Khan (1969)

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much greater than whole grain and not only bran is lost
but starch as well. This results in a low total recovery

and low recovery of whole grain.

2.3 Rice Milling Brcccsa
Rice transformation through several stages of the
milling operation are here defined. The milling process is
generally carried out in several distinct stages with
individual and separate machines doing specific operations.
The transformation stages as shown in Figure 2.3 are as
follows:
a. Precleaning to remove foreign matter with the
use of sieves and air blasts.
b. Detachment of hulls from kernels with
abrasive discs (or rubber rollers).
c. Separation of small brokens and coarse bran
(pericarp or silver skin) by screening.
d. Separation of unhulled rice from hulled
(brown) rice by paddy separator.
e. Whitening of rice by removing bran with
abrasive stones (and/or a steel grinder).
f. Refining or polishing rice with leather
strips or brush.
9. Grading of rice with sieves (and/or indented

cylinders).

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2.4 .Innnt.2addx.zactcra

Nangju and De Datta (1970) found that irrespective of
variety or nitrogen level, the optimum time of harvest of
transplanted rice for obtaining maximum grain and head rice
yields and highest germination percentage was between 28
and 34 days after initial heading in the dry season and
between 32 and 38 days, in the wet season. These periods
correspond to moisture contents of between 22 and 19
percent and between 21 and 18 percent, respectively.

Rapusas, et a1. (1978) reported that in the dry
season, in Los Banos, Philippinesl/ the non-aerated paddy
held in sacks before drying maintained its grade quality up
to 3 days. In the wet season experiment, the non-aerated
paddy held in sacks showed a loss in grade with a one day
drying delay.

Ongkingco, et al. (1964) found that the highest
:milling recovery was obtained from grain dried to a
moisture content ranging from 13.0 to 14.5 percent wet
basis, with milling yields ranging from 68.2 to 69.9
percent and the highest percentages of whole grain (head

rice) ranging from 58.3 to 58.8 percent. Much lower

l/The dry and wet season average temperature and
relative humidity in Los Banos are 26.8°C and 80.0 percent
and 26.l°C and 85.4 percent, respectively.

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recoveries were obtained from grain dried to a moisture
content of 15.0 to 16.5 percent. Lower percentages of
*whole grains were also obtained, which may have been due to
damage caused by overheating caused by respiratory heat

during storage.

2.5 Milling Easter:

Manalabe, et al. (1978) reported that the quantity as
well as the quality of brown rice recovered from single
pass hulling varies significantly with the huller
clearance. The indica rice variety has an average brown
rice kernel length, width and thickness of 7.13 mnu, 2.18
mm. and 1.66 mnn, respectively. At 14 percent moisture
content wet basis, the clearance adjustment for optimum
hulling efficiency should range from 3.0 mm. to 4.0 mm. for
the stone disc huller and from 0.75 to 1.0 mm. for the
rubber roll huller. Hulling efficiency on these settings
ranges from 55.1 to 57.8 percent for the disc huller and
82.1 to 83.3 percent for the rubber roll huller. Between
the two huller types, the rubber roll huller had a higher
total outturn of brown rice from paddy of 75.2 as compared
to 72.8 percent for the stone disc huller. There was
partial scouring of brown rice and brokens are blown away

with the husks in the stone disc huller.

 

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Manalabe, et a1. (1978) concluded that the milling

system used did not have a significant effect on the
recovery. The recovery percentage for the milling systems
using a stone disc huller ranged from 63.2 to 64.87
percent, while that for the milling systems using a rubber
roll huller was higher, though not significantly so,
ranging from 66.5 to 67.7 percent.

Researchers at the University of Arkansasl/ found
that the relative humidity of the mill room affects the
yield of head rice. Optimum yields were obtained when the
relative humidity was between 70 and 80 percent. The mill
room temperature did not affect the yields of head rice
when the temperature of the rough rice entering the mill
was approximately the same as that of the mill room air.
The mill room relative humidity ranged between 70 and 80

percent.

1L6 .Amhlan.Bcchi e Humidifx.and.Temperatnre

Rice milling researchers at the University of
Arkansas presented results of the effects of mill room
humidity on milling efficiency as well as in some portion

of the milling process. The effect of rice temperature

l/See undated Reference entitled Rice Milling Research

TerminalRechfcftheInscifgfecfScienceandTechnglegy,

University of Arkansas, Fayetteville, Arkansas.

 

27

relative to room temperature on milling efficiency was also

studied. The relationship of air humidity and rice
moisture content has been discussed in several published
reports (Hall, (LW., 1963) concerned with hygroscopic
equilibrium. Rice with a moisture content of 12.5 to 14
percent is in equilibrium with the atmosphere at
approximately 70 percent relative humidity, which would
indicate that rice milled under such conditions would
neither gain nor lose moisture.

Rough rice samples of approximately 1,200 lb each
were collected from representative lots from Southern
Arkansas commercial mills for each series of test runs.

The 1,200 lb samples were divided into six 200 lb mill
samples and stored in airtight containers for three weeks
preparatory to milling. The mill room temperature was held
constant at 80°F plus or minus 2°F during all runs. An
attempt was made to mill all runs at relative humidities of
30, 50, 60, 80 or 90 percent with a duplicate run at each
of these humidities. Determination of moisture was made
and the fat content of bran layers was measured in order to
determine whether bran was being removed to a uniform
degree in a specific series of runs. Figures 2.4 and 2&5
show, respectively, the average deviation from maximum
yield with changes in humidity and the effect of

temperature on Zenith_and Rexark Rice.

28

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Table 01 in Appendix 0 shows the normal weather

data for Southern Luzon (Philippines) as recorded by the
College Weather Station. The relative humidity varies from
78 to 86 percent with a mean of 83 percent. This
corresponds to a hygroscopic equilibrium of 15 to 16
percent. The variation is negligible on a day-to-day
basis but it is pronounced on a seasonal basis. Figure

2.4 shows that the head rice was reduced under this
condition from 0 to 1.2 percent depending on the actual
relative humidity and rice variety. It was found that the
conditioning of first and second stage hullers was not of
prime importance. It is apparent from the data that the
operations in which the rice comes into contact with large
volumes of air, such as aspirators and elevators, are the
critical points where humidity affects yield of head rice.
Due to lack of data on the effect of humidity on Philippine
grown varieties, this variable was not considered in the

modeling.

2.7 Eccncmic Eastern

Shelby, et al. (1974) modeled three rice mills with
capacities of 11.0, 22.0 and 36.8 tons per hour. Capital
requirements and Operating costs were analyzed to develop
average cost curves. The importance of maintaining high

levels of mill utilization is shown by the fact that costs

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31

decreased 30 to 40 percent when the mill operating time was
increased from a 5-day, 40-hour week to a 7-day, l68-hour
week.

The ll-ton-per-hour mill had the highest operating
cost at any given level of time utilization. However, this
relative cost disadvantage narrowed as the operating time
increased. Bagtas and Lizardo (1970) reported that the
benefits to the investor from rubber roll rice mills are
insufficient to make them attractiven Such studies
indicate the need to increase the hours of daily operation
of the mills approximately fifty percent to cape with the
increased harvests derived from HYV‘s and new farming

technology.

2.8 Saracens Technigne used in the Modeling

Manetsch and Park (1977) identified three major
ways in which models can go wrong. The first is at the
'problem definition! level where a model must obviously
address the correct set of problems. It must adopt the
right variables as policy inputs and include the correct
variables for enabling decision-makers to evaluate
alternative possibilities. Secondly, a “good" model must
in its mathematical structure produce close approximation
of actual experience. This requirement introduces such

complexity as to require that the system be expressed in

32

terms of a computer program in order to be efficiently
handled. This means that it is necessary to produce a
mathematical model that approximates the real world.
Finally, a 'good"model must incorporate a computer model
that closely approximates the mathematical model.

Models can be classified according to the view
they take of the real world: microscopic or macroscopic.
Microscopic models take a very detailed view of reality and
represent, explicitly, individual entities moving through,
or being processed by, the system. For example, a detailed
model of the operation of a grain storage system would
represent each individual shipment of grain as it was
loaded or unloaded at the storage facility. A macroscopic
(or aggregative) model, on the other hand, deals with
aggregative flows of goods or services; for example,
aggregated birth and death rates in a population or total
production of a commodity in a geographical region. A good
“problem definition" will help decide which type of model
to build. Some problems require a microscopic point of
view; for others, a macroscopic model is clearly more
appropriate.

A second important way to classify models is
whether or not they represent dynamic phenomena in the real
world. A good test to determine whether a given system or

situation is dynamic or not is to pose the question, "Will

33

actions taken today influence the future in some important
ways?“ If the answer to this question is "yes," the system
is dynamic. Static models are incapable of providing
information about the future consequences of current
decisions. Static models can, however, be useful in
addressing decision problems in agricultural development.
For example, a static model may provide information to a
farmer on how many acres of various crops he should produce
this year, given particular assumptions about prices and
yields per acre.

A third important way of classifying models is
according to whether they are deterministic or stochastic
(random). A stochastic model contains random elements
‘which will simulate outcomes of an uncertain nature, while
deterministic models do not. Deterministic models are
appropriate when the effect of stochastic elements are
small or negligible.

Stochastic models approximate the impacts of random
factors and provide deCision-makers with some idea of the
range of outcomes that are possible from a particular
decision, given the random factors that are present in the
given situation. In order to do this, models are operated
repetitively in a so-called "Monte Carlo" mode. In each
I'Monte Carlo" run of the model, the random factors involved

are allowed to take on a different set of values that are

 

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34

consistent with the randomness inherent in the real world.
The average value obtained from the "Monte Carlo"
simulation is considered the most likely value to be
encountered in real life situations.

With the broad outlines of the system model
established as a result of sound problem definition and
given selection of the most appropriate model type, two
major approaches to model-building can be employed singly
or in combination. These are the so-called "black box" and
"structural" approaches. Essentially the "black box"
approach seeks to identify a system model from data
describing the past behavior of the real-world system.
Through various statistical and mathematical techniques, a
model is derived which in some sense is a best fit to the
historical data.

The "structural” approach to building a model
attempts to represent or simulate the detailed system
structure that causes the total system to behave as it does.
This approach decomposes a system into its component parts,
builds mathematical models that approximate the behavior of
those component parts, and then interconnects the component
models to obtain a model of the overall system. Many, if
not most, large-scale decision models are developed using
this approach aided by the "black box" approach to fill

certain parts of the structure.

35

In system simulation there are significant
decisions to be made in the choice of computational
techniques used in the computer model. Proper choice here
can lead to substantial savings in model development time
and cost.

In almost every model simulation there is a need to
represent the relationship between two variables or
quantities in language a digital computer can understand.
Avery common and efficient means of doing this is the so-
called "straight-line approximation" method. In some cases
the functional relationship between two variables can be
implemented with functions built into a programming
language such as FORTRAN (examples are logarithmic,
exponential and trigonometric functionsL. Programming
using ”built-in" functions is easier, but they almost
always use more computer time than the straight-line
approximation method. Another method of function
representation, polynomial approximation, can be extended
to functions of more than one variable but is less common
than the two methods cited.

Differential equations are solved on a digital
computer by the process of numerical integration; and there
are several ways to do this. The simplest and most common
numerical integration technique in agricultural models is

the so-called "Euler" integration.

36
Holtman, et al. (1972) discussed that the primary

function of systems analysis is the determination of
performance characteristics of a system of interaction
components operating in a specified environment. The
engineer's basic interest is to study system performance
utilizing various component configurations. In some
situations there may exist a fixed set of components from
which those to be utilized are selected. Alternatively,
the primary emphasis may be the design of a particular
component which is suitable for use in.the system. In
either case, the systems methodology will be enhanced if
component manipulation capability exists.

A second feature of engineering models is the
description of mass and energy flows among components. The
system is viewed as a collection of components which
interact with each other and their environment via mass and
energy exchanges. The law of continuity is involved at
every exchange point.

Finally, a level of energy is associated with each
material. The energy level is an intensive attribute which
reflects the energy required (per unit of material) to
place this material in its current form and location.

Koenig and Tummala (1972) discussed the components
of an ecosystem into three general classes: material

transformation processes, material transport processes and

37

material storage processes. All production processes in
industry and agriculture can be defined as transformation of
materials to achieve a well defined change in their
physical, chemical, technological or biological structure,
through the application of energy in one or more of the
three forms: solar, human and/or physical. The
transformation processes are organized in technical units
called plants, a plant being a technically coordinated
aggregate of fixed capital under common management. The
term "process“ is used in a semi-abstract sense as distinct
from the total productive activity of the plant. It
specifically excludes the technological treatment to which
materials are subjected to bring them further toward their
completed state as products useful to man.

Transport processes in the ecosystem can be viewed
as a special class of material transformation processes
wherein the process simply moves the material form from one
geographic location to another at a cost.

Storage processes can also be viewed as a special
class of material transformation processes wherein the
input and output materials are identical in fornn The
accumulation of material and energy (or monetary value of
inventory) depend upon the flow rates ingoing and outgoing

in the storage process.

 

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A model of the physical and economic character-
istics of any given or proposed system is obtained by
constraining the freeébody models of the system components
according to the laws of material and energy balance
imposed.by the interconnections between the material
transformation, transportation and storage processes and
the components of the natural environment.

Various literature on grain and food handling were
reviewed to find out if there was previous work on modeling
grain processing facilities. This author did not come
across any technical model of grain-handling facilities
similar to the proposed model. However, an economic model
by Shelby, et a1. (1974) was discussed earlier in Section

2.7.

2.9 Snmmarxchexienchitcratcre

From the preceding review of literature sufficient
information on rice grain parameters was obtained to build
a rice mill systems model. Important grain parameters
include input paddy factors such as: moisture content at
milling, maturity, purity and drying delay. Environmental
input variables such as relative humidity and ambient
temperature are fairly constant on a dayjto-day basis; and

were assumed constant in this modeling.

39

The parameters which are controllable in rice mill
design include type and size of component for each process
stage. Additional data were needed on the rice mill
parameters to successfully simulate a rice mill operation
including consumption and time analysis of various
components. .A more detailed discussion of this is
presented in Sections 3.3, 3.4 and 3.5. Other parameters
such as material reduction rate, conversion efficiencies
and machine capacities are available and were discussed in
Section 2.5. Two management input variables are non-
controllable: the operator's skill and dedication and the
condition (state of repair) of the mill, hence these
variables are unpredictable for a simulation model. It was
decided by this author to assume that good maintenance is
performed and that proper training and conditioning of mill
operators takes place (which is highly desirable). The
Philippine government through the National Grains Authority
in connection with the University of the Philippines at Los
Banos has been conducting training on rice milling for rice
millers and operators since 1972. The training consists of
lectures and laboratory work in the Grain Processing
Laboratory in Los Banos and on-site training in NGA leased
commercial mills. The training emphasizes the importance

of well maintained equipment and its prOper adjustment.

CHAPTER III

RICE MILL PERFORMANCE MODEL

This chapter covers the development of the rice

mill performance model and its implementation with a
computer. Based on the specific objectives of this study
as presented in Section 1.2 and the discussion of systems
technique in Section 2.8, it was decided to develOp a
stochastic static model. This model incorporated grain
variables such as: harvesting and drying delay, moisture
content and purity of paddy. It was decided to employ a
microscopic view of the rice mill to study precisely what

occurs as a batch of grain goes through the stages of the

rice milling process. A "structural" approach was utilized

to enable the model user to manipulate components for
purposes of simulating various combinations and capacities

of machinery.

3.1 Medel Definitign

Primary considerations for rice mill output

performance evaluation were the milling yield and rice

40

41

quality. The parameters that affect these variables were
analyzed. A causal diagram was developed and is shown in
Figure 3.1. The system was identified based onpthis
diagram.

Energy consumption, which is another performance

criterion, is discussed separately in Section 3.4.

Desired Outputs are:

a. High milling yield and high proportion of head
grain (unbroken kernels).

b. High quality of grain (no discolored and
checked kernels).

c. Clean grain kernels.

Designer Controllable Input Variables are:

a. Type and size of component for each process
stage.

b. Mill capacity (tons per hour).
c. Optimum combination of locally produced and
imported equipment.
Management Non-controllable Input Variables are:
a. Operator's skill and dedication.

b. Condition (state of repair) of mill.

Non-controllable Environmental Input Variables are:
a. Relative humidity.

b. Ambient Temperature.

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Non-controllable Inputs are:
a. Input grain parameters.

b. Total grain input per batch (tons).

3.2 Srainlariahles

Data on the grain variables that affect milling
quality (maturity, moisture content at harvest and at
milling, drying delay, purity and grain-shape) were
obtained from several studies by other researchers:L/

These studies were done under controlled conditions to
isolate the effect of each variable. The value of each
(loss or yield) variable was expressed as a percentage of
the maximum.va1ue attained in the experiment. These
percentages were then multiplied to obtain an index similar
to Steelefls multiplier (Steel, et al., 1969). These
reflect the extent of damage due to adverse conditions. If
grain was produced and processed under ideal conditions,
its milling yield and quality index would be at a maximum
value for that particular grain-shape. The use of grain
shapeZ/ instead of variety has the advantage of eliminating

model obsolescence as new varieties are developed.

l/See the works of Camacho, et al.; Eriyatno; Nangju and De
Datta; and Rapusas, et a1.

2/See Appendix J, Figure 3.15 and Section 3.3 for more
details.

44

3.2.1. Input Paddy Factors

Grain is a living and respiring biological product
and its treatment prior to millinghas a direct bearing on
the quality of the final product. These include production
treatments such as fertilization, solar radiation and
irrigation; and post-harvest conditioning such as harvest
maturity, drying method, duration of storage and delays in
the various processing operations. It was not possible to
include the effect of all these factors due to non-
availability of data. However, the variables that have the
greatest known effect on the quality of the final rice
product were included. These were maturity at harvest,

drying delay, moisture content at milling and grain-shape.

3.2.1.1 Maturity at Harvest

Nangju and De Datta (1970) reported the effect of
time of harvest on the grain yields of four varieties in
the dry season. ‘When the varieties were first harvested
at 16 days after initial heading (DAH), the kernels had
just turned from the milk stage to the soft dough stage,
the kernels were thus under-developed, mostly green and
partly filled. The mean moisture contents of the mature
grain, unfilled grain and green kernel at 16 DAR (average
of four varieties at three nitrogen levels) were 33, 36 and

89 percent, respectively. As the harvesting was delayed,

45

more of the kernels were fully filled and had turned from
green to yellow. Consequently, the percentage of unfilled
grains and of moisture content of the grain decreased and
the 100 grain weightl/ increased in grain yield during the
early ripening period. The loo-grain weight sample from
filled grains was highest at about 22 DAH. The further
increase of grain yield after 22 DAR apparently resulted
from a decrease in unfilled grains, which was lowest at
about 30 DAH.

For the wet season, the mean moisture content
percentages for the mature, unfilled and green kernels at
16 DAH (average of four varieties and nitrogen levels) were
34, 44 and 89, respectively. As the moisture content of
the grain decreased and the loo-grain weight increased, the
grain yields of the four varieties increased.

Data showing the effect of time of harvest on
percentage of total milled rice and head rice for the four
varieties during the dry and wet seasons are presented in
Figure 3.2. In both seasons, total milled rice and head
rice yields increased to maximum values until the moisture
content decreased to the point of optimum yield. There-
after, total milled rice yields remained fairly constant,

whereas head rice yields dropped off with a further

l/A loo-grain weight is the weight in grams of 100 grains
of rice.

46

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47

decrease in moisture content. A combined graph as
discussed in Section 2.4 is shown in Figure 35L

The low levels of head rice for the four varieties
at early harvest were primarily due to the presence of many
meature,Agreen and chalky grains, which were easily broken
during hulling and polishing. As the level of these low-
quality kernels decreased, the head rice yield increased.
After reaching the maximum value of head rice yields, the
head rice yield at later dates was caused by the alternate
wetting and drying of the grains, which caused sun-
checking.

When variability arising from varieties and
nitrogen levels was ignored, the maximum head rice level
occurred between 24 and 34 DAB in the dry “season and
between 32 and 38 DAR in the wet seasons. Harvesting
before or after these optimum periods resulted in
significant head rice decreases due either to under-
ripening or over-ripening of the kernels. The
corresponding moisture contents of grain during these
optimum stages were between 19 and 25 percent for the wet
season and between 18 and 21 percent for the dry season.

Camacho, et al. (1978) reported that farmers used
several methods which could be categorized as follows:
twenty-one percent used maturity date, eight percent used a

percentage of 50-69 ripe grains, eleven percent used a

TOTALMILLED RICE
PERCENT OF MAXIMUM

HEAD RICE

§

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48

TOTAL MILLED RICE

WET AND DRY
SEASON

 

 

 

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/ ”i -___, " SUNDRIED HEADRICE
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g 1 l 1
1 l l
DRY SEASON % Mo 22 20 1 8
WET SLEASON % Mo 22 21 1a
1 l L 1 1 1 I 1 1 l L 1 l
20 24 28 30 32 34 36 38 40

DAYS AFTER HEADING

FIGURE 3.3 RELATIONSHIP BETWEEN DAYS AFTER HEADING AND HEAD

RICE PERCENTAGE FROM NANGJU AND DE DETI'A (197D)

49

percentage of 70-89 ripe grains and sixty percent used a
percentage of 90-100 ripe grains. Although most harvested
at maturity, 5 percent delayed up to 10 days after maturity
and a few advanced the harvest 1 to 10 days before
maturity. In the simulation normal distribution was used

with the mean being the Optimum harvest date.

3.2.1.2. Delay in Drying

Rapusas, et al. (1978) found that the quality of
paddy deteriorated with delays in drying. This study was
conducted during both the dry and wet harvesting seasons of
1976 and 1977 in the Grain Processing Laboratory of the
University of the Philippines at Los Banos. The three
study methods of handling paddy were: sack handling with
28 m3/min.-ton aeration, bulk handling with 35m3/HUJn-ton
aeration, and sack handling without aeration treatment.
The three rice varieties tested for both seasons included

IR-26, IR-32 and IR-38.

m Season Results

Based on the percentage of damaged kernels in
milled rice as the controlling factor, non-aerated paddy
was stored from 1 to 3 days before drying without loss in
grade. .Bowever, when delay in drying was prolonged to 15

days, the recovered milled rice had deteriorated to sample

50

grade. The paddy in sacks aerated at an airflow rate of

23m3/ndJn-ton was held after threshing up to 9 days prior
to drying. At an airflow rate of 35m3/min.-ton applied on
paddy handled in bulk, the safe period of drying delay was
extended up to 2 weeks.

Laboratory milling of paddy samples from all the
handling treatments revealed a slight decrease in milling
recovery because of drying delay, although paddy held
without aeration (Figures 3.4 and 3.5) showed a faster rate
of deterioration as delay increased. Aerated paddy in bulk
and in sacks gave an almost uniform milling recovery after
26 days of drying delay. At the same time a drop of less
than 1 percent from the initial milling recovery was
evident for paddy held without aeration treatment.

Head rice yields of aerated paddy in bulk and in
sacks were higher than those obtained from non-aerated
paddy at any period of drying delay. This can be
attributed to the presence of being fewer damaged kernels
which break easily during milling. Paddy aerated at an
airflow rate of 35m3/udJn-ton gave better head rice yields
than those aerated at 28m3/minudxnn Neither airflow rate
maintained the initial head rice yield obtained when paddy

was dried immediately, however.

51

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53

Het.fieason.Besults

Aerated paddy held in sacks and in bulk showed no
lowering of grade based on the percentage of kernels for a
drying delay of 2 or 3 days after wet season harvests.
After ten days, however, a decrease in milling recovery was
experienced in paddy no matter how stored. With aeration,
however, smaller reduction in milling recovery was
experienced. Paddy stored without aeration for the ten-day
period yielded a four percent decrease in milling recovery
as compared to aerated paddy. Smaller decrease of milling
recovery was observed for paddy aerated with 28m3/minu4xnu
followed by the paddy with 35m3/min.-ton. For 10 days
drying delay, there was a 4 percent drop in milling
recovery of paddy stored without aeration as compared to
the initial milling recovery of aerated paddy that was
dried immediately after threshing. The milling recovery of
aerated paddy either in sacks or in bulk dropped by about 2
percent from the initial milling recovery of 66.8 percent
for a 10-day drying delay.

With non-aerated paddy there was a rapid decrease
in head rice yield as drying was delayed 10 days. This
reflected the rapid increase in damaged kernels which are
prone to break during milling. After a two-day delay in
drying there was already a drop in head rice of 4~6 percent

from the initial head rice recovery of 84.6 percent.

54

The graphs on Figures 3.3, 3.4 and 3.5 were
incorporated in the simulation by the use of a "straight-
line approximation" method discussed in Section.2.8. The
drying delay was generated by the computer in accordance

with the probability distribution shown in Figure 3.40.

3.2.1.3 Moisture Content at Milling

Ongkingco, et a1. (1964) reported that there was a
direct correlation between moisture content and milling
recovery. Tables Kl to K8 in Appendix K show the milling
recovery of samples milled one day after drying and at
storage periods of one month, two months and three months
after drying. The milling recovery for rice stored at
moisture contents of 13 to 14.5 percent was the highest at
the one-month milling date. After this there was a gradual
decrease in milling recovery. In the fourth and last
milling, the recovery increased again but to a lower value
than in the first milling. This variation in milling
recovery was attributed to variation in moisture content of
the grain at milling which in turn was attributed to
prevailing weather conditions.

Eriyatno (1979) presented a mill conversion curve
(Figure 3.6)‘which has a mathematical relationship between

rough rice moisture content and mill conversion as follows:

HEAD RICE, CRHEAD I IN PERCENTS OF ROUGH RICE)

50

55

 

‘0

 

 

‘6'

 

 

 

A ‘ ‘
‘ A
A
/
CRHEAD- -0.288790 + 13.715072 mama
—123970912 'RRMC"2
9 257.995278'RRMC”3
cnanox- 1.297751-2542162raamc

' +181.129922'RRMC"2

~392976423' RRMC"3

 

E

O

 

13318 H9008 :IO smaouad NI I )IOUBUD .3318 NBXOUB

.0
"I

 

10

 

 

 

 

 

 

 

'IO

15

MOISTURE CONTENT. WET BASIS (IN PERCENT)

Figure 3.6 Milling Yield Curve
from Eriyatno (1979)

20

56

CRHEAD = -0.28879 + 18.715072 x RRMC - 123.970912

x RRMCZ + 257.996278 x RRMC3

where CRHEAD = percentage of head rice, in percent of
rough rice

RRMC = rough rice moisture content, percent wet
basis

Paras (1976) measured grain rigidityl/ at different
moisture contents (Figures 3.7, 3.8, 3.9 and 3.10). A
graph of the relationship is presented in Figure 3.11.
Rigidity may be converted to pressure by dividing the
rigidity in kilograms by the cross-sectional area of the
plunger on the rigidity tester.

Sang Ha No (1976) presented a characteristic curve
for a whitening machine showing the relationship between
head rice recovery and radial pressure (Figure 3.12). In
this figure, the capacity scale of the lower horizontal
axis was developed using the linear relationship which
exists between average grain radial pressure and machine
capacity. The head rice recovery-radial pressure (H-Pr)
curve constitutes the upper scale of the horizontal axis
showing radial pressure and the left-hand vertical axis
showing head rice recovery; The capacity-electric power
consumption (C-EC) curve involves the horizontal axis

---------------------------------------q--------------------

l/Rigidity is the hardness in kilograms of a grain kernel
measured by a tester consisting of a plunger with a 5 mm.
diameter.

57

 

Figure 3.7.

Rough rice at different
moisture contents was collected
during a drying run using a
flatbed mechanical drier.

 

Figure 3.8. Moisture content of samples
were measured using a capa-
citance moisture meter.

Figure 3.9.

 

The rough rice grain sample
was dehulled by hand.

 

6O

 

Figure 3.10. Pressure was applied on the grain
until it cracked and the fOrce and
pressure recorded. A total of 50
grains at each moisture content
level was tested.

Newton

RPGHHTY(RL

0.11

0.09

.08

.07

.05

.03

.02

.01

61

210
200
190
180
170
180
150
140

130
120

R = 1 8.23 - 0.63 MC

r2=o.97 no
100

8
GRAIN PRESSURE(PL

8

8 8

 

13 . 14 15 18 17 18 19 20 21 22

GRAIN MOISTURE CONTENT, % W.B.
M.C.

Figure 3.11 Grain Moisture Content VS Rigidity
All information where the source is not
noted were gathered by the author for
this dissertation.

Pa

62

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63

(capacity) and the right hand vertical axis (power
consumption). The dotted lines along the H-Pr curve show
the variation caused by differences in cylinder speed,
screen type and counterpressure.

The general characteristic curve shows that when
internal radial pressure is maintained at a low level, head
rice recovery will be high but machine capacity will be
limited and energy consumption per unit of output will be
high.

In contrast, if radial pressure increases beyond
the optimal range noted on the chart, a sharp decrease in
head rice recovery occurs without any significant increase
in power consumption efficiency or machine efficiency.
Therefore, to ensure high machine and milling efficiency,
it is important to maintain the pressure inside the
whitening chamber within the optimal range by controlling
the feed rate and the counterpressure during milling
operation.

The four relationships presented by Ongkingco,
Eriyatno, Paras and Sang Ha No are in close agreement with
each other in that they show that as the grain moisture
content reaches 15 percent or a pressure of 1.75 kg. per

cm.2, the head rice recovery starts decreasing.

64

The milling yield by Eriyatno was adOpted by the
author for predicting head rice yields in the simulation

model.

3.3 min shape Refinements

The various rice varieties differ in their grain
shape (length-width ratio) and hardness. The first
prOperty could be easily measured using the FAO recommended
method. The second property is affected by other variables
such as: moisture content, fertilization and delay in
dryingyl/ Due to lack of information and also the
difficulty involved in segregating these effects, this
author decided not to include variation of grain hardness
(due to variety) in the analysis.

Figure 3.13 shows the relationship between grain
shape and head rice. The grain shape ratio was obtained by
dividing the length of a brown rice kernel by the widthMZ/
Grains with a ratio of l to 2 are classified as round
shape, those with a ratio of 2.1 to 3 are bold and those
with a ratio of 3.1 to 4 are slender. The varieties grown
in the Philippines are either bold or slender. Two

l/See works of Eriyatno (1979), Nangju and De Datta (1970)
and Ongkingco, et al. (196 4).

g/See Appendix J for actual values.

6S

 

 

 

 

 

 

 

 

 

 

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AoHeea mm<=mv
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66'

Japonica (round shape) varieties grown in Korea were

plotted on the graph for comparison purposes.

3.4 Wfimmmmmmmm

Energy consumption and milling time were the two
other performance criteria measured for the model. To
measure energy consumption and milling time, it was
necessary to monitor the following electrically driven
conventional disc-cone mills:

l. Mindanao Progress Corporation, Quezon City
(6.25 tons per hour).

2. Tobacco Industries of the Philippines,
Valenzuela, Bulacan (2.5 tons per hours).

3. U.P.L.B., Los Banos, Laguna (1.0 ton per
hour).

3.4.1 Methodology

The input paddy which was supplied by the National
Grains Authority (NGA) was first weighed in bags. The
weight of the empty bags was then subtracted from this
gross weight to get the net weight of paddy; One observer
was then posted at each machine component and at a
predetermined time, the rice mill was started. The
observers (whose watches were synchronized) then noted the
time of entry and exit of paddy or rice as the case may be.
Voltages, amperages, power factors and efficiencies were

noted at each electric motor at three different times using

67

clamp-on (induction) testers during the milling operation
(Figure 3.14). The time at which each component was shut

down was also noted by the observers.

3.4.2 Analysis of Results

The total milling time then was the time from the
start-up of the mill to the shut—down of the last
component. This was equivalent to the operating time of
the last machine component plus the delays before the paddy
reached the last machine component. In the computer model,
the total milling time was obtained by adding all the
different delays plus the operating time of the last
machine component. The delays were calculated by dividing
one-half of the capacity of the feeder bins by the handling
rate of the machine component immediately ahead of it. The
time measurement data obtained in the milling operation in
the three rice mills were used for validating the computer
results. Figures L1 and L2 in Appendix L show the results
of the time measurements. It was learned that the practice
of waiting for the feeder bins to be half-full increases
the time delay between two batches of paddy. It took
almost an hour (53 minutes) for the rice to travel from the
intake to the output in the 2.5 ton-per-hour locally made
mill. It took only seven minutes for the imported 6.25-

ton-per-hour mill because of the smaller bins.

68

 

Figure 3.14 Measurement of voltage and current of motors during
a milling run. Some motors were inaccessible and
must be measured at the control panel.

69

 

Figure 3.15. Motor nameplate data were noted as to the power
factor and efficiency ratings.

70

The energy consumption was computed by the

following formulas:

Power (kw) = Current (I) x Voltage (V) x
Power Factorl/ x EfficiencyZ/
Energy Consumed (kw-hr) = Power (kw) x Processing
A Time (hr)

Current, voltage, power factor, efficiency and time
came from measurements on each machine component. The
total energy was the sum of the energy consumption of each
individual machine component. Appendix Table A1 shows the

results of these measurements and computations.

3.4.3 Discussion of the Different Machine Components
Figure 3.16 shows a double-sieve precleaner which
is commonly used in Philippine rice mills. The large-mesh
upper sieve performs a 'scalping' operation which is the
removal of straws and other large impurities by allowing
the smaller grains to pass through. The lower fine-mesh
sieve performs a ”screening" action which is the removal of
smaller impurities such as sand by allowing them to pass

through the sieve opening. The sieve assembly was mounted

l/Power factor of the electric motor is the cosine function
of the phase angle or the ratio of resistance to impedance.

Z/Efficiency of the electric motor in converting electrical
energy into mechanical energy.

 

 

 

 

 

71

 

 

 

 

 

 

 

 

 

 

 

 

 

A - Input paddy

Figure 3.16 Double

7f // / / / 7 ff/ff/ fr/ 7r/ 7///I//7//‘/7,.

___|_..'.L_

0qro--0-.--

 

 

 

 

 

 

 

 

 

B
I
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r '7 ”l
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. . :::
! l” ___..a
L‘T'] Lm '; [HF_‘
3 c
l - Large mesh sieve B - Large
2 - Fine mesh sieve C - Fine

sieve precleaner

impurities
impurities
D - Clean paddy

72

on flexible wooden legs and driven in a reciprocating
motion by an eccentric mechanism. Figure 3.17 shows a
single-drum precleaner used in Japanese mills. It operates
on principles similar to the double sieve except for the
addition of a suction blower which eliminates fine dust and
a scalping drum which eliminates large impurities.

The power requirements of these two types of
precleaners are shown in Figure 3.18. The double-sieve
precleaner has a lower power consumption but has the
disadvantage of creating a very dusty Operating condition.
The single-drum precleaner eliminates this problem by
blowing the dust through exhaust pipes and out of the mill
building.

The disc huller (Figure 3.19) consists of two cast-
iron discs partly coated with an abrasive emery layer. The
top disc is fixed in the frame housing while the bottom
disc is driven by a belt pulley. The paddy is driven
centrifugally between the discs and approximately sixty
percent of the paddy is dehulled by the friction and
pressure between the discs and grains.

The rubber-roll huller was developed in order to
provide a huller that does not damage the second protective
layer (pericarp) of the kernel (Figure 3.20). This is
advantageous in that transporting and storage of brown rice

greatly reduces the space requirement. The rubber-roll

 

O‘U‘lAuNb—l

n

 

73

 

 

Vibrating comb
Dispersing plate
Feeding roll
Regulating valve
Rotating rubber wing
Moving rubber brush

Figure

mmcnou>

3.17 Single drum

 

Input paddy

Large impurities

Dust

Medium size impurities
Sand, soil, etc.

Clean paddy

precleaner

74

 

 

12

 

11

 

.5
O

 

 

 

 

Y=1fl4XQ8

 

(JAPANESE)
fl=032

 

W/

 

/

 

if

 

/

 

 

POWER REQUIREMENT, HORSEPOWER
N

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6 /7
5 // V//
, / Ti
4 K/ :
/ .AK‘: ?
.J ;
3 / “ \I=o.61x°-86 A;
/ (PHILIPPINES) ‘
2._41_‘/// R=1b
1 /U
o 2 4 e a 10 12 14 16

CAPACITY, TON S PER HOUR

FIGURE 3.18 PRECLEANER CAPACITY AND POWER REQUIREMENT.

7.0

6.0

6.0

5.0

4.0

3.0

2.0

1.0

POWER REQUIREMENT, KWATI'S

.n UIBJF‘

@mVOU‘I

75

Input paddy tube 10
Paddy tube adjustment 11
Paddy spreader 12
Fixed disc level adjus- 13
ting screw 14
Fixed disc emery 15
Revolving disc emery 16
Fixed disc cast iron frame17
Outlet tube 18
Huller frame

Figure 3.19 Disc

 

- Huller stand

- Huller base

- Revolving disc drive shaft

- Upper bearing

— Drive belt

- Drive pulley

- Lower thrust bearing

- Disc clearance adjusting wheel
- Adjustment bar

huller

 

 

 

’m/
x 2:

 

 

 

 

 

 

 

 

 

 

kMNI—I

Figure

 

Feed shutter
Feeding roll
Regulating valve
Stationary roll

- Rubber plate

- Aspirator

- Outlet for rice

— Screw conveyor for
unripe grain

LOCKING

3.20 Schematic diagram of a rubber roll huller

77

huller consists of two rubber rolls, one of which has a
fixed position and the other has an adjustable position
that enables the desired clearance between the two rolls to
be fixed. The rolls are mechanically driven in opposite
directions. The adjustable roller turns at a speed 25
percent lower than the fixed roller. The paddy passes
through the gap between the rolls and is twisted and
dehulled. The resiliency of the rubber roll provides some
tolerance for variation in grain sizes and its smoothness
eliminates abrasion to the pericarp. The relationship
between hulling capacity and hulling time is shown in
Figure 3.21. Different grain types, iJL, short, medium
and long, have different hulling times. The power
requirement for these hullers is shown in Figure 3.22.

The separation of broken brown rice and coarse bran
(damaged pericarp) after hulling provides additional income
to the rice mill. Otherwise, the broken brown rice would
be blown out of the mill in the husk aspiration process.
Figure13.23 shows a V-type sifter commonly employed in
Philippine mills for this purpose. It operates on the
"screening" principle and is oscillated by an eccentric
drive. The power requirement is shown in Figure 3.24.

Due to the lack of individually driven and
electrically powered machines in the Philippines, there are

only three actual data points available. However, the

HULLING TlME,HRS/TON

78

 

 

. ‘
MEDIU
y=5.04¢:‘1 '5“

 

 

 

 

LONG

 

 

F5.898'1 .76X 11i—

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

\
FR
\\

CAPACITY.TONS/HR.

Figure 3.21 Huller Capacity VS Hulling Time

1.1

POWER REQUIREMENT, H.P.

14

13

12

11

10

79

 

 

 

 

 

 

 

—.—-—J—-— -—- p-.. -—

i- u.

 

 

 

 

“+—

1-- -4 - __ "T

 

 

 

 

 

 

 

 

 

 

 

 

 

 

7”

 

 

 

 

 

 

CAPACITY, TONS PER HOUR

FIGURE 3.22 l-IULLER CAPACITY

AND POWER REQUIREMENT RELATIONSHIP

11

‘10

POWER REQUIREMENTS, KWATTS

80

 

Figure 3.23 V—type Sifter

81

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82

curve must pass through the origin as an initial condition
that the power requirement is zero when the capacity is
zero. The values obtained in the measurement show that for
this machine, the power requirement goes down as the
capacity increases which rules out the use of linear
relationship between capacity and power requirement. In
view of these considerations in addition to the fact that
the other machines used power curves, the relationship used
for the power sifter was a power curve. The next machine
used in the processing of rice is the husk aspirator shown
in Figure 3.25. The operation is based on screening and
aspiration. The mixture of husks, brown rice, brokens,
bran and dust is discharged on an oscillating sieve which
separates the brokens, bran and dust. The mixture of
husks, paddy and brown rice is then passed through the husk
aspirator which separates out the husks and dust. The
power requirement of this machine is shown in Figure 3.26.
For the husk aspirator, there are only two actual
data points available and there is no indication that the
power requirement goes down as the capacity increases.
Another machine which is constructed similar to the husk
aspirator but not included in conventional cone-type rice
mills is the aspirator plus stoner for use in removing
impurities from paddy in rubber-roll mills. There are

three actual data points for this machine and the power

 
  
  

HUSKS
IMMATURED GRAINS
BROWN RICE-PADm

 

l l l I I

 

  
  
 

firBRAN-DUST
. BROKENS

 

 

 

 

 

 

 

 

 

.-

.-

 

Oscillating fine mesh sieve A
Suction blower B
Feeding trough C
Separating chambers D
E

Figure 3.25 Double action husk

 

rice and husk
airblast
husks

- rice

airblast

aspirator.

POWER REQUIREMENT, HORSEPOWER

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

84

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[I 3.5
/ 3.0
I / f
v -- 0.75 x‘ -36 I \
(HUSK ASPIRATOR)
I ’ /
J/ / 2.0
I o 1.5
7V3. v=o.14 x1-75 _,1 o
/ / (ASPIRATOR WITH STONER)
/ / r2 = 0.97
/ / 0.5
o
1 2 3 4 5 6

RICEMILL CAPACITY, TONS PER HOUR

FIGURE 3.26 HUSK ASPIRATOR AND ASPIRATOR WITH STONER

POWER REQUIREMENT.

POWER REQUIREMENT, KWATTS

85'-

curve is similar to the power curve for the husk aspirator.
The efficiency of hullers varies between 60 and 95 percent
which leaves from 5 to 40 percent unhulled. The
compartment-type paddy separator is used in the Philippines
for separating the paddy from the brown rice and it uses
the differences in mass and coefficient of friction of the
two grains. .A newer machine, the tray-type separator,
utilizes their differences in specific gravity but is not
effective in countries where moisture content of paddy
varies greatly. Figure 3.27 shows the operation of a
compartment-type paddy separator. The mixture of paddy and
brown rice is introduced into the center of a specially
shaped passage (bottom diagram) which is steadily
reciprocated in the direction of the arrows on the left.
The paddy, because of its heavier mass and lower
coefficient of friction, hits the walls harder causing it
to bounce from wall to wall through the passages going up
the inclined passage until it reaches the left—hand side.

The lighter and stickier brown rice hits the wall
more softly and because of the downhill inclined surface it
eventually reaches the right-hand side of the passage.
The frequency of the oscillation is usually adjustable and
some machines also have adjustable stroke length (Figure
3.28). Figures 3.29 and 3.30 show the hulling time and

power requirement, respectively.

86

 

 

 

Figure 3.27 Grain Flow through the Paddy Separator

87

 

 

 

 

 

 

IZI
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l

 

 

 

 

 

A
m-
PLA

 

 

 

 

 

 

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q.

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SEPARATOR TABLE '8‘!
COMPARTMENT < 24) 'I 9 "
GRAIN DISTRIBUTION Box IO
SPOUT FEEDING COMPARTMENT :Ip
ECOENTRIO DRIVE :1}.

SUSPENSION FOR ECCENTRIC POD I3
ELECTRIC MOTOR WITH SPEED 'l4)
CONTROL

.'—i I I I
I. _.-.‘ III II I 1 BROWN
4 RICE

 

FLYWHEEL/ DRIVEN PULLEY
INCLINATION ADJUSTMENT
TABLE LOCKING DEVICES
TABLE SUPPORT (SIX)
COMPARTME NT FEED CONTROL
CENTRAL tFEED CONTROL

HANDWHEEL FOR SPEED
CONTROL

Figure 3.28 Components Of a Paddy Separator

HRS/TON

SEPARATING TIME

3.5

88

 

 

 

 

3.0

2.5

2.0

1.5

1.0

0.5

 

 

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LONG GRAIN y=3.24o‘°-°9"
3:034

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L I ' f
I . o
I I
I I '
I I I
I I ,
I I _ _ - _.. I -_ ”i" ._ 7 . “4----..“
. ~ ' L ‘
I I I
I I
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; f I g
i a r
7 I
SHORT G‘sAalN ‘ .
' 0 9X I '
*_~-'- _. F2.846 T~+."V' J-—-————L—~—c \ c——'
I ‘ a K a
i
' ‘ I i
i e ‘ I I
I I L
I I l I I

 

 

0 0.2 0.4

FIGURE 3.29

CAPACITY,

TONS / HR.

PADDY SEPARATOR CAPACITY VS SEPARATING TIME

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.3 2.4

POWER REQUIREMENT, HORSEPOWER

89

 

5.0

4.5

 

 

 

 

 

 

“I

 

 

 

 

  
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

 

 

I
I m '
I I
I ' i
g 1.07 ?

Y = 1 .57x " I
'2 = 0.84 f I ;
(fixed stroke) ‘ I
I I1 ‘ '

I 7 ‘
I I
I I O i
s I I
I A '
I y 1.1 9
' v = 0.76X I
r2 = .97
m __-_. (adjustable stroke) _—.
. I I
I
I I
I
O
1 2 3 4

RICEMILL CAPACITY, TON 8 PER HOUR

Figure 3.30 Paddy Separator Energy Requirement VS Capacity

POWER REQUIREMENT, KWATTS

90

The next operation is done by a whitening machine
and involves the removal of the pericarp and the bran layer
of the brown rice. Four kinds of whitening machines are
widely used in the rice-processing industry, namely:

a. The vertical abrasive whitening cone,

b. the Engleberg friction whitener,

c. the horizontal abrasive whitener, and

d. the horizontal jet friction whitener.

Figure 3.31 shows the whitening cone which consists
basically of an inverted frustum of a steel-reinforced cone
made of emery and carborundum stone surrounded by a fine
mesh sieve and 30 to 50 mun wide rubber brakes. In
operation, brown rice is introduced from the tOp and is
subjected to a grinding action and pressure of about 5 Pa,
as the cone rotates and the rubber brakes prevent the rice
from moving about freely. The Engleberg whitener is not
commonly found in the Philippines as a whitening machine
but rather as a one-pass rice mill. However, in some
plants in the 0.8., it is used in series (10 to 20 units)
whitening.

The horizontal abrasive whitener (Figure 3.32)
which is also used in conjunction with the horizontal jet
friction whitener (Figure 3.33) is the latest technique in
rice whitening. The abrasive whitener consists of

carborundum rollers and a feed screw mounted on a shaft.

91

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110 I? ' "

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

3 9/47/71 11

 

 

‘ i .
////////////{///7f/;////////fl

 

'1 - Feeding valve 7 - Bran outlet

2 - Drive shaft 8 - Gear drive for bran scraper
3‘- Rotation 9 - Belt drive for bran scraper
4 - Rubber brake 10 - Air exhaust

S — Rice outlet 11 - Support bar for clearance

6 — Bran scraper adjustment

12 - Upper bearing

Figure 3.31 Whitening cone

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94
The assembly is enclosed by a perforated-steel cylinder
with three equally spaced bars with resistance pieces
(similar in purpose to the rubber brakes of the cone
whitener). The horizontal jet friction whitener is a
result of several improvements on the Engleberg mill.
These improvements include position of the outlet,
adjustable weighted outlet and air jet which blows off the
bran and cools the milled rice. Figures 3.34 and 3.35,
respectively, show the whitening time and power requirement
of the different whiteners discussed.

The next operation is the removal of brewer‘s and
small brokens from the milled rice. This is accomplished
by the use of an oscillating sieve (Figure 3.36) using the
screening principle. The t0p sieve consists of a single
screen which allows the smaller particles to pass through
the lower compartment. The oscillating motion of the sieve
speeds up the process. The sieve in the middle consists of
two screens of different sizes and also works on the
"screening" principle allowing the smaller particles to
pass through the bottom compartment. This arrangement
results in three sizes of grain particles, namely, whole or
head grain, large broken and small broken. The bottom
system consists of two sieves of single screen each and

produces three sizes of product. Any number of grain sizes

HRS/TON’

WHITENING TIME,

3.5

3.0

2.5

2.0

1.5

1.0

0.5

95

 

 

F

 

LONG GRAIN
I

y = 7.320 '2-1 1 "

 

 

 

MEDIUM GRAIN

I
ya 6.74e°2.14x I

 

 

 

 

 

SHORT GRAIN .4

y -_-.- 6.089-‘2'1 4X

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

fl=039
' o
. o
i
y
i
I
I
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.3 0.9
CAPACITY, TONS/HR.

Figure 3.34 Cone Whitener Capacity VS Whitening Time

HORSEPOWER PER UNIT

POWER REQUIREMENT

14

12

.0
O

96

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

    

/
g I
I -I f
i
Z ,3 ‘
I i
I I
l I
I i
g I
;L .
I a
I
l
. 0.62 .
- c _ v = 1.23x
' (CYLINDER)
I r2 = .97 /
i

 

  

 

0.41

 

 

  

 

Y = 1 3x
(FRICTION)

l r2=1.0

4

 

 

 

 

 

1 2 3 4
CAPACITY, TONS PER HOUR

(RICEMILL CAPACITY FOR CONE TYPE)

FIGURE 3.35 WHITENER CAPACITY AND POWER REQUIRED

1O

POWER REQUIREMENT, KWATI'S

98

could be separated by the use of different sieve sizes.

The power requirement is on Figure 3.37.

3.5 Distribution W 91 Inmu: Raddy More

The distribution function of paddy purity and
moisture content is determined using the Chi-square
goodness-of-fit test (Appendix D). The frequency graphs of
these two variables are shown in Figures 3.38 and 3.39.
These two variables plus the harvesting data were generated
using a table look-up function (Appendix B) in conjunction
with the cumulative normal distribution function and a
computer built-in random number generator. A table look-up
function is one of the computer subroutines specifically
designed for simulation work. In some situations, one
system variable is to be causally related to another and
either an explicit mathematical function is not known or
one cannot be reasonably assumed. More details are in
Appendix B. Physical damage, iae., cracking and chemical
(fermentation) are affected by drying delay. Drying delay
was defined as the number of days from harvesting to drying
of paddy at equilibrium moisture content. It was assumed
that the delay was the same for sun drying and mechanical
drying. Drying delay is caused by three successive delays,
cutting, hauling and threshing. It was simulated by a

third order gamma distribution function. The actual

99

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100

 

 

0.4

 

 

0.3

 

 

PROBABlLITY
o 2

0.1

 

 

 

 

/

9.0 10.0 11.0 12.0 13.0 "14.0 15.0
MOISTURE CONTENT; PERCENT

0:9

Figure 3.38 Probability distribution f0r moisture
content of paddy delivered for rice
milling in the Bicol region.

 

 

0.4

3

 

 

 

 

PROBABILITY
0 2

 

 

 

 

 

_ ///

v
I\
CI
0

PURITY OF PADDY, DECIMAL

 

0.990
1.000

0 941
0.952
0 963

Figure 3.39 Probability distribution for purity of
paddy delivered for rice milling in
the Bicol region.

101

cumulative distribution graph is shown in Figure 3.40. For
more details on gamma distribution function and the

subroutine gamma, see Appendix C.

3.6 .Computerlmnlementationofthencdel

The computer used in the modeling was a Radio Shack
TBS-80 Model I with 16K RAM and BASIC level II language.
The program is depicted by the flow chart in Figure 3.41.
After initialization, the rice mill parameters and grain
variables were read. These include capacities of the rice
mill, precleaner, huller, separator, huller bin, bran
sifter bin and whitener bin; huller, whitener and separator
types and the number of hullers and whiteners. The grain
variables include drying method, grain shape, grain type
and the growing season.

The first operation was precleaning, which involves
computation of the material reduction variable by using
subroutine random. This subroutine is a table look-up
function with a normal cumulative distribution function and
a random number generator. For more details on the
subroutine random, see Appendix B. Other variables
computed were run time, time delay to fill huller bin and
energy used. The next step was the hulling operation

which was by either a disc or rubber-roll huller. The

102

 

 

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._15

 

 

 

0.1

 

 

 

 

PROBABILITY

 

.05

 

 

1 0 0

 

 

0.9

 

 

0.8

7

 

 

0

 

0.6

 

 

 

 

0.4

 

CUMULATIVE PROBABILITY
3

 

8 9 10 11 12 13 14 15 16 17

DRYING DELAY, DAYS

Figure 3.40 Probability and cumulative distribution of drying
delay for paddy delivered for rice milling in the
Bicol region.

 

103

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103a

C c“;- D

DETERMINE A RANDOM VALUE
FROM BUILT-IN COMPUTER FUNC.

 

 

 

 

{

COMPUTE THE DIFFERENCE BETWEEN THE INDEPENDENT
ARGUMENT (VARIABLE) AND THE SMALLEST VALUE
OF THE INDEPENDENT ARGUMENT

1

DUM = Yqb- SMALL

 

 

 

 

 

 

 

NO

 

 

 

DUM=OI

 

 

V

 

 

 

 

 

DUM=(K-1)*DFI I=1 + DUM/DIFF

 

 

 

 

 

IS
=K

NO 7 YES I=K-‘I

 

 

 

 

 

 

 

 

XBAL = F¢ (Z) + ( (FQD (2+1) - F¢(Z) I/DFI) * (DUM - (FlX(Z-1) ) - DFI

 

 

 

 

(-1...)

Figure 3.42 Flow chart of subroutine RANDOM. Subroutine TAB is similarto
the above chart except for the absence of the first block.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

XCAL= -LOG (TD) / (FIX( Q1 /51 )

 

 

 

I-

< RETURN )

Figure 3.43 Flow chart of subroutine GAMMA.

104

performance of the huller varied also with grain type,

i.e., short, medium or long. The next operation was coarse
bran separation. Coarse bran has some monetary value so it
was separated before passing the material to the husk
aspirator. After the husk aspiration process, the mixture
of paddy and brown rice is passed to a separator. The
performance of the separator varies again with the grain
type. The next step, whitening of the brown rice, was done
in series whiteners. The number of whiteners varied from 1
to 6 depending on the size and type of mill. The final
step was the grading operation where brewer's rice was
separated from the mixture of broken and head rice.

The quality indexl/ was computed in the grading
operation. The index is the product of four indices: (1)
moisture content at milling, (2) harvesting delay, (3)
grain shape and (4) drying delay. The quality of paddy
index includes the head rice recovery percentage from the
milling operation. Other variables computed were total run
time, rice output, recovery, head rice, discolored kernels

and total energy used in the operation.

l/This is similar to Steelefls multiplier used to obtain dry
matter loss in corn deterioration. See the work of Steele,
et al. (1969).

program

105

3.7 Anfixamnlscfmmnsethemssnill

WM].

The data that the model user must input into the

(lines 175 to 181) in Appendix E are as follows:

Nwmber of Replication (R) = 1 to 100

Paddy Cleaner Capacity (PCAP) = 0.33 to 1.83
tons per hour

Huller Bin Capacity (HBIN) = 0.0069 m3
Whitener Bin Capacity (WBIN) = 0.009 m3

giddy Separator Bin Capacity (PBIN) = 0.0089
Rice Mill Capacity (RCAP) = 0.33 to 0.83 tons
per hour

Number of Paddy Cleaner (CPN) = 1.0

Separator Capacity (SCAP) = 0.495 to 1.98 tons
per hour

Number of Whitener (NW) = l or 2

Whitener Capacity (WCAP) = 0.255 to 0.731 ton
per hour

Huller Type (HT): Disc or Rubber Roller
Whitener Type (WT): Cone or cylindrical
Drying Method (DM): Sun or mechanical

Paddy Huller Capacity (PHCAP) = 0.294 to 1.83
tons per hour

Number of Paddy Huller (HNP) = l or 2
Grain Shape (GS) = 1.0 to 4.0
Input Paddy Mass (I) = any mass, say 10.0 tons

Separator Type (ST): Fixed or adjustable

106

8. Grain Type (GT): Short, medium and long

t. Growing Season (SEAS): Wet or Dry

u. Sifter Bin Capacity (SFBIN) = 0.077 m3

Once all the above information is typed into the
statements in lines 175 to 181 of the computer program, the
rice mill performance model is ready for a simulation run.
In the TRS-80, this is done by typing in "RUN" and pressing
the input button. The computer then prints or displays the
results as in Table 3.1.

The first column gives the time in hours it took
the rice mill to process 10 tons of paddy and the second
column is the output in tons of milled rice. Column three
is the head rice recovery based on clean paddy. It is
obtained by dividing column two by the weight of paddy
after the precleaner. The fourth column gives the head
rice recovery which is based on the total milled rice
recovered. Column five gives the percentage of discolored
or fermented grain. The last column gives the energy in
kilowatt-hours that was used in the rice milling
Operation.

Different combinations of equipment such as disc
huller-cone whitener-fixed separator or rubber roll huller-
cone whitener-adjustable separator, etc. may be used. The
bin capacities may also be changed to vary delay times.

The number of equipment may also be changed such as from

107

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108

double parallel to single huller. Growing season, grain

type, grain shape and drying method may also be changed.

CHAPTER IV

LINEAR NETWORK ECONOMIC MODEL

The linear network economic model was developed to
evaluate the cost of milling rice over a range of
capacities. The model takes into account the differences
in the amount of by-product produced by each mill. For
example, the Engleberg type steel hullers produce large
amounts of bran, powdered husks and brewer‘s rice which have
some economic value. To compare such a mill with a rubber-
roll mill in terms of rice output alone would be biased as
to by-product values.

To evaluate the milling system's cost of operation
over a range of capacities, the system was analysed as a
set of components described in terms of their mass-energy
characteristics. The components of a rice mill are of two
types, material transformation and transportation
components. A material transformation is conversion of the
input material into an output product. The transformations
are performed by the application of processing energy which
is non-linear. Transportation components incur a movement

material transfer energy cost.

109

110

4.1 .Theorx.9f Linear Netnork.£cgn9mis.nodel

Linear network economic modeling utilizes
Electrical Network Theory and the Principle of Energy
Conservation. Electrical Network Theory was developed from
Kirchoff's Circuit Laws, namely, Kirchoff's Current Law
and Kirchoff's Voltage Law. This approach was used by
Holtman, et al. in 1972 in the analysis of a poultry farm,
and the following year Hughes applied it to the analysis of
beef production systems in Michigan. The latter model was
also applied to a dairy farm and later to a field crOp

production system.

4.1.1 Kirchoff's Circuit Laws

These laws could be best explained by an
illustration. Consider an electrical circuit consisting of
two resistors, l and 2, and a battery as shown in Figure
401a. Kirchoff's Current Law states that the sum of all
currents flowing into a junction is zero. Applying this
rule to junction 0 in the figure, the symbols i1 and 12

refer to the current passing through resistors R1 and R2

and the symbol 13 refers to the current through battery Bl-

i1 + 12 - i3 = 0

i3 = 11 + i2

111

 

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loop

i?¥¥L\
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loop
(a) (b)

Figure 4.1 Example circuit and its linear network.

0

 

.1 3; 2
_ l _

 

(a)

Figure 4.2 Modified circuit and its linear
network.

112

The result is obvious to the reader from the figure
by inspection. Kirchoff‘s‘Voltage Law on the other hand
states that the sum of voltages around a closed loop equals
zero. Applying this rule to 100ps l, 2 and 3 in Figure
4.1a:

0
0

loop 3: V1 - v2

loop 2: V3 + V2
loop 1: V3 + V1 = 0
Again, the results are seen to be correct by

inspection. Figure 4.1a could be transformed into another
form called linear network shown in Figure 4.lb. The
circuit equations could be written for this network using
Kirchoff's Laws. For large and complicated networks, a
slight modification of the circuit is necessary. Consider
again the circuit in Figure 401a. The circuit could be
redrawn as shown in Figure 4.2a. This time the bottom leg
of the circuit was replaced by a common ground. The linear
network could be drawn as shown in Figure 402b. All
grounded ends of linkages are marked by a letter R which
stands for reference. A ground or reference point means
that all potentials are at the same level. Kirchoff's
Circuit Laws are still applicable to this form of the
circuit and the linear network and would give the same
results except that when the voltages are added, linkages

must be selected that end in a reference point to make sure

113

that a 100p is completed. Also, the reader is not supposed
to go over a reference point when summing voltages in
situations where another linkage is attached to the
reference point. Additional discussion of the details of
this method is provided in the procedure of the development
of this model.

In applying linear network analysis to economic
models, electrical currents become flow rates of materials
and electric potentials (measured with respect to a
reference point) become energy costs. This analogy is
applicable to transport and material storage operations.
These two operations could be modeled by resistors and
'capacitors in electrical network theory. It should be
observed that the existence of electrical analogs for the
transport and storage processes did not imply the existence
of every economic and ecological component or system. On
the contrary, there are no electrical analogs to material
transformation processes. Thus it is necessary to use the
Principle of Energy Conservation in the analysis of
material transformation processes. This principle states
that the sum of all energy input to a process equals the
sum of all the energy output.

The production processes in the physical and
agricultural industries represent a sequence of

transformations of the structural state of materials to

114

achieve a well-defined physical, chemical, biological or
technological form. Each such transformation can be
abstracted as a material input-output process
characterizable by a model as shown in Figure 403 for three
input materials, one useful product and one by-product. In
this model, the Y1, i = l, 2, ..., 5, represents the flow
rates of the five materials. A coefficient K,
characterizing the transformation, is introduced here in
writing the equations. This coefficient has a variety of
specific interpretations, depending on the level of
analysis. For example, if the transformation is associated
with a firm, industry or geographic region, then it is
called the "technological coefficients of production." On
the other hand, in other analyses they may be used to
characterize the composition of the output material in
terms of the inputs and by—products. In the former
association, changes in the coefficient K reflect changes
in the technology of production. However, in the latter
case they are unique to the particular product Y5-

Applying the conservation of energy principle to

the process in Figure 4.3, we have:

Output energy + energy of the inputs + energy lost
in the waste + processing energy = 0

01'

115

 

INPUT OUTPUT
INPUT
INPUT BY-PRODUCT

Figure 4.3 A Typical Industrial Process

116
XSYS + (XlYl + X2Y2 + X3Y3 = X4Y4) = f(Y5)Y5 = 0 (A1)

where f(Y5) is a function of Y5 and represents the
processing energy per unit of output. Xi is the energy per
unit of material Yi-

Also, from the figure, we have the flow model

equations:

Y1 = Kle
Y2 = K2Y5
Y3 = K3Y5
Y4 = K4Y5
where Ki = technical coefficient of proportionality for the

particular material "i."

Substituting these equations into A(l),

x5 =4-K1X1 - K2x2 - K3X3 - K4X4 - f(Y5)
or X5 =2 Kij - f (Y5)
.=1
or 3 [X1]

X5 = [K1K2K3K41 [X21 - f<Y5>
[X3]

[x4]

This cost equation constitutes a coordinated linear
network and economic model of the transformation process.
The matrix product to the right of the equality sign
~represent the energy costs involved in making the inputs

available to the process and in removing the joint products

117

or "waste” from the process. In the context of a system
design problem, Y5 represents the design capacity of the
plant and f(Y5) denotes the variation in the processing
costs associated with the scale of operation or design
capacity. In the context of existing plant management
problems, Y5 represents the level of output and f(Y5)
denotes the variation of processing costs with the
different levels of output. In both cases, this function
represents precisely the economies of scale to the various
forms of energy and monetary cost associated with volume
rates of transformation process. These economies of scale
are of central concern in assessing the ecological and
sociological impact of modern technology. It is these
economies of scale particularly with respect to labor that
motivate much of the contemporary trend to high-volume
geographically concentrated industrial production and
highly specialized large-scale agricultural production with

the attendant spatial concentration of people and wastes.

4.2 W

4.2.1 Energy Notations

A notational scheme has been adopted for the
formulation of component models (Figure 4.3%. Material

flow rates of material "i" into or out of component "j"

are denoted Yij (i.eq.‘Yij has units 0f quantity 0f

118

material per unit time). The amount of energy "m"
associated with this material is denoted xmij (i.e., Xmij
has units of quantity of energy "m" per unit quantity of
material). The product xminij then denotes an energy
flow rate.

Associated with each material flow rate is a vector
value that represents the monetary cost, capital outlay and
the energy required to put a unit of the material into its
current form and location. In this model, labor is
formulated as an energy cost, rather than as a flow of
services, and is considered as a nonrenewable resource
along with solar and physical forms of energy. Land is a
measure of the solar energy needed to produce a crop.
Elements of the energy cost vector are denoted by xmij (Mnfl
indicates the energy type) where:

m = 1: capital ($)

m = 2: human energy or labor (man-hours)

m = 3: fossil energy (horsepower hours)

m = 4: electrical energy (kilowatt-hours)

m = 5: land (acres)
or all the above could be replaced by:

m = 6: dollar cost ($)

Example: Xmij = X611 = P 1.00 per kg. (the cost of paddy)

119

Instead of using the several energy dimensions
discussed above, the analysis could be simplified by an
alternate measure, namely, cost per unit of material. The
analysis, of course, is valid only for the prices and costs
actually used. The unit dollar cost, x51j, of material "i"
for component "j" is among other things a scalar function
of the energy costs of the other five energy forms. The
value 0f x61j depends on the relative availability of the
five forms of energy and the preference society places on
each of the input materials.

The precleaner subsystem (Figure 404) input
includes mixed dried paddy rice and impurities which are

separated in the process. The flow model equation is:

Yil = KilYOl i = 1' 2' 0.004

where Yil is the quantity of material ”i1" required or

taken out to produce one unit of clean paddy.

The OUtPUt Y01 determines the quantities of the
other flows and is called the stimulus variable. The flows
other than the stimulus variable are called response
variables. The Kil's are the technical coefficients for
the components.

The conservation of energy principle requires that
the net energy flow into the component plus the applied

processing energy must equal zero. The expression:

120

 

DRIED
PADDY
CHAFF 1 SAND
1 1
v
41 21
31
01

CLEAN STRAW
PADDY

Pre-cleaner component - 1

Material - ij
Clean paddy - O1
Dried paddy - 11
Sand -21
Straw - 31
Chaff 41

Y“: Kqu 1: 1 , 2, 4

4
"1:. m,m m=1,2,...5
‘01 l__2_:'1Kux n 1'1 “01)

FIGURE 4.4 DIAGRAM OF PRECLEANER COMPONENT PROCESS

121

Iil Kilxmil m = l, 2, ....6
is an accumulation of various energy forms 'm" in the
input and output materials required to produce one ton of
clean paddy.

Processing costs include the cost of machinery,
building, labor, fuel, taxes, depreciation, etc. The
processing energy cost function is typically a non-linear

function of the production level. The amount of processing

energy 'm' required for one ton of clean paddy is:
fm1(Y01) m = 1’ 2' .0006

Cost relation of the components is:

.5

xm01 = 1:. Kilxmil - fml (Yov m = 1. 2. ----6
=1
In this particular analysis, the cost per unit of
material, which is the economic measure of the energy level
spent to place the material in its current form and
location was utilized. The analysis is then, of course,

valid only for the prices and the costs actually used.

4.2.1.1 Precleaner Component
Applying the representation of a process in Figure
403 to the first rice milling component, the diagram of a

precleaner component process is obtained as in Figure 4.4.

122

The input to the process is dried paddy and the output is
dried paddy. The by-products are chaff, sand and straw.

The flow model equation is:

Ki]- : KilYOI i = 1' 2' 0.004

where Yil is the quantity of material "i1" required or

taken out to produce one unit of clean paddy.

4.2.1.2 Huller Component

For the huller component, the input is clean paddy‘
and the product is a mixture of brown rice, hull, broken
rice, coarse bran and paddy mixture. The flow model

equation is:

Y12 = K12Y02 i = 1, 2

where Y12 is the quantity of material "12" required or
taken out to produce one unit of a mixture of brown rice,
hull, broken rice, coarse bran and paddy mixture. Figure

4.5 shows this component and the related equations.

4.2.1.3 Plansifter and Aspirator

The next component after the huller is the
plansifter and aspirator component. The input is the
mixture of brown rice, hull, broken rice, coarse bran and
paddy mixture; The output is a mixture of paddy, brown
rice and hull. The by-products are brokens and coarse

bran. The flow model equation is:

123

CLEAN

PADDY. Y12 = K12Y02

x32 = K12x1“; ' Igwoz)

 

BROWN RICE, HULL
BROKEN RICE, COARSE
BRAN AND PADDY MIXTURE

Figure 4.5 Diagram of Huller Component Process

43

 

Y|3 = Klsyoa
i = 1 , 2, .. 4
03 m 4 m
PADDY, BROWN x03 = -£ K.3x,3

RICE & HULL =1
1 3 4:le03)
23 v
m = 1 , 2, 6
BROKENS
COARSE BRAN

Figure 4.6 Diagram of Sifter Components Process

124

Yi3 = K13Y03 i = 1' 2' 3

where Yi3 is the quantity of material "i3" required to
produce one unit of mixture. Figure 406 shows the

component and the related equations.

4.2.1.4 Paddy Separator Component
The next component, the paddy separator, separates

the brown rice from the paddy. The flow model equation is:

Yis = KisYos i = 1' 2

where YiS is the quantity of material "i5" required or

taken out to produce one unit of brown rice. Figure 457

shows the component and the related equations.

4.2.1.5 First Stage Whitener Component

The first stage whitening process has brown rice as
input. The product consists of first-stage milled rice and
the by-product is dark bran. Figures 4.8, 4.9 and 4.10
show the first-, second- and third-stage whitening process
components and the related equations which are similar to

previously discussed components.

4.2.1.6 Grader Component
The last step in the rice milling process is the

grading of milled rice. The input is third-stage milled

BROWN RICE
AND PADDY

q

 

05
25

PADDY

12S

YiS KisYos
i= 1 , 2
2
x05 E, Kisxis
BROWN
RICE ‘ IT (Yos)

FIGURE 4.7. DIAGRAM OF PADDY SEPARATOR COMPONENT PROCESS

BROWN RICE

26
\/

 

06

FIRST STAGE
MILLED RICE

Yi6 —_—_ Ki6Y06
DARK -
= 2
BRAN ' L
2
X05 2 £1 KiG ' I? (Yoe)
m = 1, 2 6

FIGURE 4.8 DIAGRAM OF FIRST STAGE WHITENER COMPONENT PROCESS

126

 

FIRST
MILLED RICE
(P
27
‘1’
vi? = Ki7YO7
MEDIUM ' 2
BRAN 17 x07=1221kflxnd91 (YO?)
‘1’ 07 I = 1. 2
a m = 1, 2. . . .6
SECOND STAGE
MILLED RICE

FIGURE 4.9. DIAGRAM OF SECOND STAGE WHITENER COMPONENT PROCESS

SECOND STAGE Vie z Kfives
MILLED RICE
X08 2 '1 2, KIBYIB ' It's“ (Yog)
I I = 1, 2
; 28
‘i’ m ——~ 1, 2, 5
i
18 08 THIRD
FINE STAGE
BRAN MILLED
RICE

FIGURE 4.10 DIAGRAM OF THIRD STAGE WHITENER COMPONENT PROCESS

127

rice and the output is graded milled rice. The by-products

are brewer's rice and polish. The flow model equation is:

Yig = KigYog i = 1, 2, 3

‘where Yi9 is the quantity of material "i9" required or
taken out to produce one unit of graded milled rice.

Figure 4.11 shows the component and the related equations.

4.2.1.7 Transport Component

The transport components are the laborers,
elevators, chutes and conveyor in the system. Since the
same material flows into and out of a transport component
and it is assumed that no losses are incurred: only
transport energy costs need be considered. Figure 4.13
shows the location of the transport components. The cost
models for the transportation components are:

Elevator 1:

XmlT = -fm1T(YlT) m = 1' 2' .0006

where fm1T(Y1T) is read as the function of YlT and

represents the transport energy per unit of material.

Elevator 2:

xsz = -fm2T (YZT) m = 1' 2' .0006
Elevator 3:
Xm3T = 'fm3T (Y3T) m = 1, 2, ....6

128

 

BREWERS
Yi9 = KIQYOQ
1 9 i=1, 2, 3
39 29 3
MILLED _ ..
> k a POLISH x09 “_ Z“KISYIQ' ‘3‘ (Yogi
RICE |: 1

09

GRADED MILLED RICE

FIGURE 4.11. DIAGRAM OF GRADER COMPONENT PROCESS

BROWN RICE PADDY
AND HULLS
2O 22
0
Yzo = Y22 = Y25Y05

x22 2 'Kzoxzo + f22 (Y22)

FIGURE 4.12 DIAGRAM OF RETURN HULLER COMPONENT PROCESS

9
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130

Elevator 4:

Xm4T = 'fm4T (Y4T) m = 1, 2, ....6
Elevator 5:

meT = -fm5T (YST) m = 1' 2' .0006
Elevator 6:

xm6T = -fmsm (YGT) m = 1, 2, ....6
Elevator 7:

xm7T = -fm7T (Y7T) m = 1, 2, ....6
Elevator 9:

xm9T = -fm9T (YgT) m = 1, 2, ....6

4.2.1.8 Return Huller Component

The return huller component performs the hulling of
paddy coming from the paddy separators. Figure 4012 shows
the paddy as input and the mixture of brown rice and hulls
as output. This component is needed because the efficiency
of the primary hullers is only from 60 to 95 percent. The
unhulled S to 40 percent paddy are dehulled in the return
huller. The related equations are also shown in the

figure.

4.2.1.9 Rice Milling System Economic Model

The whole system is shown in Figure 4.14. The flow

Ylo (milled rice) is the system stimulus variable. The

131

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132

following material flow relations are from Figures 4.4

through 4.12:

Y11 = K11Y01

Y21 K21Y01
Y31 = K31Y01

K41Y01

*4
.5
g...-

ll

Y13 = K13Y03
Y23 = K23Y03

K14Y04

*4
5.:
Q

II

K15Y05

|-<
5.:
U1

ll

Y16 = K16Y06

K17Y07

m

H

\1
ll

Y18 = K18Y08

K19Y09

K:
g...
\D

II

K29Y09

m

n:

‘o
I!

In order to complete the rice milling economic

model of the system, the energy cost equations must be

133

derived. An energy cost, Xi, per unit of material is
defined for each material flow. The energy costs are
defined analogously to such intensive physical variables as
voltage or velocity (ium, they are relative measures like
elevation, electrical potential and gravity). Energy cost
is measured at one point in the system relative to another.
A material flow rate is associated with each of the
oriented line segments such as 1, 1T and 11 segments
(called edges). The vertices such as A in Figure 4.14
(edge endpoints) constitute the set of points at which
energy cost evaluations are made for the linear graph in
Figure 4.14. The decomposition of variables into stimulus
and response subsets are made utilizing the notion of a
tree of the linear graph. A tree of the linear graph is a
subset of edges of the graph of maximal size such that the
edges in the tree form no closed paths or circuits in the
system graph. Those edges in heavy lines constitute a tree
for the system graph of Figure 4.14.

To facilitate computation, certain conventions have

to be adopted.

a. The per unit energy costs of each edge are

defined positive at the tail of the arrow of
that edge with respect to the narrow tip.
Thus x§1 is the energy cost per unit of paddy
at ver ex A minus the energy cost per unit of
paddy at vertex B.

b. All vertices "R" are reference vertices

(analogous to an inertia reference or an
electrical ground). Thus, the energy levels

134

of all materials are defined to be zero at
IR.I

c. Material flows towards a vertex are negative

and flows outward are positive. The component

interconnection pattern is mathematically
described via application of continuity and

compatibility laws to the linear system graph

of Figure 4.14. The continuity law is

analogous to Kirchoff's current law and states

that:

The.snm of flora into a xertsx.inot.includins.rsfsrence
lattices). is zero. Thus we obtain:

Y11 - YlT = 0 A(l)

where Yll is the flow rate of dried paddy into the

precleaner and YlT is the flow rate of paddy through the

elevator "1T."

Y12 - Y2T = 0 A(2)

where le is the flow rate of brown rice, hull and paddy
mixture to the sifter and YZT is the flow rate of brown

rice mixture from the return huller.

Y43 - Y02 - Yzo = 0 A(3)

where Y43 is the flow rate of brown rice, hull and paddy

mixture to the sifter, and Y02 is the flow rate of brown

rice mixture from the return huller.

135

Y22 - Y4T = 0 A(4)

where Y22 is the paddy flow rate to the return huller and

Y“: the flow rate to elevator 4.
Y4T - Y25 = 0 A(S)

where Y25 is the flow rate of paddy from the separator.
Y24 - Y3T = 0 A(G)

where Y24 is the flow rate of brown rice and hull mixture

to the aspirator.

Y15 - Yo4 = 0 A(7)

where YlS is the flow rate of brown rice and paddy to the

separator and Y04 is the flow rate of brown rice and from

the aspirator.

Y26 - Y5T = 0 A(8)

where Y26 is the flow rate of brown rice to the first

whitener and YST is the flow through elevator number 5.
Y27 - Yos = 0 A‘9’

where Y27 is the flow rate of milled rice to second

whitener and Y06 is the flow rate of milled rice from the

first whitener.

136

Y28 - Y7T = 0 A(10)

where Y28 is the flow rate of milled rice to the third

whitener and Y7T is the flow rate of milled rice through

elevator number 7.

Y39 - Yoa = 0 A(ll)
where y39 is the flow rate of milled rice to the grader and

Y03 is the flow rate of milled rice from the third

whitener.

Yog - YBT = 0 A(12)

where Y09 is the flow rate of milled rice from the grader

and Y5”! is the flow rate of milled rice through elevator

number 8.

Y12 - Y01 = 0 A(13)

where Y12 is the flow rate of paddy and Y01 is the flow

rate of paddy from the precleaner.
Y24 - y03 = 0 A(l4)

where Y24 is the flow rate of brown rice and hull mixture
to the aspirator and Y03 is the flow rate of brown rice and

hull mixture from the sifter.

137

where Y27 is the flow rate of milled rice to the second

whitener and YGT is the flow rate of milled rice through

elevator number 4 .

Y28 - Yo7 = 0 A(16)

where Y28 is the flow rate of milled rice to third whitener

and Y07 is the flow rate of milled rice from the second

whitener.

Y25 - Y05 = o A(l7)

where Y26 is the flow rate of brown rice to the first

whitener and Y05 is the flow rate of brown rice from the

separator.

Ylo - Y9T = 0 A(18)

where Ylo is the flow rate of milled rice to the rice mill

outlet and YgT is the flow rate of milled rice through

elevator number 9.

The remaining portion of the graph equation was
developed via application of the law of compatibility which
is an analogy of Kirchoff's Voltage Law. Compatibility

states that:

138

Juu:sumxofficrismdufiiensrschnstsnarQundsasflsuuuisxuuzof
.gdgga‘ganiahesL For the path of edges IT and 11 of Figure
4.13, the following is obtained:

x1 + X1T = x11 = 0

where X1 = unit peso cost of input paddy and

Xij a unit peso cost of material "i” for component
"j“ in Figure 4.14,

recalling that all references to vertices are
defined to be at the same energy level.

X11. and x11 have the same sign because they have

the same orientation in the path from R to R. The complete

set of compatibility relation is:

X1 + xlT + X11 = 0 3(1)
X01 + x2T + x12 = 0 3(2)
x02 + x43 = 0 8(3)
X02 + x43 = 0 3(4)
X03 + XBT + x24 = 0 3(5)
x04 + x25 = 0 3(5)
X25 + X4T = 0 3(7)

X05 + XST + X26 = 0 3(8)

139

X06 + XGT + x27 = 0 3(9)

X07 + X7T + x28 = 0 3(10)
x08 + x39 = 0 B(11)
x09 + XgT = 0 3(12)

It has been proven that the above set of continuity
and compatibility equations constitute the largest possible
set of independent graph equations (Koenig, et al., 1967).
Therefore, all of the available information pertaining to
component interconnection pattern is contained in these
equations. Any system input-output relationship can be
obtained using the appropriate combination of component
model and graph equations. For example, consider the
relationship between paddy and hull in Figure 3.14. The
component models are derived by the method illustrated by

Holtman, et a1. (1972). The flows are:
Y11 = YlT C(1)

where Yij = the flow rate of material i for component j.
Y12 = Y2T C(2)
Y43 = Y02 + 20 0(3)

Y24 = Y3T C(4)

140

Y15 = Y04 C(5)
Y25 = Y4T 0(5)
Y4T a Y22 C(7)
Y12 = Y01 C(8)
Y14 = K14Y04 C(9)
Y11 = K11Y01 C(10)
Y12 = K12Y02 C(11)
Y33 = K43Y03 C(12)
Yzo = K25Yos C(13)
Yls = K15Y05 C(14)
Y24 = K24Y04 C(15)
Y14 = K14Y04 C(15)

In effect, the set of equations above transforms
the entire system model into a component model with sixteen
material forms. Since we want a relationship between paddy
and hull, we begin with equation C(10) which is an

expression of the flow rate of paddy.

Y11 = X11Y01 C(10)

141

Substitution of Equation A(13),

Y11 = x11le

Substitution of Equation C(11),

Y11 = K11K12Y02

Substitution of Equation C(3),

Y11 = K11K12(Y43 - Yzo’

Substitution of C(12) and C(13),

Y11 = K11K12(K43Yo3 - Kstos)

Substitution of C(14),

Y11 = K11K12<K43Y24 - K25(1/K15)Y15)

Substitution of C(15),

Y11 = K11K12<K43K24Y04 - K25(1/K15)Y04

Substitution of C(16),

Y11 = K11K12(K43K24<Y14 ' K25(1/K25)Y14

The flow Y1o (milled rice output) is the stimulus

variable that dictates the remaining flows. The main
interest in this model is the output cost x10 per kilogram

milled rice.

142

To derive an equation for x10, start with the cost

equation for node 18 after the grader component.

x10
X09
x39
X08
x28
X07
x27
x06
X26

X05

- x09 - X9T
K39X39 - K19X19 '
- x03

- K18X18 + K28X28
'X7TX07

- K17X17 + K27X27
- X06 - X6T

- K16X16 + K26Y26
‘ X05 ‘ XST
K25X25 + Klsxls +
- X04

K14x14 + K24X24 +
' x03 ‘ XBT

' K13X13 ' K23Y23

- x02

K29X29 + f09(X09’

+ f08(X03)

+ f07(Yo7)

+ f06(Y06)

fos(Y05)

f04(Y04)

+ x43x43 + fo3(Y03)

E(l)

E(2)

E(3)
E(4)
E(s')
E(6)
E(7)
E(8)
E(9)
E(lO)
E(ll)
E(12)
E(13)
E(14)

E(lS)

143
X02 = K12X12 + f02(Y02) 3‘15)
x12 = - X01 - X2T 3‘17)

X01 = Kllxll - K21Y21 + K31X31 + K41x41
+ f01(Y01) E(18)

X11 = — xlT — x1 E(lg)

Combining the above equations to eliminate all

internal x values, we could arrive at an expression for x10

in terms of the x values of the input and output, i.e., x1,

x2, x31, x41, x13, x23, etc. Also all the flows could be

expressed in terms 0f Y10- These procedures (see Appendix

F) would give us an expression for output cost X10 in terms

of all input and output cost.

x10 = K43K39K28K27K26K24K15K12f01K39K28K27
K26K12(K43K24K15 ' K25>Y10

+ K43K39K29K27K26K24K15fo2K39
K28K27K26(K43K24K15 ' K25>Y10

+ K39K28K27K26K24K15f03(K39K28K27K26
K24K15Y10)

+ K39K28K27K26K15fo4<K39K28K27K26K25Y10)

+ K39K28K27K26f05(K39K28K27K26Y10)

144

K39K28K27f06(K39K28K27Y10)
K39K28f07<K39K28Y10)
K39f03(K39Y10)

f09(Y10)

K43K39K28K27K26K24K15K12K11f1TK43K39
K28K27K26K24K12K11(K43K24K15 - K25)
Y1o

K43K34K28K27K26K24K15f2TK39K28K27K26K12
(K43K24K15 - K25)Y10

K39K28K27K26K24K15f3T(K39K28K27K26
K24K15Y10)

K39K28K27K26f5T(K39K28K27Y10)
K39K28K27f6T(K39K28K27Y10)
K39K28f7T(K39K28Y1o)

f9T(Y10)
K43K39K28K27K26K24K15K11X
K43K39K28K27K26K24K15K21XSA

K43K39K28K27K26K24K15K31XST

145

K43K39K28K27K26K24K15K12K41XCH
K39K28K27K26K24K15K13XBR0
K39K28K27K26K24K15K23Xcs
K39K28K27K26K15K14XHU
K39K27K16XDB

K39K28K17XMB

K39K18XFB

K19XBRE

K29XPo
K39K28K25f4T(K31K28K27K26K25Y1o)
K39K28K25f22(K39K28K27K26K25Y10)

K39K28K25K12K11f1TK43K39K28K27K26K12
K11(K43K24K15 - K25>Y10

K39K28K25K12f01(K43K39K28K27K26K24K15
K12Y10 - K39K28K27K26K25K12Y10)

K39K28K25K20K12K11X1
K39K28K25K20K12K21X3A

K39K28K25K20K12K41XCH

146

‘ K39K28K25K12f2T(K43K39K28K27K26K24K15K12
Ylo)

‘ K39K25f02<K43K39K28K27K26K24K15Y10)

The first nine terms are the processing cost, the
tenth to sixteenth terms are transport cost, the
seventeenth term is the cost of the paddy input x1: the
eighteenth to twenty-eighth terms are the by-product costs
and the rest are the different costs in the return paddy
loop.

Using the available data (Appendix G) and the
relationship on Figures 3.18, 3.22, 3.24, 3.26, 3.30, 3.35
and 3.37 under Philippine conditions, the above equations

were simplified (Appendix B) into the following equations:

X10 = 0.0000048541 210-0-14 + 0.684834 1410-0-54

(cone) + 0.0485612 210‘0-07 + 0.048863 Y100'36

+ 2.922862 3100.29 + 0.27699 Ylo'l

4-0.466497 Y10'0°08-+0.00071381‘Y10'+]u1130

x10 = 0.0000048454 210‘0-14 + 1.8958 y100o41

(RR) + 0.0485612 Y10'0-07 + 0.048863 Y10°°35

+ 2.922862 210-0.29 + 0.27699 210-1

+ 0.466497 Y10'0-03 + 0.00071381 Ylo + 1.1062

These equations were used in determining the cost

of input and processing energy per kilogram of milled rice.

147

The system analysis of a rice mill based on linear
graph theory has been completed. The methodology begins
with a decomposition of the system into components. The
mathematical structure of the component models emphasized
their mass-energy exchange relationship. The following
chapter will cover an application of the two equations

developed in this chapter to three sizes of rice mills.

CHAPTER V

ANALYSIS OF THE RESULTS

5.1 Rice Mill Performance Model

The rice mill performance model was used to
simulate the operating characteristics of rice mill types
currently being manufactured in the Philippines. These
locally manufactured rice mills need to be given primary
attention in upgrading the quality of rice milling systems
in the Philippines. 01d or existing mills (mostly imports)
that were custom built or do not fall into any type studied
may be considered on an individual basis.

There were about twelve "standardfl mill types
produced by two leading manufacturers. The simulation
model also included eight models pr0posed by the author. A
total of twenty mill types were therefore simulated from
the base data of three rice mills. A brief description of
the "standard" mills is presented in Table 5.1. The mill
types designated by individual numbers consist of l huller
and 1 whitener. Those with an A or F (Felson models)
designation are equipped with 2 whiteners and those with a
B or A (Felson models) designation are equipped with 2

hullers. The second column from the left side presents the

148

149

. 3333...

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150

input capacity of the mill in tons per hour, while the
third column gives the number of hullers and the diameter
in meters. In the fourth column is the number of whiteners
and the diameter in meters. The fifth column presents the
number of compartments in the millls separator, and in the
sixth column is the power requirement of the mill in both
brake horsepower and kilowatts.

Each simulation included 100 milling runs for each
mill type without any modification, modified with an
adjustable separator and modified with a single huller for
the mills over 0.916 ton-per-hour capacity. Each milling
run is different as determined by three random variable
generators built into the program and discussed in Chapter
3. A "milling run" is one complete milling operation of a
"batch" of paddy with resulting time data, output, etc.
Some typical simulation runs are shown in Tables 5.2, 5.6,
5.7, 5.8, 5.9, 5.10 and 5.11. The fixed input data which
were used for all simulations were as follows:

a. Huller Bin Capacity (HBIN) = 0.0069 m3

b. Sifter Bin Capacity (SFBIN) = 0.077 m3

c. Paddy Separator Bin Capacity (PBIN)

= 0.0089 m3
d. Input Paddy Mass (I) = 10.0 tons
e. Grain Shape (GS) = 3.0

f. Number of Paddy Cleaners (CPN) = 1

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152

Table 5.3 Rice milling rates/of small disc-cone mill by region in the
Philippines, 1973-

 

 

 

 

. No. of No. of All Paddy Rice Recovered
Region 22:2:23 Trials iiiiggam Quantity Percent
Kilogram
Ilocos 7 9 378.70 218.30 57.64
Cagayan valley 19 24 7890.66 4929.49 62.47
Central Luzon 128 223 99207.78 62695.24 63.20
Southern Tagalog 86 135 57813.16 36850.72 63.74
Bicol Region 37 66 83838.30 54042.30 64.46
Western Visayas 24 31 51078.95 32705.50 64.03
Central Visayas 24 24 1079.53 718.95 66.60
Eastern Visayas 20 30 6720.40 4490.40 66.82
Western Mindanao 4 4 1123.48 748.61 66.63
Northern Mindanao 13 15 12805.20 7470.58 58.34
Southern Mindanao 6 6 7669.80 4613.00 60.15
PHILIPPINES 368 567 329605.96 209483.09 63.55

 

l-/From Basilio, E.A. and J.C. Alix, 1975. Rice Milling Recovery Rates,

1973. Ag. Econ., Statistics and Market News Digest, Vol. IX, No. 3

2/

Table 5.4 Engine sizes and capacities of disc-cone rice mills-

 

 

Capacity Engine Size Average4Price Investment Costg/

Gavan/12 hrs. tons/hr. BHP Pesos- Pesos/cavan cap.
50- 100 0.183-0.366 15- 24 16799.00 376.37

150- 250 0.550-0.915 25- 3O 36417.00 286.08

250- 350 O.9lS-l.281 32- 45 47481.00 260.65

350- 450 l.28l-1.647 45- 55 56936.00 219.13

450- 700 l.647-2.562 60— 80 83601.00 193.14
700-1000 2.562-3.66O 90-140 146027.00 204.10

 

2-/From Duff, B. and I. Estioko, 1972. Establishing Design Criteria for

Improved Rice Milling Technologies, Saturday Seminar (Aug. 26, 1972)
3/

-Taken at midpoint of capacity range.

2/One dollar was equivalent to F7.47.

Includes cost of engines.

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160

Grain Type (GT) = Medium

Whitener Bin Capacity (WBIN) = 0.009 m3

The variable input data were:

a.
b.
c.

6.

Number of Paddy Hullers (HNP = 1 or 2)
Huller Type (HT): Disc or Rubber Roller
Whitener Type (WT): Cone or cylindrical

Growing Season (SEAS): Wet or Dry
Drying Method (DM): Sun or Mechanical

Rice Mill Capacity (RCAP) = 0.33 to 1.83 tons
per hour

Paddy Cleaner Capacity (PCAP) = 0.33 to 1.83
tons per hour

Number of Whiteners (NW) = 1 or 2

Separator Capacity (SCAP) = 0.495 to 1.98 tons
per hour

Whitener Capacity (WCAP) = 0.255 to 0.731 tons
per hour

Paddy Huller Capacity (PHCAP) = 0.294 to 1.83
tons per hour

Separator Type (ST): Fixed or Adjustable

Table 5.2 is the simulation for mills type No. 1

with a 0.33 ton-per-hour capacity. It shows the time to

mill 10 tons of paddy, the milled rice output, the recovery

percentage, head grain percentage, discolored kernels and

energy consumed in the milling process. Each row

represents one simulation run which in this case involves

the milling of a 10-ton batch. The result of each run

161

varies due to the random variables built into the program.
These variables, namely, moisture content, purity and
drying delay, were discussed previously in Section 3.2.1.
The first average (AVElO) refers to the average of the
first ten simulations and AVElOO refers to the average of
100 simulations. For the 0.33 ton-per-hour mill, it takes
an average of 30.76 hours to mill 10 tons of paddy.

For a medium grain and grain shape of 3.0, wet
season crop and sun drying, the average yield or recovery
was 6.53 tons. The head grain percentage was 61.94 percent
and the discolored kernel percentage was 1.52. The total
energy used was 498.21 kw-hrs. or 49.82 kw-hrs. per ton.
The results of the simulation for a Type 1 mill were
validated by comparing results with actual data collected
in the field. Table 5.3 shows the milling recovery of
small disc-cone mills by region in the Philippines
(Basilio, et al., 1973). The probability distribution of
moisture content, purity and drying delay variables used in
the simulation were typical of the Bicol region. Table 5.3
lists the milling recovery from the Bicol region as 64~46
percent. The simulation milling recovery was 65.3 percent
based on 6.53 tons milled rice from a paddy input of 10
tons. There were no extensive surveys on head grain
percentage. Table 5.5 from Khan, et a1. (1973) shows head

grain percentage as affected by harvest date, drying method

162

and variety. The mean head grain percentage was 62.46
percent from the field data. The simulation result showed
a head grain percentage of 61.94. For comparison of the
discolored kernel percentage of the simulation, 64 samples
from the work of Camacho, et a1. (1978) were analyzed.
Camachofls discolored kernel percentage was 1.92 percent
with a standard deviation of 2.6. The simulation showed a
mean of 1.52 percent. For energy use comparison, Table 5.4
from Duff, et al. (1972) presents engine sizes and
capacities of different mills. Duff's calculated energy
used for a 0.366 ton-per-hour mill for an input of 10 tons
and 30.3 hrs. of operation was 537.54 kw-hrs. The
simulation had a value of 498.21 kw-hrs. The simulation
value was lower because it measured a continuous operation
while actual data included the additional energy required
for starting and clogging conditions.

Table 5.6 presents the results of a simulation for
which the fixed paddy separator was replaced with an
adjustable type. Simulation energy consumption dropped to
455.48 kw-hrs. for ten tons. This is a reduction of 42.81
kw-hrs.:for ten tons which, when projected, amounts to
yearly savings 0f P 14153-181/ or roughly the cost of 1 ton
of milled rice. Table 5.7 presents simulation for a dry
growing season and a mechanical dryer. The head grain

increased by 17.5 percent over that for a wet season shown

163

on Table 5.6. This improvement was expected since there
was generally a lower relative humidity and no
precipitation during the dry season. Dry season conditions
reduced the likelihood of grain moisture reabsorption
during the harvesting period. Also the use of a mechanical
dryer with controlled drying temperature assured lower
grain breakage. Table 5.8 shows the simulation results for
a rubber-roll huller instead of a disc huller. The head
grain increased by 4.75 percent and the energy consumption
decreased by 2.86 kw-hrs. compared to the disc-huller
equipped mill. Head grain in rubber-roll milled rice was
an average of 7.68 percent higher than for milled rice
processed by disc hullers. Lower rubber-roll huller energy
consumption may be attributed to the higher hulling
efficiencyZ/ of 82:7 percent as compared to 56.5 percent
for the disc huller.

Appendix Table IS presents the simulation results
of two whiteners in series for the 0.33 ton-per-hour rice
mill (type 1). There was an increase of 0.15 percent in
head rice reovery and 2.93 percent in total recovery with a
corresponding 12.22 kw-hr. increase in energy consumption

over the use of one whitener. Appendix Table I6 presents

l/Based on 64 percent utilization of 288 hrs. per month and
computed as: 4.27 kw-hrs. X 0.33 tons/hr. x 0.64 (utiliz.
rate) x 12 mos/yr X $0.05 kw-hr = $155.84 or Pl,153.18.

2/This term was defined in Section 2.1.

164

the results after replacing the fixed-stroke paddy
separator with an adjustable-stroke paddy separator. The
energy consumption dropped 42.47 kw-hr. Appendix Table IS
presents the simulation results of two whiteners in series
for the 0.33 ton-per-hour rice mill (type 1). There was an
increase of 0.15 percent in head rice recovery and 2.93
percent in total recovery with a corresponding 12.22 kw-hr.
increase in energy consumption over the use of one
whitener. Appendix Table I6 presents the results after
replacing the fixed-stroke paddy separator with an
adjustable-stroke paddy separator. The energy consumption
dropped 42.47 kw-hr. Appendix Table I7 presents the
simulation results for rice mill type 2 rated at 0.44 tons
per hour. Table 18 indicates a reduction of 35.51 kw-hr.
for the adjustable paddy separator. Table 110 shows a
reduction of 39¢2 kw-hr. when the separator was replaced
with an adjustable paddy separator for a 0.55 ton-per-hour
mill (Type 2A). Table Ill shows an increase in total and
head rice recoveries of 2.34 and 4.95 percent respectively
for the rubber-roll huller over the disc huller. Table 112
presents the results of the simulation of a type 3 rice
mill which has a capacity of 0.66 tons per hour. Table I13
shows the results from the same mill with an adjustable
separator and Table 114 gives the results from that same

mill equipped with two whiteners in series. Table 5.12

.1655

 

 

 

 

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00.500 50.50 00.05 00.000 00.00 00.05 00.000 00.00 00.05 05.000 50.00 00.05 000 000
00.050 00.00 00.05 00.000 00.00 00.05 05.500 00.00 00.05 00.000 00.00 00.05 05.500 00.00 00.05 000 000
00.050 00.00 05.05 00.050 0.00 00.05 00.000 05.00 00.05 00.000 00.00 50.50 00.000 00.00 00.50 0 0
00.000 00.00 00.05 00.000 00.00 00.05 00.500 00.00 00.05 00.000 00.00 00.50 00.500 00.00 00.05 00 40
00.000 00.00 00.05 00.000 0.00 00.05 00.500 00.00 00.05 00.000 00.00 00.50 00.500 00.00 00.50 0 0
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166

smmmarizes the results of simulation from Appendix Tables
IS to I40. Table 5.10 shows the results for the 1.83 ton-
per-hour mill after substituting a rubber-roll huller and a
cylinder whitener for the disc huller and cone whitener,
respectively. The energy consumption was reduced by 1.6
kw-hrs. and the head grain went up by 4.87 percent compared
to the disc-cone combination. Table 5.11 presents
simulation results when double hullers were replaced by a
single huller. The energy consumption was 218.85 kw-hrs.
higher than for a 1.83 ton-per-hour mill with double
hullers. This result indicates that there was a limit on
increasing the size of the huller. This finding was
utilized in the determination of the proposed mills
presented in Figure 5.1. The simulation runs of mill sizes
from 0.367 to 1.1 tons per hour indicated that the energy
consumption was lowest with a single huller. For the
larger mills from 1.54 to 1.83 tons-per-hour capacity, the
energy consumption was lowest with double hullers. Other
simulation results are shown in Tables I5 to I42 in
Appendix I.

Figure 5.1 shows the simulation of the different
mills including eight mills proposed by the authon. The
solid circles indicate the energy consumption of the
different mill types studied (Table 5.1%. The most popular

mill in the Philippines had a 0.916 ton-per-hour capacity

167

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168

(Type 4), and was available in three versions with a wide
variation of energy consumption, from 25.1 to 44.7 kw-hrs.
per ton. The basic type with a single huller and a single
whitener (type 4) had the lowest energy consumption.
However, the rice quality performance was the poorest, as
shown in Table 117 in Appendix I. Both total recovery and
head rice percentage suffered. This was confirmed by both
the simulation and previous experimental results of
Manalabe, et al. (1978). In terms of output rice quality,
there was really no difference between the version with
double hullers (type 4A) and the version with double
hullers and whiteners (type 4A8). But in terms of energy
consumption, there was a difference of 14.7 kw-hrs. per ton
between the two mill types. The apparent advantage of the
double-huller equipped mill (type 4A) was the extra huller
which would allow the mill to continue operation in case
one huller broke down. With the rising cost of energy in
the Philippines, which is dependent on foreign sources for
90 percent of its needs, a trade-off of this magnitude (8%
difference) is worth considering. Higher capacity (types
7A8 and 8AB) gave the lowest energy consumption among the
different types.

To determine which combination and number of
components are most desirable, various modifications of

existing mill types were considered. One modification that

169

lowered energy consumption was the replacement of the
fixed-stroke separator with an adjustable type. Another
modification was replacement of double hullers with a
single large huller. The single huller reduced energy
requirements up to a capacity of 1.1 tons per hour. These
two modifications simulated reductions in energy
consumption of 6 percent for the 0.367 ton-per-hour mill
(type 1); 9 percent for the 0.550 and 0.696 ton-per-hour
mills (type 2A and 3A); 10 percent for the 0.916 ton-per-
hour mill (type 4A); 19 percent for the 1.1 ton-per-hour
mill (type SAB); and 13 percent for the 1.36 ton-per-hour
mill (type GABL. For larger mills with capacities of from
1.54 to 1.83 tons per hour, the only modification possible
was replacement of the fixed-stroke separator with an
adjustable stroke. This modification reduced the energy
consumption of the larger 1.54 and 1.83 tons-per-hour mills
by 6 percent (type 7A8) and 5 percent (type 8AB).
Validation of the model was conducted next. The
capacity and energy consumption were compared with
currently manufactured mills in Figure 5.2. The energy
required to run the power sources for these mills were then
computed by:
Energy(kw-hr./ton) = ' 0 S 0 W
Efficiency X power factor X mill

capacity (ton/hr)

The computed values obtained from the above formula were

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171

also plotted in Figure 5.2. There was a varying difference
of about 10 kw between the computed and the simulated data.
However, the values for the computed and simulated data
were in general parallel. The difference may be attributed
to the margin of safety provided by the manufacturer for
recommended sizes of prime movers. The simulation data
were based on continuous operation of the electric motors
which would be somewhat lower than the required power unit
ratings. Actual power unit ratings should be higher to
easily handle the higher starting torque and clogging

conditions for interrupted actual operations.

5.2 Linearflsfmrkficonmisflodel

An economic model was applied to six rice milling
systems, iJL, three conventional mills with 1/3, 1/2 and l
ton-per-hour capacity and with three rubber-roll equipped
mills with the same capacities. Economic data were
available for these mill sizes which represent a common
classification of small, medium and large mills in the
field. The mills simulated by the rice mill performance
model included all mills in production to evaluate

undesirable as well as good features. The economic

 

 

l/Efficiency of the electric motor in converting electric
energy into mechanical energy.

2/Power factor of the electric motor is the cosine function
of the phase angle or the ratio of resistance to impedance.

172

simulation results are shown in Table 5.13. Tables 61 to
G3 in Appendix G present the data used in the analysis.
Simulations were made at 50 and 100 percent utilization
ratel/ for comparison purposes.

The simulation results showed the rubber roll
milling cost to be higher by 2 to 3 centavos per kilogram
HL025%) based on an asumption that the milling recoveries
were equal. TestsZ/ of commercial mills under field
conditions showed that the milling recoveries from
conventional mills and rubber-roll mills were not
significantly different. On the other hand, when mills
were carefully serviced, adjusted and operated under the
close supervision of well trained technicians, the recovery
by rubber—roll mills was observed to be higher by 2 to 3
percent over that of conventional millsrl/ To determine
the effect of this difference on milling costs, a
sensitivity test was conducted. The linear network
economic model was used to simulate the milling cost with a
2 to 3 percent rubber roll higher recovery at each 100 and

50 percent level of utilization. The simulation results

 

(Table 5.14) under controlled conditions and a 2 to 3%

l/Full capacity or 100 percent utilization is 12 hours per
day for 24 days a month or 288 hours per month.

2/See Andales, et al. (1978).
3/See Manalabe, et al. (1978).

173

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174

 

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175

higher recovery showed that rubber-roll mills produced rice
at a higher cost of about 0.3 to 1.4 centavos. The
differences were greater for the 50 percent utilization
level. Since the result in Table 5.13 simulates commercial
milling conditions, the higher charge by rubber-roll mill
operators seems justifiablerl/ In actual practice, rubber-
roll mill operators charged 2.22 centavos per kilogram
(0.02%) higher than other mill types. The differential
cost was at the mill level and not at the consumer rice
market level. Market prices do not distinguish between
rubber-roll and conventional milled rice but may vary due
to variety and/or storage duration.

A sensitivity test was made on the effect of
rubber-roll costs on the maintenance and total costs of
milling (Table 5.15). Prices of locally produced rubber
rolls in 1978 and imported rolls in 1976 were used (see
Appendix Q). The government stOpped the importation of
rubber rolls when locally made rolls became available. The
1978 price increase of 10 to 85 percent over 1976 prices
increased the total cost of milling per kilogram of milled
rice by 1.0 to 2.0 centavos (0.02%).

The change of status of the Philippines from a

rice-importing to a rice-exporting nation.in 1979 made it

l/See Table P5 in Appendix P, which was adopted from
Camacho, et al., 1978.

176

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177

necessary to price rice with respect to its grade
classification to cover the extra cost of improved
processing and to stimulate quality control.

The paddy and rice grade classifications in the
Philippines are shown in Appendix N. There were five
grades with ten criteria required for paddy. For milled
rice, there were four grades with nine criteria. The
higher the grade classification of paddy, the lesser was
the undesirable component and impurity content. It
therefore follows that the milling yield was higher from
the higher grades. .A lower milling yield reduces the
output and increases the total unit cost of the milled rice
with rubber-roll mills (Tables 5.13 and 5.14). The milling
cost of the roll was lower than for conventional mills when
there was a 3 percent higher milling recovery of the
rubber-roll mill. At 100 percent utilization, the rubber-
roll-milled rice cost more by 1.1 to 2.8 centavos as
compared to the conventional milled rice.

Another undesirable aspect of milling low-grade
paddy is the additional sorting processes required to bring
it up to a higher grade of milled rice. These processes
may involve the use of indented graders (trieurs) and color
sorters to eliminate broken and discolored kernels. This
means additional cost to the miller in terms of time,

energy and equipment.

178

A prorated price schedule was recommended to
encourage improved quality of paddy and milled rice. The
prorated price schedule discounted the selling price of
rough rice in accordance with its grade. Full price was
paid for the highest grade rice while a corresponding
gradually increasing discount schedule was applied to the
lower grades. This provided an incentive to invest in the
extra cost of processing poor-quality paddy and helped ease
the problems encountered in marketing surplus rice in other
countries.

Chapter Five analyzed the effect of milling
recovery on the cost of milling rice by using rubber-roll
and conventional mills under simulated and field
conditions. The finding was that under ideal conditions
the rubber roll has a lower milling cost. However, under
actual conditions, the conventional mill has a lower cost.
Sensitivity tests on the increased cost of rubber rolls
with frequent replacement were also conducted. An increase
in the price of rubber rolls from 10 to 85 percent over a
2-year period (1976-78) increased the processing cost by l
to 2 centavos per kilogram. Another sensitivity test was
made on the time utilization rate for mills. A 50% as
compared to 100% utilization rate caused the cost of
rubber-roll milling to increase from 0.8 to 4 centavos per

kilogram over conventional millling.

CHAPTER VI

CONCLUSIONS AND RECOMMENDATIONS

The two models developed in this research can be of
considerable use to rice mill designers and government
policy planners. The models make it possible for the first
time to simulate the operation of mills with different
capacities and combinations of components without the
expensive monitoring of mills in the field. Further, the
model will simulate mill sizes not available in the field.
The model can simulate various proposed levels of
performance and different mill sizes to provide valuable
data for policy makers. Mill designers can manipulate the
model with different combinations of equipment to predict
the most desirable characteristics, without the expense of
actually building prototypes.

The specific accomplishments of this research were:

1. The Rice Mill Performance Model was developed
to simulate the operation of selected milling technologies.
It will predict the milling time, rice output, percent
recovery, head grain percentage, discolored kernel
percentage and energy consumption. The assumptions used in

the model were as follows:

179

180

a) Drying delay was defined as the number of
days from harvesting to start of drying of paddy.

b) The drying delay was the same for sun-
dried and mechanically-dried paddy.

c) Variation of hardness of grains due to
varietal differences was not considered.

d) The effect of variation of relative
humidity was not included because relative humidity varied
only seasonally.

2. The Linear Network Economic Model of rice
milling systems was developed for predicting milling costs
over a range of capacities for a selected set of rice-
milling technologies. Limitations of the final form of
this model were as follows:

a) The polisher or refiner by-product which
is produced in small amounts with a value of P055 PE?r
kilogram was excluded due to lack of data.

I» The model is applicable to modified mills
of from 0.367 to 1.36 tons per hour capacity.

3. Simulation runs were conducted to demonstrate
the use of the models in decision— and policy-making
processes and also to demonstrate their validity as far as
comparisons of technologies and sizes are concerned.

a) Rice Mill Performance Model Simulation

Results:

181

l) The energy consumption of Philippine-
manufactured rice mills can be reduced from 4.9 to 8.6
percent by incorporation of adjustable-stroke paddy
separators. The initial higher cost of these units would
therefore be offset by the energy savings.

2) The energy consumption of mediumrsize
rice mills (1.1 to 1.4 tons per hour) can be reduced by
10.0 to 13.0 percent by replacing the double hullers with a
single huller of twice the capacity.

3) The milling recovery and head grain
percentage of the smaller size mills (0.367 to 0.916 tons
per hour) can be increased by 0.5 and 1.6 percent
respectively, by inclusion of two or even three whiteners
in series with an increase of about 4~0 percent in energy
consumption.

4) The per-ton energy consumption for
modified mills (0.7 to 1.8 tons per hour) is not changed
appreciably (26.6 to 27.2 kw-hr. per ton).

b) Linear Network Economic Model

1) The rice processed by rubber-roll
mills costs more than rice processed by conventional mills
under commercial milling conditions by 2 to 3 (1.7 to 2.3

percent) centavos per kilogram.

182

2) The lowest simulated milling cost was
attained at 100 percent level of utilization for the 1.0
ton-per-hour mill.

3) The maintenance cost of the 0.3 and
(L5 ton-per-hour rubber-roll mills was double that for
conventional mills.

4) The maintenance cost of 1.0 ton-per-
hour rubber-roll mills compares favorably (0.3 centavo
difference per kilogram) with conventional mills of the
same size.

5) The fixed and energy costs for both
conventional and rubber-roll mills were approximately the
same (0.4 centavo difference per kilogram).

The recommendations from this research are:
1. Additional field research should be conducted
as follows:

a) Duration of drying delays and losses
experienced in the field and at what moisture content do
these occur.

b) Actual harvesting dates practiced by
farmers based on days after initial heading and moisture
content.

c) Effect of relative humidity levels on rice

head yield of Philippine varieties.

183

2. Update the Rice Mill Performance Model with
current information.

3. The hardness of different varieties grown in
the Philippines should be studied.

4. Conduct a study with the National Grains
Authority in identifying solutions to current problems in
the export of excess rice and their relationship to the
improvement of rice-processing facilities. A possible
approach is to identify areas where there is an excess of
rice for local consumption. Improvement in the rice
quality could then be implemented in these areas through
the use of rice dryers and improved rice mills.

5. Coordinate with the Rice Mill Manufacturers
Association through the NGA on the implementation of the
results of this research in the manufacture of future

energy-efficient rice mills.

BIBLIOGRAPHY

BIBLIOGRAPHY

l. Andales, S. C., C. T. Dilag, V. G. Gayanilo and I. R. Camacho, 1976.
Comparative Performance Test of Rice Mills UsinggRubber Roll and Stone

Disc Hullers. Agricultural Engineering Department, University of the

 

Philippines at Los Banos, Los Banos, Laguna, Philippines.

2. Araullo, E. V., D. B. de Padua and M. Graham, 1976. Rice Post-harvest

 

Technology. International Development Research Center, Ottawa, Canada.

3. Bagtas, R. V. and V. C. Lizardo, 1970. An Economic Analysis of the

 

Rice Milling Problem in the Philippines. Research Paper for the 5th

 

Session of the Wisconsin-UP Training Program in Development Economics,

Quezon City, Philippines.

4. Basilio, E. A. and J. C. Alix, 1975. Rice Milling Recovery Rates, 1973.

 

Ag. Econ., Statistics and Market News Digest. Vol. IX, No. 3
S. Camacho, I. R., P. Hidalgo, B. Duff and E. Lozada, 1978. A Cogpari-

son of Alternative Rice Milling_§ystems in the Bicol Region. AGPET,

 

INSAET, University of the Philippines at Los Banos,- Philippines.

6. Duff, B. and I. nstioko, 1972. Establishing Design Criteria for

 

Improved Rice Milling Technologies. Saturday Seminar (Aug. 26, 1972)

7. tder, I. W. t. and Goslings, 1965. Mechanical System Design. Pergamon

 

Press, 1nc., N. Y., U. S. A. pp. 10-21.

8. triyatno, 1979. System Modelling on Rice Milling Technolggy in Indone-

 

sia. Unpublished Ph. D. Dissertation, Michigan State University, E. Lan-
sing, Micnigan, U. S. A.

9. Esmay, M. L., Soemangat, Eriyatno and A. Phipps, 1979. Rice Post

184

10.

11.

12.

13.

14.

lb.

16.

17.

18.

185
Production Technology in the Tropics. East-West Center, Hon01u1u,

 

U. S. A.

Getubig, I. R., 1977. Sources of Inefficiencies in the Rice Milligg

 

Industry in Bicol, Philippines. East-West Center Ford Institute,
Honolulu, U. S. A.

Hall, C. M., 1963. ProcessinggEquipment for Agricultural Products.

 

AVI Publishing Co. lnc., Wesport, U. S. A.

Hillier, F. S. and G. J. Lieberman, 1974. Operation Researcn. Holden

 

Day Inc., San FranCisco, California, U. S. A.

Holtman, J. 8., et al., 1972,Graph Theory Applied to Systems Analysis

 

Problems. American Society of Agricultural Engineers Paper No.

72—501, St. Joseph, Michigan, U. S. A.

Khush, G. S., C.M. Paule and de la Cruz, N. M., 1978. Rice Grain

 

Quality and Evaluation and Imrovement at IRRI. workshop on Chemical

 

Aspects of Grain Quality, Oct. 23-25, IRRI, Los Banos, Philippines.

Koenig, H.E. and R. L. Tummala, SML—Z. Principles of Ecosystem

 

Design and Management. IEEE Transactions on Systems, Man and Cy:

 

bernetics, pp. 449-459.

Kono, A., 1973. Lectures on Rice Milling, Grain Processing Training

 

Course, Satake Engineering Co. Ltd., Hiroshima, Japan.

Manalabe, R. R., D. B. de Padua and E. P. Lozada, 1978. Milligg Para-

 

meters for Maximum MillingYield and Quality of Milled Rice. AGPbT,
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Philippines.

Manetsch, T. and G. Park., 1977. System Analysis and Simulation with

 

Application to Economic and Social Systems. Part lI. Department of

 

Electrical Engineering and System Science, Michigan State university,

19.

20.

21.

22.

23.

24.

25.

26.

186
East LanSing, Michigan, U. S. A.

Mears, Leon, et al., 1974. Rice Economy of the Philippines.

 

university of the Philippines Press, Diliman, Quezon City, Phi-
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Mellor, J. M., 19/0. Elements or Food Marketing Policy for Low

 

Income Countries. Foreign Ag. Econ. Report No. 96, USDA.

 

Nangju, D. and S. K. Deuatta, 1970. Effect of Time of Harvest

 

and Nitrogen Level on Yield and Grain Breakage in Transplanted

Rice. Agronomy Journal Vol. 62 July-August.

Ongkingco, P. S. Gatera, M. E. and P. G. Castro, 1964. The
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Ag. Eng'g. Aspects or Rice Production, Storage and ProceSSing,
March.

Paras, A. S., 1976. Rice Milling PrinCiples and Systems. ACPET,

 

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Philippines.

\

Paras, A. S., 1978. Comparative Cost Analysis of Rice Milling

 

Systems in the Philippines. Unpublisned Term Paper ror FSM-PAM

 

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Z7.

25.

29.

3U.

31.

32.

33.

34.

187
Rapusas, R. S., D. B. de Padua and E. P. Lozada, 1978. Pre-drying

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Los Banos, Philippines.

 

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John Wiley 5 Sons, Inc. New York, N. Y., U. S. A. 7
Shelby, n. W. Morrison and n. Traylor, 1974. economic Models for
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Agricultural Experiment Station of Arkansas and Lousiana, U. S. A.
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Timmer, C. Peter, 1974. ChOice of Techniques in Rice Milling

 

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of Science and lecnnOlogy, UniverSity of Arkansas, Fayetteville.

APPENDI CES

APPENDIX A

ENERGY CONSUMPTION

MEASUREMENT RESULTS

APPENDIX A

Appendix Table.Al presents the results of energy consumption
measurement on three electrically driven rice mills in the Philippines.
The second column gives the rated horsepower of the electric motor drive.
The next three columns give the efficiency, power factor and amperage
as measured. The sixth column gives the output horsepower as measured
and computed. The measured voltage was 220 volts througout the test.
All the values indicated were averages of three readings.

Table A2 presents the power requirement of adjustable and non-
adjustable separators as computed from the fbrmula given by Van Ruiten
(1974).

Table A3 gives the power requirement of Japanese rice mill com-

ponents as published by Satake Engineering Co. Ltd. of Tokyo, Japan.

188

 

 

 

 

 

th_h.—.nu-.—.-I——-.- n.—n.-———u~u ILAV uu.~.~hu.-P.L..—~—-u..<..,-h ~hAy~.~l.—».:-u..xhav..~ >uv--~r~hh

 

00:00000 oops» mo mumpm>w 0L0 mucoecpsnmoz .muHo> omm 000 00000H0> HH<

 

 

 

 

\m
.930: no: meow 0 Ho hpwomamo H090» 0 £903 :ofiwo:mecoo xoaczc 0 mm: Haws 00:0 \A
I I I I I I I I I I 0.0 0.0 00.0 00.0 0.0 o>000 0:00;
00.0 00.0 00.0 00.0 0.0 0.0 0.0 00.0 00.0 0.0 0.0 0.0 00.0 00.0 0.0 o>000 c000
I I I I I 0.0 00.0 00.0 . 00.0 0.0 00.0 0.00 00.0 00.0 0.00 0 chc>ce0
0000
I I I I I 0.0 00.0 00.0 00.0 0.0 00.0 0.0 00.0 00.0 0.00 0 000c>000
I I I I I I 00.0 00.0 . 0.0 00.0 0.0 00.0 00.0 0.00 0 LC0c>CH0
Cu—vaCH
00.0 0.00 00.0 00.0 0.0 0.00 0.0. 00.0 00.0 0.00 00.0 0.00 00.0 00.0 0.00 0 000000;:
00.0 0.00 00.0 00.0 0.0 0.00 0.00 00.0 00.0 0.00 00.0 0.00 00.0 00.0 0.00 0 000001;;
00.0 0.00 00.0 00.0 0.0 0.00 0.00 00.0 00.0 0.00 00.0 0.00 00.0 00.0 0.00 H 000000;:
'0”. 00V mm. o o 0.0 Con .. ..o o o la). 0 ,o 0 Co .. 0 ..rWCQ.
e H 0 <0 0 0 0 \mm o L c ,0 0 00 0 0 0 \et 0 0 RH 00 0 <0 0 t 0 \MLcsrtr .
I I I I I 00.0 0.0 00.0 00.0 H.H 00.0 0.0 00.0 00.0 0.0 00:00: srzc
00.0 0.0 00.0 00.0 0.0 I I I I I 00.0 0.0 00.0 00.0 0.0 0c000000<
I I I I I 00.0 0.0 00.0 00.0 0.0 00.0 0.0 00.0 00.0 0.0 m 0000::
2.0500
I I I I I I I I I I 00.0 00.0 00.0 00.0 0.0 0 0000::
00.0 0.0 00.0 00.0 0.0 I I I I I 00.0 0.0 00.0 00.0 0.0 H 0000::
00.0 N 00.0 00.0 0.0 00.0. 0.0 00.0 00.0 I 00.0 0.0 00.0 00.0 0.0 0000000000
.MQE< .a.: .00E< .&.: . .mCE< .L.:

.0.: .0 10.0 .000 00000 .0.: H .0.0 , .000 00000 ".0.: \M .0.0 .000 00000

. 0:02 900 so» H 05o: had mcou 0.0 \MLso: Lea mCOp 0.0 050500:
0<0 .0000 zH<00 000: .0.0 .000000202 .0H:0 .0000000020 0000000
0 0 H a H 0 < a < 0 . 0 a H z 0 0 H 0

I..On ‘0. .0. ‘SI' Ill!

 

I mAAH: NZHLQHAHZQ mmmze 00 whzmzmz3m<mz :OHBQZDmZOU >ozuzm .Hc 0;C<B

190

.HHHE ma:# :0 too: 090 upcadpcsoc OaLCuuzfiTc 03%

.LOuChmccc casc;r:hTCI:c:

.AN< canmh 000v :cflpcpsasoo L00 maachow 0H .0: 00:0L000: asap: confichO cc; 0002 HCCC10177<

\0
\m

a}

r

191
y
TABLE A2. COMPUTED ENERGY REQUIRE-TENT OF SEPARATORS

 

 

CAPACITY POWER REQUIRE-IBM , H. P.
Tons per hour Adjustable Nonsadjustable
l 1.0 1.5
2 2.0 3.0
3 3.0 4.5
4 4.0 6.0

 

l/

- Formulas in Reference E0. 33 were used and results checked by
actual measurement in Table A1.

2/
TABLE A3. ENERGY CONSUMPTION OF JAPANESE RICE MILLS

 

 

 

CAPACITY K A C H I N E
, Abrasive Friction
Tons per hour Precleaner Huller Whitener Whitener

 

H o r s e p o w e r

0.9 0.54 2.0 - -
1.0 - - 1.3 1.3
1.2 2.01 - - -
1.75 - 5.0 - —
2.0 - - 1.73 1.73
2.75 - 10.0 - -
3.5 - 14.75 - _
4.0 4.02 — — -
5.0 - - 3.5 2.5_
7.5 3.02 — - _
15.0 7.51 - — _

 

2/

— Satake Rice Processing Machinery, Satake Engineering Co. Ltd., Tokyo,

Japan

192

 

 

TABLE A4. DATA ON MACHINE CAPACITIESi/
MACHINE CAPACITY PROCESSING TIME

Tons per hour “Long Medium Short

Precleaner 0.257 3.89 3.40 3.03
0.293 3.41 3.03 2.72

0.403 2.48 2.27 2.10

0.623 1.61 1.52 1.43

1.027 0.97 0.94 0.91

Disc Huller 0.330 3.89 3.40 3.03
0.367 3.41 3.03 2.72

0.477 2.48 2.27 2.10

0.697 1.61 1.52 1.43

1.100 0.97 0.94 0.91

Rubber Roll Huller 0.850 1.00 1.18 1.43
1.75 0.80 0.67 0.50

2.75 0.40 0.36 0.33

3.50 0.33 0.29 0.25

Paddy Separator 0.352 3.25 3.03 2.84
0.528 2.16 2.02 1.89

0.704 1.62 1.52 1.42

0.880 1.30 1.21 1.14

1.056 1.03 1.01 0.95

1.408 0.81 0.76 0.71

1.525 0.75 0.70 0.66

1.760 0.65 0.61 0.57

2.112 0.54 0.51 0.47

2.346 0.49 0.45 0.43

Whitener 0.278 4.31 3.92 3.60
0.292 3.98 3.68 3.42

0.362 3.27 2.99 2.76

0.501 2.39 2.17 2.00

0.697 1.80 1.59 1.43

0.766 1.43 1.37 1.31

 

l/Jose Bernabe & Co., Inc., 1535 J. Luna St., Manila, Philippines

193
y
TABLE A5. MATERIAL REDUCTION FOR DIFFERENT MACHINES

 

 

 

MACHINE MATERIAL REDUCTION VARIABLE BY GRAIN TYPE
Long Medium Short
Rubber Roll Huller 0.74 0.78 0.82
Disc Huller 0.72 0.76 0.80
Whitener
1-pass 0.922 0.922 0.922
2-pass 0.961 0.961 0.961
3-pass 0.974 0.974 0.974
Separator
Disc Huller 0.61 0.61 0.61
RR Huller 0.88 0.88 0.88

 

1/

- See Reference No.

1

APPENDIX B

RICE MILL PERFORMANCE
SIMULATION MODEL PROGRAM

APPENDIX B

RICE MILL PERFORMANCE SIMULATION MODEL PROGRAM

This program simulates the performance of a conventional
disc-cone type rice mill. The program user could select the mass
of input paddy, type (within a prescribe type group), sizes and
number of component machinery to;be used in the simulation. He could
also select tne crop season, drying method, grain type and shape.
Other variables like drying delay, purity and moisture content of
paddy are based on probability distribution fUnctions. The output

of the program includes milling time, mass of rice output, milling

recovery, head rice percentage, discolored grain percentage and

energy used.

194

195
APPENDIX B

RICEMILL PERFORMANCE SIMULATION MODEL PROGRAM

fl RANDOM

5 REM
1¢ REM
15 REM
2¢ REM
25 REM
3D REM
35 REM
4¢ REM
45 REM
5¢ REM
55 REM
6¢ REM
65 REM
7D REM
75 REM
8¢ REM
85 REM
9¢ REM
95 REM

1¢¢ REM
1¢5 REM
11¢ REM
115 REM
12¢ REM
125 REM
13¢ REM
135 REM
14¢ REM
145 REM

*

I:

*

PROGRAM RICE MILL *

EP = ELEVATOR ENERGY REQUIREMENT, KMATT-MRS *
PCCAP = PADDY CLEANER CAPACITY, TONS/MR *
MEIN = MULLER BIN STORAGE CAPACITY, TONS *
NEIN = WHITENER BIN STORAGE CAPACITY, TONS *
PHCAP = PADDY HULLER CAPACITY, TONS/MR *

RCAP = RICEMILL CAPACITY, TONS/HR *

CPN NO. OF PADDY CLEANER IN PARALLEL *

MNP NO. OF PADDY MULLER IN PARALLEL *

SCAP = CAPACITY OF PADDY SEPARATOR *

ST = SEPARATOR TYPE: FIXED, VARIABLE STROKE *
HT = HULLER TYPE: DISC, RUBBER ROLL *
WT = WHITENER TYPE: CONE, CYLINDER *

wCAP = NHITENER CAPACITY, TONS/HR *

MC MOISTURE CONTENT AT MILLING, PERCENT NET BASIS *
DD DRYING DELAY, DAYS *

DM DRYING METHOD: SOLAR, MECHANICAL *

DAM = DAYS AFTER READING *

SR = SHAPE RATIO, LENGTM/NIDTM *

GT = GRAIN TYPE: SMORT, MEDIUM, LONG *

SEAS = PLANTING SEASON: NET, DRY *

TRLGTM = TOTAL RUNLENGTM , MR. *

TENRGY = TOTAL ENERGY, KNATT—MR. *

CRLGTM = PRECLEANER RUN TIME, MR. *
MDELAY = MULLER TANK FILLING TIME DELAY *

COT = PRECLEANER OUTPUT, TONS/HR. *

HP = POWER REQUIREI-IENT, HORSEPOI'IER *

CENRGY = PRECLEANER EIERGY CONSUMPTION, KNATT-HR. *
HTIME = HULLER RUNTII'IE, HRS *

196

REDUCTION VARIABLE FOR NMITENER *
155 REM * TMD TOTAL MILLED RICE RECOVERY DUE TO HARVEST DELAY *

16¢ REM * MMD MEADRICE RECOVERY DUE To HARVEST DELAY, PERCENT OF MAX *
165 REM * DG = MEADRICE RECOVERY DUE TO DRYING DELAY, PERCENT OF MAX *
17¢ REM * DCK = DISCOLORED KERNELS, PERCENT

174 DIM RATE (6). F¢(21). G¢(21)

175 INPUT R

176 FOR 2 1 to R

177 PCCAP ¢.33: MBIN = ¢.244: NBIN = .35 : PBIN = .313

178 RCAP = ¢.33: CPN = 1: SCAP = ¢.495: ME = 2: NCAP = .255

' 15¢ REM * VNI'.’

179 HTS = "DISC": WTS = "CONE": DMz = "SUN": PMCAP = .294
13¢ MNP = 1: Gs = 3.¢: IN = 1¢: STE = "FIXED"

181 GT8 = "MEDIUM": SEAss = "NET": SFBIN = ¢.27

19¢ RC = ¢

191 DAR = fl: W¢ = ¢: COT = 0: WOT = ¢

195 TRLGTM = ¢

2¢¢ TENRGY = ¢

2¢5 ' REM * PRECLEANING OPERATION *
2¢e w¢ = ¢

2¢7 IF CPN = ¢: GOTO 212

212 IF PCCAP = ¢: GOTO 217

215 CRLGTM = IN/PCCAP : GOTO 22¢

217 CRLGTM = ¢

22¢ MDELAY = (MBIN/2)/PCCAP

227 REM * REDUCTION VARIABLE AT PRECLEANER *

229 E¢(1) = -4. : E¢(2) = -1.645 : F0(3) = -1.282 F¢(4) = -1.036 :
E¢(5) - : E¢(6) = -.674 : E¢(7) = -.524 : E¢(8) = -.335 :
170(9) -.253 : F¢(10) = -.126 : 170(11) = 0 : 'F¢(12) = .126 :
E¢(13) = .253 . F¢(14) = .385 : E¢(15) = .524 : E¢(16) = .574 :
E¢(17) : F¢(18) 1.¢35 : E¢(19) = 1.282 : E¢(2¢) = 1.545 :
E¢(21)

235 SMALL = ¢ : DFI = ¢.¢5

237 DFI = DlFI

24¢ K = 21

- 242 GOSUB 3¢¢¢¢

II I
.I
(D
b
N
o o

11."
§

197

243 PDEV = XBAL : N¢ = ¢.969 + ¢.¢2 * PDEV

244 COT = N¢ * IN

245 REM * PRECLEANER ENERGY REQ *

246 EP = 3.63 * CRLGTM * ¢.746

247 TENRGY = ¢ : RLGTM = ¢ : NOT = ¢ : DCK = ¢ : TYLD = ¢ :
MEAD = ¢ : TRLGTM = ¢

25¢ MP = ¢.¢8 + ¢.47 * RCAP

255 Ml = MP * ¢.745

26¢ CENRGY = N1 * CRLGTM

261 TRLGTM = MDELAY + TRLGTM

265 TENRGY = TENRGY + CENRGY + EP

27¢ RATE(1) = IN/CRLGTN

271 IF GT3 = "SHORT" GOTO 331

272 IE GTS = "MEDIUM" GOTO 315

275 REM * MULLING OPERATION *

28¢ REM * DET MULLING TIME FOR LONG GRAIN BY CAPACITY *

29¢ MTIME = 5.89 * (2.721‘(-1.76 * PMCAP)

295 MRLGTM = MTIME * COT

3¢¢ GOTO 345

3¢5 REM * DET MULLING TIME FOR MEDIUM GRAIN *
315 MTIME = 5.¢4 * (2.724‘(.1.84 * PMCAP)

32¢ MRLGTM = MTIME * COT
325 GOTO 345

33¢ REM * DET MULLING TIME FOR SMORT GRAIN .
331 PMCAP RCAP/MNP
335 MTIME = 4.65 * (2.721‘(1.54 * PMCAP)
34¢ MRLGTM = MTIME * COT
345 MDT = ¢.77 * COT
35¢ REM * DET MULLING ENERGY REQUIREMENT *
355 IE MTS = "RR" GOTO 365
36¢ MP 2.7 * RCAPf(O.46) : GOTO 37¢
37¢ N7 HP * ¢.746
375 MENRGY = N7 * MRLGTM
380 EP = 3.63 * MRLGTM * ¢.746
,385 TENRGY = TENRGY + MENRGY + EP

 

198

39¢ RATE(2) = COT/MRLGTM

395 REM * COARSE DRAM SIFTER *

4¢¢ REII * DET PROCESSING TIME AND ENERGY *
4¢5 BSDEL = (SFBIN/2)/PHCAP * NNP)

41¢ TRLGTM = TRLGTM + BSDEL

415 B2SLGTH = MRLGTN

419 MP = ¢.49 * PNCAP 1‘(¢.93)

42¢ ESENRGY = E2SLGTM * NP * ¢.746

425 EP = 3.63 * E2SRLGTM * ¢.746

43¢ TENRGY + ESENRGY + EP + TENRGY

435 REM * RUSK ASPIRATOR *

44o REM * DETERMINE PROCESSING TIzzE AND ENERGY *
445 MADEL = ( BI :/2)/¢. 95 * PNCAP * MNP )

45¢ TRLGTH: TRLGTM + NADEL

455 WURLG = MRLGTM

459 MP = ¢.75 * RCAP1‘(1.36)

46¢ IIIUIIRGY = EUR LGTII * NP * ¢ .745

465 EP = 3.63 * MURLGTM * ¢.746

47¢ TENRGY = IT'NRGY + EP + TEIP GY
475 REM * PADDY SEPARATION ’
48¢ REY * PROCESSIIIG TIME *

485 PSDEL = (PBIN/2)/(¢.73 4 PMCAP * MNP)

49¢ TRLGTM TRLGTM + PSDEL

495 IF GTE "LONG" GOTO 51¢

5¢¢ IF GTE "MEDIUM" GOTO 515

5¢5 SRLGTM 2.84 * (2.72 T(-¢.89 * SCAP))* NOT : GOTO 525

5¢6 GOTO 525

51¢ SRLGTM = 3.24 * (2.72’T(-¢.89 * SCAP))* MOT : GOTO 525

511 GOTO 525

515 SRLGTM 3.¢:. * (2.721‘(-¢.89 * scum): NOT

52¢ REM * DET SEPARATOR REDUCTION VARIABLE *

525 IF RTE = "RR" GOTO 535
53¢ SOT NOT * ¢.61 : GOTO 545
535 SOT = NOT A 0.82

'540 REII * DET SE. ATOR EIIERSY RT QUIRE.EUT *

199

545 IF STS - "FIXED" GOTO 555
55¢ Cl - ¢.746 3 (0.76 3 RCAP (1.19) :3 GO‘I'O 56¢

555 01 .. ¢.746 3 (1:.5 ‘3 RCAP (1.07).

56¢ SENRGY . Cl 3 SRLGTH

565 EP - 4.9 3 SRLGTR 3 ¢.746

57¢ TENRGY . TENRGY + SENRGY 4» EP

575 RATB(3) .-.. HOT/SRLGTH _

53¢ RE}: 3 WHITENING OPERATION 3

585 NDEL a (U31N/2)/SCAP

59¢ TRLGTH . TRLGTH + mm

595 REN 3 NI-IITENING TIME 3 .

6¢¢ FOR A . 1 To (NU-1)

6¢5 NOEL . (NEIN/zm-JCAP 3 (¢.¢73) 3 (Alma),

61¢ TRLGTR . TRLGTH + UDEL

615 1151):? A

62¢POR3=1TONN

625.]! GT5 3 "LONG" GOTO 64¢

63¢ D‘ GT8 - "MEDIUM" GOTO 64¢

335 02 . 6.08 3 (2.721‘(-2.¢7 3 ¢.77 3 PRCAP)) . GOTO 645
64¢ 02 . 7.32 3 (2.721‘(-2.11 3 ¢.77 3 PHCAPH . GOTO 645
.645 NRLGTR . HOT/CZ

65¢ REN 3 DET REDUCTION VARIABLE FOR NINTENER 3
655 VNN . (¢.922) ‘r (1/NN)

67¢ IF UTS . "CYL" GOTO 69¢

675 REN 3 DET, CONE METEN'ER ENERGY REG 3
63¢ C5 - 3.61 3 RCAP? (¢.71)

635 UENRGY .. C5 3 NRLGTR

69¢ EP . 4.9 3 URLGTH

695 TENRGY s TENRGY + NENRGY + EP

7¢¢ NEXT 3

7¢5 GOTO 775

71¢ REM 3 DETERMINE AERASIVE/FRICTION ENERGY REQ 3
715 cs .. 1.21 3 NCAP‘!‘(¢.62)

72¢ C7 =- 1.3 3 NCAP‘!‘ (¢.41)

~725 wENRGY a (C6 + C7) 3 NRLGTH

73¢
735
74¢
745
75¢
755
76¢
765
77¢
775
78¢
731
732
735
737
733
79¢
792
794
325
33¢
335
84¢

346
347
343
351
355
36¢
365
37¢
372
373
376

200

E? = 4.9 3 NRLGTN
TENRGY = TENRGY + WENRGY + EP
FOR C = 1 TO NP

PDELAY (PBIN/2)/PCAP)
TRLGTH a TRLGTH + PDELAY

NEXT C

REN 3 GRADING OPERATION 3
REM 3 DETERNINE REDUCTION VARIABLE DUE TO QUALITY 3
REM 3 MOISTURE CONTENT
GDELAY 14.¢/6¢.¢
TRLGTH TRLGTH + GDELAY
NDEv = XBAL
NC ¢.1157 + ¢.¢¢11 3 NDEv
EN (257.996) 3 (I-ICT3)-(123.97) 3 (NC1‘2) + (13.715072) * ¢.¢9121
REE 3 NATURITY (DAYS AFTER READING) 3
IF SEASz = "DRY" GOTO 9¢4
DAH = 34 + 2 3 PDEV
IF DAR>44 GOTO 79¢
IF DAH< 24 GOTO 79¢
REN 3 NATURITY (DAYS AFTER READING) 3
IF SEASS = "DRY" GOTO 9¢6
IF DN 3 = "SUN" GOTO 346
HHD = -¢.53 + ¢.1012 3 DAN -¢.¢¢15 3 (DAR1‘2)
GOTO 37¢
z¢ = DAN
G¢(1) ¢.92 G¢(2) ¢.94 : G¢(3) = ¢.95 : G¢ (4) = ¢.95
G¢(5) ¢.94 G¢(6) ¢.925 : G¢ (7) = ¢.89 : G¢(3) = ¢.35
SMALL = 28 : K = 8.93 : DFI = 2.25
GOSUB 25¢¢¢
HHD = XAL

REN 3 HEAD RICE DUE TO DRYING DELAY 3
01 = 3 : $1 = 1¢.49
GOSUB 4¢¢¢¢ : DD = XCAL
IF DD 13 GOTO 372
Z¢ = DD

378
879
881
882
883

335
392
393
395
396
397
393
399
9¢1
9¢2
9¢4
9¢6
9¢3
9¢9
91¢
915
92¢
921
923
924
925
93¢
935
933
939
941
942
945
95¢

20].
G¢(1) = ¢.925 : G¢(2) = ¢365 : G¢(3) = ¢.33 : G¢(4) = ¢.3¢

G¢(5) = ¢.793 : G¢(6) = ¢.795 : G¢(7) = ¢.795 : G¢(3) = ¢.795
SMALL = ¢ : K = 3 : DFI = 2

T¢ = z¢
GOSUB 25¢¢¢
DG = XAL
REE 3 DISCOLORED KERNELS DUE TO DRYING DELAY 3
G¢(1) = ¢.¢ : G¢(2) = ¢.¢1 : G¢(3) = ¢.¢ 2 : G¢(4) = ¢.¢6
G¢(5) = 1.¢1 : G¢(6) = 1.94 : G¢(7) = 2.33 : G¢(3) = 3.75
SMALL = ¢ : K = 3 : DFI = 2.¢
z¢ = DD
GOSUB 25¢¢¢
DCK = XAL
GOTO 93¢
REN 3 DRY SEASON 3
DAH = 23 + 2 3 XBAL
IF DAH) 33 GOTO 9¢2 : IF DAN<13 GOTO 9¢3
Q1 = 3 : 81 = 1¢.49
GOSUB 4¢¢¢¢ : DD = XCAL
IF DD 13 GOTO 9¢3
IF DR S = "SUN" GOTO 921
HHD = —1.1372 + 0.1412 3 DAH - ¢.¢¢23 3 DAR¢2)
GOTO 933
z¢ = DAH
G¢(1) = ¢.94 : G¢(2) = ¢.94 : G¢(3) = ¢.93 : G¢(4) = 0.92
G¢(5) = ¢.33 : G¢(6) = ¢.34 : G¢(7) = ¢.79 : G¢(3) = 0.72

SMALL 23 : K = 5 : DFI = 3
GOSUB 25¢¢¢

HHD = XAL

G¢(1) 1.¢ : G¢(2) = ¢.97 : G¢(3)
G¢(5) ¢95 : G¢(6) = ¢.95 : G¢(7)
SMALL = ¢ : K = 3.¢ : DFI = 4.¢
z¢ = DD

GOSUB 25¢¢¢

D6 = XAL

¢.9s ; c¢<4) = ¢.955
9.95 : 69(8) = ¢.95

954
955
956
957
96¢
97¢
975
93¢
933
934
935
986
933
939
99¢
992
995

1¢¢¢

1¢¢5
1¢1¢
1¢15
1¢2¢
1¢25
1¢3¢
1¢35
1¢4¢
1¢45
1¢55
1¢6¢
1¢65
1¢7¢
1¢75
1¢8¢

1¢89

202

G¢(1) ¢.¢ : G¢(2) = ¢.3 : G¢(3) = ¢.79 : G¢(4) = 1.35
G¢(5) 1.72 : G¢(6) = 1.74 : G¢(7) = 1.99 : G¢(3) = 1.99
SMALL 2 ¢ : K = 3.¢ : DFI = 6.¢

z¢ = DD

GOSUB 25¢¢¢

DCK = XAL

REM 3 SHAPE-RATIO FACTOR 3
VS (1¢¢.73 - 9 3 Gs)/1¢¢
QP MM 3 HHD 3 vs 3 DG
THD = 37.17 + 17.33 3 LOG(DAH)
IF HTS = .3 RR 3 GOTO 933
NOT _ VNN 3 HOT 3 ¢.97 3 TED/1¢¢ : GOTO 99¢
NOT VNN 3 HOT 3 TED/1¢¢
GOTO 992
GOT OP 3 NOT : GOTO 1¢¢2
GOT OP 3 NOT 3 1.¢8 : GOTO 1¢2¢
REM 3 DETERMINE ENERGY REO BY CAPACITY 3
IF HT 3 = "RR" GOTO 1¢2¢
GRLGTH = (NOT/.922)/NCAP
GENRGY = (¢.49 3 RCAP1‘(¢.93)) 3 ¢.746 3 GRLGTR
GOTO 1¢3¢
GRLGTH

(NOT/¢.922)/NCAP
GENRGY 1¢.43 3NCAP‘r(¢.92)) 3 0.746 3 GRLGTH
TENRGY TENRGY + GENRGY
RATE (6) = POT/GRLGTN
TRLGTH = TRLGTH + GRLGTH
REM 3 DETERMINE PERFORMANCE CRITERIA 3
1¢¢ 3 (GOT/NOT)
BROK 1¢¢ 3 1¢¢ (NOT—GOT)/NOT
TyLD 1¢¢ 3 (NOT/COT)
PRINT 64 "TIME OUTPUT RECOV HEAD DISC ENERGY"
PRINT 123, "HRS TONS PERCENT PERCENT PERCENT KNATT-RRS"

HEAD

PRINT TAB (¢) TRLGTR ; TAB (3) NOT ; TAB (14) TYLD ; TAB (24) HEAD

TAB (35) DCK ; TAB (46) TENRGY
RTNRGY = RTNRGY + TENRGY '

1¢9¢
11¢¢
1115
1113
1125

1133

114¢
24399
25¢¢¢
25¢65
25¢7¢
25¢75
25¢3¢
25¢35
25¢9¢
23995
3¢¢¢¢
3¢¢35
3¢¢4¢
3¢¢45
3¢¢5¢
3¢¢55
3¢¢6¢
3¢¢65
3¢¢7¢
33995
39¢¢¢
4¢¢¢¢
4¢¢¢5
4¢¢1¢
4¢¢15
4¢¢2¢
4¢¢25
4¢¢3¢
4¢¢35

‘203
RRLGTH = RRLGTH + TRLGTH
RDCK = RDCK + DCK
RHEAD = RHEAD + HEAD
RYLD = RYLD + TYLD
PRINT TAB(¢) RRLGTH ;
TAB(32) RDCK ;

TAB(G) RWOT ; TAB(13) RYLD ; TAB(Zl) RHEAD

TAB(46) RTNRGY

STOP

NEXT 2

END .
REE 3 SUBROUTINE TAB 3

DUN = z¢ - SMALL

IF DUM< ¢ THEN DUN = ¢

IF DUI-I)((K-1) 3 DFI) THEN DU‘II = (K-1) 3 DFI
R = 1 + DUE/DFI
IF R = K THEN R = K — 1
XAL = G¢(R) + ((G¢(R+1) - G¢ (R))/DFI) 3 (DUM -(FIX(R-l) 3 DFI))
END
REN 3 SUBROUTINE RANDOM 3
Y¢ = RND(¢)
DUN = Y¢ - SNALL
IF DUI-K ¢ THEN DUM = ¢
IF DUN)((K—1) 3 DFI)
I = 1 + DUN/DFI

DENDmI=(R4)3INI

IF I = K THEN I = K - 1
XBAL = F¢(Z) + ((F¢(Z+1) - F¢(Z))/DFI) 3 (DUN -(FIX(Z-l)) 3 DFI
RETURN
END

REM 3 SUBROUTINE GAMMA 3
TD = 1.¢
FOR T¢ = 1 TO 01

R¢ = RND (¢)

IF R¢ = ¢ GOTO 4¢¢1¢
TD = TD 3 R¢

NEXT T¢

XCAL
RETURN

— LOG (TD)/(FIX(Ql)/Sl)

APPENDIX C

GAMMA DISTRIBUTION FUNCTION

APPENDIX C

GAMMA DISTRIBUTION FUNCTION

In any activity or project there is a time lag or delay

between initiation and completion. Tnis type of delay is frequent-

ly encountered in aggregative processes wnere streams or flows made

up of many entities are subject to delays which vary Irom entity to
entity. Examples are the rate of adoption or an attitude or innovation,
the aggregate growth of capital in an economy and the rate at which
plants reach maturity. Tnis type of delays can De modeled by a dirferen-
tial equation such as shown 1n this appendix. Delays come In different
orders depending on the aiSErioution function of tue delay. The order

of the delay is defined as the order 01 the differential equation.

204

205

APPENDIX C

GAMMA DISTRIBUTION FUNCTION

 

k k—l k-l
_ P. dE(t) Q d EH?) 2 dB(t) .3
Mt) " {1:} dt + I‘D.) dt k-l + + k[k] dt + “(m

('1

expected average delay (no. of years to complete a project)

N“
7‘

= order of the delay

E = rate of completion scheduled per year
when N = O, B(t) = E(t) which implies instantaneous completion
k = l, E(t) = D* dB(t) + E(t) (exponential distribution)

dt

k co. B(t) = E(t-D)
’k-é 93, the time profile of the completion approaches the normal
distribution.

APPENDIX D

CHI-SQUARE ANALYSIS OF
RANDOM VARIABLES

APPENDIX D

Among the parameters used in the rice mill performance model
were three random variables. These were moiSture content, purity
of paddy and drying delay. Base data from the WOIK of tamacho, et al.,
(1978) was analyze to determine the hind or distribution function
for these variaoles. The hyp0theses that the distribution function
for these variables was a normal curve was tested by the chi-SQuare
test. Table D1 shows the Chi-square value computed for maisture con-
tent of samples taken from rice mills used in the study. The Chi-
square value was 12.55 which is less than the tabular value of 13.3.
This indicates that the distribution function is normal. The same
is true fbr Table D2 which Shows the analysis for purity of paddy and

the cumulative prooability table for drying delay.

206

207

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APPENDIX E

VALIDATION OF THE LINEAR

NETWORK ANALYSIS

APPtNDlX E

This appendix provides a validation on the result of the linear

network economic analysis. The cost of milled rice with the correspon-

ding credit to by-product was computed by an accounting method. The
result which gave the total cost of milled rice as P 1.25 per kilogram

was closed to the values given by the linear network equations.

299

2 10
APPENDIX E

ESTIMATE or cosr or MILLED RICE-1!

Cost of Paddy :- P 1.00 per kg.-2-/
Cost of paddy needed to produce 1 kg. rice 1’ 1.43
. Less cost of broken 1.43 X 0.075 X 0.80 a 0.090
Less cost of dark bran 1.43 x 0.025 X 0.4 a 0.014
Less cost of medium bran 1.43 X 0.06 X 0.5 I 0.430
Less cost of fine bran 1.43 X 0.0808 X .65 a 0.075
Less cost of brewers 1.43 X 0.005 X 1.0 a 0.070
Total = 0.229

Cost of paddy less by-product P 1.201
Cost of processing 0.05
Total Cost of Milled Rice P 1.251

 

A[Includes material and processing energy cost

2/

- 14 percent moisture content

APPENDIX F

DERIVATION OF LINEAR

NETWORK ANALYSIS

APPENDIX F

DERIVATION OF THE LINEAR NETWORK ECONOMIC

MODEL

This Appendix gives the detail of the combination of equations
E(l) to E(19) and C(1) to C(16) in Section 4.2.1.8 to obtain the equa-
tion for the rice milling economic model. For example, equation F(1)

gives the equation for X which was obtained by combining equations

43
5(15), E(16), E(17) and E(18).

211

212
APPENDIX E

LINEAR NETWORK ECONOMIC HODEL

\r + K f (Y )

' K12“41"41 12 01 01

X43 = K12K11Xll ' K12‘21X21 ' h12K31x31

+ K12X2T ' f02(Y02) F(1)

X = K + K X + K + K K Y

c . x <
24 13X13 23 23 43K11112X11 43 12 21 21 + I43I

f x
‘12K31‘31

+ Y

.43K

K f (Y ) - K

K
43.12 01 01 y K f (Y )

r V r
12h41“41 + 43h12‘2T + 43 02 02

" f03(Y03) ' X3T F(2)

. = _ - , K K K K <
X K14Xi4 K24K13X13 + K24x23X23 + k43K24 12 11X11 + 43 24112

I: K n J E: -
121721 + I[431(24K12Y2T + 43K24f02(Y02) K24f03(Y03)

— Y

.24? f (Y

‘3T ‘ 04 )

04 F(3)

X05 = ‘ K25X25 + K151‘14’14 + h151‘24K13X13 ’ K15K24K23X23 “ K43K24K15

Y K K X

h121‘11X11 ‘ K K K K K X ' k ‘15 12 31 31

43 24 15 12 21 21 43h24

_ K Y K K K f (Y )

K x (x x - s
43 24V15112K41y41 43 24 15 12 01 01 V y

( k X - L
+ I431/241’15 12 2T

- K K K f (Y ) + K K f (Y ) + K

43 24 15 02 O2 24 15 03 03 (

X + K

1’!
24“15 3T 15fo4 ) F(4)

Y04

X K K K

= - q _.. . - K ' - < K r
X06 I15X15 + K26X5T K25Y25 25 26K15 14x14 125 24 15 13‘13

- K K K X - K K K K K K X - K K Y K K

' t . K
26 24h15 23 23 43 26 24 15 12 ll 11 43 26 24 15 12 21x21

K K K K K X - K h K K X - K h

- { ' ' ' K ' ’ 1
I43 26 24 15 12 31 31 43 25 24 15 12 2T 43 26h24115f

O2

_- i K ' 1 :
(Y52) K26124 15f03(Y03) +.K26K24K15X3T + A26}15f04(YOA) F(5)

213

X09 = ’ K39K13X13 + I{359K23X7T L39’23K17X17 + h391‘23h27X6T

- K39x28K27K16X16 + K39x28K-26X5T K39K28K25X25- K39h28k26

K15K14X14 ’ K39 K28K26 24K15K 13 K13 ' K39 H28K26 24K15K23X23

\ X K \ - ' \ I K {
431(39K28y26y24 15K12V11X11K43139Y28K26h24 15121X21

\

- I{

— K . x K
K431N39K28126 24H<15}12K31X31 K43Y391H28126 24K15I{12K41X41

‘ KI43K39K231:26K24I15V12f01W01) K39K28K27K26f05(Y05) '

- K391{28K27f06(YO6) - 1(39K28f07(YO7) K39f08(Y 08) -K19X19

Y K X -K

' I ‘26 24 15 K12 2T 43K39K23

‘29X29 ' f09(Yog ) + I43‘39K23K 27

' ' C I K K ’ K
K27k26k24115f02(YO2) K39 “28127{26 24 15 f03(Y03) + I39 28x27

K K K

(Y ‘26 24 15

K K I

26 24 15X - Y K I( K I

. r K K K I
3T 39 23 27 26 15 f04 ) +

04 43 39 28 27

_ c c a K . K c .' F(5)
K12K11X11 I43139123127 26V24 15‘11y1

X25 = f4T(Y4T) + I12H11f11(Y1T I:12 M11X1 KI12 21 X21 ' K12“31“31

)— (Y ) + f (Y ) F(7)

f (Y ) ‘ h * (Y fo2 02 22 22

' K K X + 12 2T

7
12 41 41 L12 01 01 2T

To have a cost equation in terms of the output Ylo’ we have

the following relations:

— — ' \f - f
Y03 f y39 ' k39‘09 ‘ h39Y1o F‘s)

= 3 = ’ Y
YO7 Y28 KQBYOS h28K39( lO) F(9)

.Y = Y = K Y = I: I’ (Y

06 27 27 07 27 23 h'39 10) F(lO)

K:
II

V =
26

Y =

214

K K K r (Y )

05 26 06 26 27 23 39 10
Y04 = Y15 = K15Y05 = K15X26K27K28K39CY10)
Y03 = Y24 = K24Y04 = K15K24K26K23K39(Y1o)
Yo2 = Y43 "Y2o = I{433(03 ’ Y25 = K43Yo3 ' K25Y05
= K43K39K23K 27 K26K24K15(Y10) ’ K39K 23 27‘w26 25
Y01 = Y12 = K12Y02 = 1:43K39K2£3K27K26K24K15K12Y1o

K391(28 K27K26 :25K12Y10

(Y

10

)

F(ll)

F(12)

F(13)‘

F(14)

F(15)

Also, the component models by the principle of conservation

of energy for the transport processes are:

“1T

2T

.3T

4T

5T

6T

Also,

41

flT(YlT)

f (Y )

2T 2T

f3T(Y3T)

f4T(Y4T)

f (Y )

5T 5T

f6T(Y6T)

f9T(Y9T)

XCHAFF =

YlT = Y1

Y2T = Y01

YBT = Yoa

Y4T = Y04

YST = Y05

Y6T = Y06

Y7T = Y07

Y9T = Y09 = Y10

X
CH

F(16)

\F(17)

F(13)

F(19)

' 3(20)

F(21)

3(22)

F(23)

"1
[‘0
I)

21

>4

31

23

13

-X

-X

‘ XHULL = ‘

' kHERAN =

=-X

SAND =
XSTRAw =
CBRAN =

XBROKEN =

’ ’ XDBRAN =

BREW =

— ‘ XPOLISH =

V

"' 455A

X

ST

—X

— y
‘BRO

C

Y
‘HU

' ’ XFBRAN = ’

V

—X

B

- chRE

PO

215

F(25)
F(20)
1%(27)
F(28)

F(29)

.F(30)

F(3l)

F(32)

F(33)

F(34)

APPENDIX G

VALUES OF LINEAR

NETWORK COEFFICIENTS

APPENDIX G
TECHNICAL COEFFICIENTS (K), MATERIAL COST (X) AND PROCESSING COST Lf(Y)

Values of K, X and f(Y) for a one-ton per hour conventional mill
and IR-26 rice variety are listed in the appendix. The values for the
technical coefficients were obtained from' the work of Manalabe, et al.,
(1978). The values of material cost were obtained from the price list
of the National Grains Authority (1978) and the processing cost was from
measurements taken from three rice mills described in Section 3.4 and
Appendix A. These values were used in the linear network equations that
gave the cost of milled rice (See Appendix H). The results are valid
for IR-26 or any medium length and bold shape grain (See Appendix J).
The model is also applicable to rice mills ranging in size from one-
fourth to one and one-half tons per hour capacity equipped with at

least two whiteners.

216

217

APPENDIX G

Values of K,X and f(Y) for a one-ton/hour conventional
rice mill and IR-26 rice variety. I
K = 00 = r =
11 1 15 K21 no data R37 1.05
K12 = 1,0 K25 = 0.67 1.24 = 1.20
K13 = 0.075 K26 = 1.03
“14 = 0.249 K27 = 1.03
. = , ( = . .
YIS l 0 128 1 O3
hlG = QQS K29 = 0.0
hl7 = 0.06 £31 = no data
RIB = 0.0808 K41 = no data
K19 = 0.005 L43 = 1.04
X1 = ? 1.00/kg. paddy X14 = P 0.05/kg. hull
x21 = 0 X16 = ? 0.40/kg. dark bran
X01 - 0 X17 = P 0.50/kg. medium bran
= ' = , 1‘7. .
X41 0 £18 P 0 65/<o fine bran
' 3 = P 0.80/kg. broken X19 = P 1.00/kg. brewers
X23 = P 0.30/kg. coarse bran X20 = P 0.65/kg. polish
f6 = P 0.00012/kg. f6 = P 0.00063/kn.
01 CC 5
f6 ==P 0.00025/ke f6 = P 0.0005/k7.
02 " 07 D
6 h 1 6 — l-
r = P 0.0001o/ma. f o— P 0.00011/“3-
O3 ‘- Cu
6 5 v .
f = P 0.00057/kc. f o: P 0-00012/kg-
04 A O~
* C
U . p _ u
£05: 2 0.00051/dg. ‘10- P 0-000283/n5-
5 :5 _ ,6 _ PC _ 6 _ q A fifi fl
f1T_ 12T— 13T— 14T— fST P U.OUO¢¢/t<_.
C 6 6
‘F = : = O A ‘if‘.
‘5T 7T ng F o. 0040/.:

APPENDIX H

EVALUATION OF THE LINEAR

NETWORK EQUATIONS

APPENDIX H

EVALUATION OF THE LINEAR NETWORK EQUATION

This appendix shows the evaluation of the different terms in

the Linear Network Equations. values of K, X and f(Y) from Appendix

C were substituted into the corre5ponding variables in the equations.

The terms were then added and Similar terms comoined.

218

219

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APPENDIX I

RESULTS OF THE RICE MILL PERFORMANCE

MODEL SIMULATIONS

APPENDIX I

This appendix includes the results of the mill perfOrmance
modeling. Tables Ii to 142 are the results of the simulation run of
12 rice mills in their regular and modified configuration. An input of
10 tonnes paddy was used for each run. The values obtained for each
run varies because of the use of three random input data (moisture
content, purity and drying delay). Drying delay affects the head rice

and discolored grain percentages. This simulation process is discussed

in more detail in Section 5.1.

The output data includes the following variables:

(1) Time - milling time in hours.

(2) Output - milled rice output in ton.

(3) Recovery - total milled rice recovery or milling yield in
percent of clean input paddy.

(4) Head — head rice recovery in percent of milled rice output.

(5) Discolored - discolored (yellow) grain in percent of milled
rice output.

(6) Energy - energy usage per run in kwatt-hrs.

The data used in the simulation are as follows unless specified on the

table of a particular run.

(1) Bin Capacities (ft3)/m3
Huller Bin (HBIN) = 0.244/0.0069
Sifter Bin (SFBIN) = 0.270/0.0077
Paddy Separator Bin (PBIN) = 0.313/0.0089
Whitener Bin (WBIN) = o.3so/o.010
(2) Number of Paddy Cleaner (CPN) = 1
(3) Huller Type (HT): Disc
(4) Grain Shape (GS) = 3.0
(5) Grain Type (GT): Medium
(6) Drying Method (D11): Sun

224

225

- (7) Growing Season (Seas): Net
(8) Input (I) = 10 ton:.
(9) Whitener Type (WT): Cone
(10) Number of Paddy Huller (HNP) = 1

Model Numbers are those used by the manufacturer as in Table 5.1
of the text body. Those model numbers with apostrophe are modified
models. The fbllowing abbreviations in addition to those given above

were used in the tables.

(1) Rice Mill Capacity (RCAP)

(2) Paddy Cleaner Capacity (PCCAP)

(3) Number of Paddy Cleaner (CPN)

(4) Separator Capacity (SCAP)

(5) Number of Whiteners (NW)

(6) Whitener Capacity (WCAP)

(7) Paddy Huller Capacity (PHCAP)

(8) Separator Type (ST): Fixed or Adjus.

All machine capacities are in ton. per hour.

226

 

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APPENDIX J

SOME PHYSICAL PROPERTIES
OF RICE

APPENDIX J

SOME PHYSICAL PROPERTIES OF RICE GROWN IN THE PHILIPPINES

This appendix presents the type, shape, total and head rice
recovery of some rice varieties grown 1n the Phllippines. Grain length
and width was measured by the FAO recommended method of measuring the
length and width of brown rice 181d end to end and 51de by side, respec-
tively, and diV1d1ng by the number of grain used. Milling recoveries
were obtained under ideal conditions in the laboratory using rubber roll
huller and three-stage stone whitening. The rice samples were grown

under the same ideal field conditions.

261

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APPENDIX K

RELATIONSHIP OF MILLING RECOVERY

AND MOISTURE CONTENT AT MILLING

APPENDIX Kt

RELATIONSHIP OF MILLING RECOVERY AND MOISTURE CONTENT AT MILLING

This appendix.presents the result of milling teSts (milling
recovery, rice and by-product comp051tion) at different moiSture con-
tents ano periods of storage after drying. There was a strong correla-
tion oetween m01sture content at milling and milling recovery. In ge-
neral, the milling recovery dropped down as the moisture content went
up oeyond equilibrium moisture content (14 % wet basis). There was

no control on tne relative humidity during storage in this experiment.

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APPENDIX L

TIME STUDIES ON RICE MILLS

APPENDIX L

TIME STUDIES ON RICE MILLING

Time studies were conducted on two commercial mills, namely,

the Tobacco Industries of the Philippines Mill and the Mindanao Progress
Corporation Mill. The purpose of the study was to validate the results
of the simulation of milling time which were computed based on machine
capacities and time delays due to filling up of the bins. The procedure
consists of noting the time the paddy enters and leaves each particular
machine. These times were indicated on each machine in the accompanying
diagram starting with time equals zero at the intake of the mill. Note
the big difference in processing time lag between the Philippine—built
mill and the imported German mill. It took the rice to travel from intake
to output 53 minutes or almost an hour in the Philippine mill while it

took only 7 minutes for the imported mill.

272

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APPENDIX M

LINEAR NETWORK ECONOMIC MODEL

COMPUTER PROGRAM

APPENDIX M

LINEAR NETWORK ECONOMIC MODEL COMPUTER PROGRAM

The following computer program is the computer implementation of
the linear networx equations. Statement no. 50 is fOr conventional disc-

cone mills and Statement no. 100 is for rubber roll equipped mills.

275

1D
:15

25
3¢
:35
4¢
45
47
5¢
55
6O
65
7O
75
8O
85
9¢
95
1¢¢
1¢5
11¢

276

APPENDIX M

REM* PROGRAM MILLING COST*
INPUT HTS : INPUT Yifi

IF HTS = "DISC" GOTO 65

YA = ¢.¢D¢¢¢48454* Y1¢ (-.14)
YB = 1.8958* YIO ¢.41

Yc = ¢.¢485512* YID (-.¢7)

YD = ¢.¢48853* Y1¢ (.36)

YE = 2.922862* Y1¢ (-.29)

YF = ¢.27699* Yip (—1)

YG = ¢.466497 * Y1¢ (-.¢8)

YH = ¢¢¢¢7138* Y1¢
RIO = (YA + YB + YC + YD + YE + YF + YG +YH)* ¢.¢Dfl276.+ 1.1180

PRINT "le6 ="; Km

STOP

YA = D.¢¢¢¢¢48454* Y1¢ (-.14)
YB = 1.89580* Y1¢ (¢.41)

Yc = ¢.¢485612* Y1¢ (-.¢7)

YD = ¢.¢48863* YID (.36)

YE = 2.922862* YIO (-.29)

YF = D 27599* Yip (.1)

YG = ¢.466497* YID (-D.Oe)

x1¢~= (YA + YB + YC + YD + YE + YF + YG + YH +)* .¢¢¢276 1+ 1.1062
PRINT "Xl¢ = "; RIO

STOP

APPENDIX N

PHILIPPINE TRADE STANDARDS FOR RICE

1.

APPENDIX N

PHILIPPINE TRADE STANDARD
SPECIFICATION FOR MILIED RICE
(Second Revision)

PTs 042.02; 1973 .

FOREWORD

This standard specification for Milled
Rice is hereby promulgated under a fixed
designation PTS (Philippine Trade Standard)
NO. 0420.02; 1973.

This standard was revised due to the
request of the National Grains Authority and
upon the suggestion of the Technical Cannib-
tee to up-date the standard specification
and the definition of terms to suit the pre-
sent trend in the rice industry. It also
includes the weight per sack of rice to be

. 50 kilos in accordance with NGA Act.

Suggestions for revision should be ad-
dressed to the Bureau of Standards, P.O. Box
3719, Manila.

SCOPE,

1.1 This standard specification covers milled rice produced
in the Philippines, both for foreign and domestic trade.

DEFINITION OF TEES

2.1 For the purpose of this standard, the following terms
related to milled rice are hereby defined as follows:

2.1.1 Milled Rice - whole or broken kernels where
the bulls and at least the outer bran layers
and a part of the germ were removed.

2.1.2 Non-Glutinous Rice - generally translucent
with greater amylose content than amylopectin
and turns bluish when treated with potassium
iodide-iodine solution.

2.1.3 Glutimue Rice - generally Opaque, sticky when
etched, with high amylopectin than amylase con

277

2.1.4
2.1.5
2.1.6
2.1.7
2.1.8
2.1.9

2.1.10
2.1.11

2.1.12

am

278

tent and turns reddish-brown when treated with
potassium iodide—iodine solution.

Brewer or Binlid - portions of a kernel which ,
will pass through 8 U64 sieve (1.587 mm.) //

Broken kernels - milled rice smaller than head
rice but larger than brewer or binlid.

Chalk-y kernels - kernels with 50% or more white
portion.

Flinty kernel - kernel with less than 50% white
portion.

Foreign Matter - impurities such as weed seeds,
stones sand, dirt, etc. foreign to milled rice..

Head rice - whole kernels and those not less than
3/1 in size.

Kernel - edible port: on of a paddy grain-

liilling degree - the eirtent or degree of polish-
ing the rice kernel.

Other varieties - rice kernels of different va-
riety/ies other than the variety under consider-
ration.

Paddy or Palay - unhulled grain.

2.1.11. Red rice - rice with any degree of redness.

£1.15

Yellow Kernel - yellowish milled rice due to fer-
mentation or heat.

2.1.16 Damaged kernel - kernels attacked by micro-orga-

nisms, inseccts and/or other means.

3. CLASSIFICATION AND GRADES

3.1 Philippine milled rice shall be of the following type based
on the length of the kernel.

3.1.1

Type I, Long grain - longer than 5.9 m.

3.2

3.3

3.4

3.5

279

3.1.2 Type II, Medium grain - length ranges from 5.0
to 509 m

3.1.3 Type III, Short grain - shorter than 5.0 m

Each type shall be graded into sub-types according to
the shape based on the 1ength~width ratio.

3.2.1 Slender, Length-width ratio 3.0 or more
3.2.2 Bold, Length-width ratio ranges from 2.0 to 2.9
3.2.3 Rmmd, Length-width ratio is less than 2.0

The description of the milled rice according to types and
sub-types shall be taken collectively under the terms Grain

tyre.

Rice shall be classified according to varietal names. See
Appendix B.

Each group Of milled rice shall conform to any of the
following classes according to the degree of milling.

3.5.1 First Class - milled rice from which the husk,
the germ, the outer and inner bran layers have
been removed.

3.5.2 Second Class - milled rice from which the husk,
germ, the outer and major part of the inner bran
layers have been removed.

‘ 3.5.3 Third Class :- milled rice from which the husk, a

3.6

partof the gemandouterbranlayerbutnotthe
inner bran layer has been rancved.

3.5.1. Pinawa (brown rice) - milled rice from which only
the husk and part of the germ has been rancved.

Each class of milled rice shall be graded according to
the following description.

3.6.1 Premium Grade - Head rice not less than 95%, not
more than 5% of which are 3/4 kernels, broken 4%;
BKnlid 1%, yellow and damaged, 0.5% chalky and im-
mature kernels - 2%, Paddy-none, other varieties -
2.0%, Red rice - none, and Foreign matter - none.

4.

5.

280

3.6.2 Grade I - Head rice not less than 85%, not more

tinn 5% of which are 3/4 kernels, broken - 1a,
binlid - 3%, yellow and damage - 1.0%, challqr
and immune kernels - 4%, Paddy - 1 grain/100
gems, other varieties - 4%, Red Rice - 0.50%
and foreign mtter - 0.25%.

3.6.3 Grade II - Head rice not less than 75%, not more

than 5% of which are 3/4 kernels, broken 20%,
binlid 5%, yellow and damaged - 2%, chalky and
immature - 6% Paddy - 2 grains/100 grams, other
varéeties - 6%, Red Rte 1.0% and Foreign matter
0.5 .

3.6.4 Grade III - Head rice not less than 65%, not more

than 5% Of whicharo 3/4 kernels, broken 28%,

Binlid - 7%, yellow damaged - 4%, chalky and im-
matm'e kernels - 8%, paddy 3 grains/100 yams,-
Otg%er varieties 8%, Red Rice 1.5% and Foreign matter
1. .

GENERAL REQUIRHEENTS

4.1
4.2
4.3
4.4

Moisture content shall not exceed 14%.

It shall be free from unpleasant and/or repulsive odor.
It shall be free from insect infestation,

The unit of trading shall be by weigzt expressed in
kilograms or metric tons.

PACKING

5.1

Milled rice shall be packed in new or good used Hessian
cloth bag, jute gunny or plastic sacks without patches
and weighing 50 ldlograms not to afford maximnn protect-
ion i‘rom normal. hazard of transportation and handling.
Smaller packages may be allowed provided the net weight
shall be in full kilograms of 1, or multiple or 5
kilograms subject to buyer/seller agreement.

MARKING

6.1

Each bag shall be properly labelled with the following
information:

a; ‘Type & Subtype; variety, class and grade
b Name and address of miller

c Net weight in kilog‘ams

d Crop year and date of milling

7.

a.”

281

SAMPLING

7.1

7.2

7.3

7.4

Ten percent (10%) Of the total number of bags should
be sampled. but in no case should the number of bags
sampled be less than five (5) bags.

Each probe or handfull of sample drawn is called the
primary sample. The combined primary samples is called
composite sample. When a composite sample has been
properly reduced, it is called the suhnitted sample.

A sample obtained from the submitted sample is called
working sample.

The submitted sample simuldcarry the following infom-
ation:

7.3.1 Name and Address of owner

7.302 Variety

7.303 L017 number

734WOfbagsinthelot

73 5 Crop year&Date of milling

70306 Rte Cf Sampling

7 3 7 Name of inspector '

Preparation of the working sample - samples received in
the laboratory are reduced to a working sample. The
sample submitted shall be repeatedly divided so that
the working sample will be the representative of the
original. An efficient divider must be used on in the
absence of a mechanical divider.

TEST MOD

8.1

8.2

8.3

Grading Test - Weigh about 100 grams of milled rice
from the representative sample. The head rice shall
be separated from other extraneous matters and weighed
to determine the percentage.

Moisture content determination - the moisture content
of milled rice shall be determined by using a properly
calibrated moisture tester or by oven drying. In case
of air-o en method the temperature should be maintained
at 1050 '- 001°C.

Potassium iodide-iodine test - In case of doubt whether
a variety is glutinous or not, a KI-I test shall be
made on the kernels. -

 

 

REG:

:1?

282

9. WESTNIT!

9.1 This Standards Achninistrative Order shall take effect
20 days after completion of its publication in the
Official anette.

(SGD.) VIDALITO F. RANDA
Officer-in-Charge

HECOMDENDED BY:

(SGD.) MARIO R. REYES
(Achainistmtor, NACIDA)
Officer-in-Charge
Office of the Undersecretary of Trade
Chairman, Philippine Standards Council

APPROVED: Janna 21,1972
(Date

(son) TRDADIO T. QUIAZON, JR.
Secretary of Trade

1..
5.
6.
7.
8.
9.

293

STANDARDGRADEEEDUMTSFOR

fig...“ PDT‘WJ

Premium _

m; m2 I a; :1?-
Head Rice 95 Min. 85.0 Min. 75.0 Min. 65.0 Min.
Brokens I. Max 12.0 Max. 20.0 Max. 28.0 lax.
Binlid (Passes through

Sieve 4/62.) 1 3.0 a 5.0 n 7.0 a

Yellow 8: damaged 0.5 1.0 " 2.0 " 4.0 "
Change! Immature kernels 2.0 4.0 " 6.0 " 8.0 "
Paddy (No./1OO grams) None . 1 n 2 , n 3 1
Other varieties 2 I. " 6 " 8.0 ”p
Red Rice None 0.50 " 1.0 " 1.5 ""

Foreign mtter None 0.25 " 0.5 " 1.0 "

284

PHILIPPIHE TRADE STaPDARD SPECIFICaTION FOR PALKI
(ROUGH RICE)
PTS Oh2-Ol.02, 1968

O I

FORE-ORB

This standard Specification for
Philippine Palay or RoughRice is hereby
promulgated under a fixed designation
P'l‘S (Philippine Trade Standard) No. 0142- ‘
01.02: 1968. This standard was formulated
by the Philippine Standards Council, in or-
der to fix a uniform basis of determining
quality. The cooperation Of the Rice and
Corn administration, Rice and Corn Board,
U. P. College of agriculture, Bureau of
Plant Industry, International Rice Research
Institute, Rice and Corn Production Coor-
dinating Council, agricultural Productivity
Commission, hraneta University Foundation,
and Greater Manila Rice and Corn association,
were in a large measure reaponsible for the
formulation of this Standard.

Suggestion for revision should be

addressed to the Bureau of Standards,
P. 0. Box 3719, Manila, Philippines.

SCOPE

31.1 This standard covers the system of classifying and

grading palay of Philippine origin.
DEFINITION OF TIME

2.1 For the purpose of this Specification, the following
terms are hereby defined:

2.1.1 Rough rice - Those which contains 50% or more
, of unhulled kernels.

2.1.2 Foreign matters - These are impurities such as
stones and sand but excluding weed seeds.

. 2.1.3 Immature kernels - Those which are mostly green
and/or chalky.

3.

h.

' 2.1.h

2.1.6
2.1.7

2.1.8
2.1.9

_ 2.1. 10

2.1.1.1

£2.15

.' 285

/

Damaged.kernels-;.Those‘whioh are distinctly
discolored or damaged by water, insects, heat
or any’other.means. _

Other varieties - Those kernel which differ
distinctly from.the characteristics of the
palsy under consideration.

I

Fermented kernels - Those which are yellowish‘
or otherwise dis colored.

Cracked kernels - Those which are either cracked
or broken.

Red rice - Those palay, the kernels of which are red.

weed seeds and other crop seeds - All seeds which are

_other than palsy.

Moisture content - This'is the water content of the
grain computed on wet basis, as received.

Percentages - These are quantities expressed in

weight.

GENERAL REQU IRBLENTS

3.1 It shall.be free from.such foreign odors as mouldy ground,

insect,

rancid, sharp acrid and chemical, which are common

to unsound rice.

3.2. It shall be free from live insect infestation.

3.3 The unit of trading shall be by weight. expressed in kilograms
or in metric tons. ‘

CLxSSIFICATIO}! AND GRaDII-IG

h. l ‘Palay varieties shall be classified.in accordance with
the length of the kernel.

h.l. 1 The types of palsy based on the length of the ’

\

brown kernel shall be the following:

Type I - Long grain - the length of the kernel is

above 6.5 mm.

Type II-.hedium grain — the length of the kernel

ranges from.5. 5 mm. to 6.5 mm.

1+.3

286.

Type III — Short grain — the length of the kernel I

is

below 5.5. mm.

'h.2 Each type of palsy shall be grouped as follows:'

t..'2.l Fancy- the kernels have a flinty uniform appear-
ance, shinv, translucent or creamy white.

1.3.2 Special - the kernels exhibit a desirable uniform
white, creamy white or gray color.

1;.2.3 Ordinary — the kernels exhibit a white to dull
white or light gray color.

h.2.l+ Inferior - 1. Hilled rice withcolored pericarps

regardless of cooking characteris-
tics and translucency.

2. killed rice which are discolored
due to handling and storage.

Each group of palay shall be graded according to the
result of analysis as follows:

h.3.l Grade I - Palay having at least. 98% purity, foreign

h.3.2 Grade 2 ..

1+.3.3 Grade 3 -

1+.3.l+ Grade 1. ..

matter not more than 2%, weed seeds none,
immature kernels none, damaged grains 2%,
cracked kernels 3%, other varieties 3%,
fermented kernels none, red rice trace,
moisture content not exceeding 11$.

Palay having at least 96% purity, foreign
matter not more than 1.5%, weed seeds none,
immature kernels 2%, damaged kernels 3%,
other varieties 5%, cracked kernels 11$,
fermented kernels 0. 5'7}, rad rice 1%,
moisture content not exceeding 115.

Palay having at least 9% 'purity, foreign '
matter not more than 6%, ”weed seeds trace,
immature kernels A514, other varieties 8%,
damaged kernels AS, cracked kernels 5%,
fermented kernels 15., red rice 2%, moisture
content not exceeding 11%.

Palay having at least 92% purity, foreign
matter not more than 7. 75%, weed seeds

0. 25%, immature kernels 7,3, damaged
kernels 6%, other varieties 12%, fermented

5.

6.

’287

kernels 2%, red rice 3%, cracked kernels
6%, moisture content not exceeding 15%.

1..3. 5 Grade 5 - Palay having at least 90% purity, foreign

matter not more than 9. 5p, weed seeds 0.5%,
immature kernels 10%, damaged kernels 8%,
other varieties 17%, fermented kernels 3%,
red rice 1%, cracked kernels 7%, moisture ,
content not exceeding 15%.

11.3.6 Sub-standard grade - Palay that would not meet any of

the above requirements but the moisture
content shall not exceed 16%.

PAC KIM} A}! D PMMII‘B

531 Packing

5.1.1 Palay shall be packed'in jute, gunmr sacks or in
similar protective containers, weighing 50 kilograms
net on the basis of 11% moisture content.

5 .2 Marking

5.2.1 Each bag shall be properly labelled in big letters
with a suitable tag measuring approximately 18 x 12
cm., containing the following information:

5.2.1.1 Province where grown and crop year
5.2.1.2 Type, group, variety and grade
5.2.1.3 Name and address of warehouse
5.2.1.1»

’-

Net weight in kilograms

admins

6.1 Samples are drawn from the lot with the use of sampling

6.2

instruments like the Stick Trier or Sleeve—type Trier or
Hobbe Trier. When there are 10 bags or less , each bag
shall be sampled. If there are more than 10 bags but not
exceeding 100 bags at least every tenth bag but not less
than 10 bags shall be sampled. If a lot contains more
than 100 bags, at least 10% of the number of bags shall

be sampled at randan. ,
When a lot is sampled, several individual samples are
drawn from different places in the bulk. Each probe or
each handful of sample is called the primary sample. All
primary samples drawn are combined in a suitable container.
This combined primary sample is called the composite sample.

7.

, _. ~ 288

When the composite sample has. been properly reduced, it is
called the submitted sample. . This sample is suhnitted' to
the laboratory for .quality tests. The reduced sample,
obtained from the'suhnitted sample, is tenned as working
sample. ' ° ' ‘ . ‘ '

The sampling shall be done at random from top middle and

6.3
' bottom of each container. The inepector shall be reSponsible
for sampling the entire lot and the sumitted samples should

carry the following information: .

6.3.1 Name and address of owner.

6.3.2 Variety name

6.3.3 Let number

6.3.1. Number of bags in the lot

6.3.5 Date harvested '

6.3.6 Name-of inepector

6.1. Preparation of the working sample - Samples received in the
laboratory are reduced to a working sample. The sample
sutmitted shall be repeatedly divided so that the working
sample will be as representative as the original. An
efficient divider must be used.

6.11.1 Use of Mechanical Divider - The equipment usually
used is the Boerner Sample Divider. The sample is
emptied into a hopper at the top of the divider and
released by a hand lever to let it flow over an
inverted cone. It passes through a series of slots
around the circumference of the cone and falls into
two chutes. at the mouth of each chute" is a bucket
into which half of the sample runs. The Sample is
divided into approximately equal halves again and
again until it is about 1 kilogram.

6.15.2 Halving Method — In the absence of a mechanical sample
divider, the halving method will be used. The Sub-

_ mitted sample is repeatedly divided until about 100
grams of the quantity remains. .
INSPECTION . ‘ '
7.1 Destination Inspection - "hen the palay is passed through

points other than destination, the contract may require that _

7.2

. m
L

8.1

8.2

289 '

a" -

the palay be inspected at— destination for quantity and
quality. .

At the time - of loading palay for foreign trade, shipping '
weights shall be taken and certified before the inepectors'

. of the Bureau of Standards or a government weigher in the

locality.
IEPHCDS

Grading Test - Weigh about 100 grams of palsy from the
representative sample. The palay shall be separated
from other extraneous matters and weighed to determine
the percentages.

Moisture content determination - The moisture content of
palay shall be determined by using a properly calibrated
moisture tester.

8.2.1 Brown—Duvel Moisture Tester - Duplicate samples
of 100 grams each are taken from thoroughly mixed
palsy sample. Each weighed sample is transferred
into the flask of the tester which is then filled
with 1.50 c.c. of the Brown-Duvelftesting oil.
(Spica oil has similar properties.) a thermometer
is inserted through the stopper of this flask.
Four-fifths of the mercury bulb of the thermometer
should be submerged in the sample and the oil.

After tightly fitting the stopper, the flask is
connected to the condensing tube of the tester.
hang is applied until the cut-off temperature or

210 C, is reached. The moisture distilled off is
collected in a clean, dry graduated cylinden The .
amount of moisture collected will determine directly
the percentage of moisture content of the sample.

It 5 read after the temper Lture has gone down to
160 C (as prescribed in the direction for using

the Brown Duvel Moisture Tester).

8.2.2 Air-Oven Method - Two to five grams of the sample
are ground and weighed in a weighing'bottle 31th
a constant weight. The powdered sample is placed .
in an electric oven 5 hours at a temperature of
130°C. Then it is allowed to cool inside a des-
sicator and weighed afterwards. The percentage
moisture content is computed on the wet basis.

\

 

2‘90

9. EFFECTIVITY ' - \

9.1 This Standards administrative Order shall take effect
upon its approval.

(SGD.) R.'E. RnCELH
Director

JIL/I‘QB/lpl
HFCOIRLEIEED BY:

(SGD.) omen e. VIRATA
(Undersecretary of Industry)
Chairman, Philippine Standards Council

A'PLP-R'O V E D : Jul; 19, 1.3%

 

(550.) macaw s. BnlnTBhT .
Secretary of Commerce and Industry

2531

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Panel
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l. Milagrosa l.

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292‘

VaRIETY

Special

’Tjeremas

Intan
Bengawan

'Ac ALO Dr. 260

Azucena
BPI - 121
Raminad Str. 3

Special

B E _ 3
BPI - 76
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Raminad

12 - 36
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3.

h.

5.

293‘

WNCTIONS or THE emu or s'mmmns ransom
'ro mane .191“ A109

To formulate and promulgate rules and regulations for the
establishment of standard specification.‘for Philippine
products, codes, and methods of tests, subject to the
approval of the Secretary of Commerce and Industry.

To inspect and sample in order to determine if, and to
certify that, the products satisfy the requirements as

to kind, class, grade or standard of any of the products,
before the government, including government owned or con-
trolled corporations, make a purchase and/or producer
manufacturer and/or dealer offers for sale any conuodity
which affects the life, health and preperty of the people.

To inapect and sample in order to determine the standard
commodities for which a standard has been promulgated and
approved and to certify the inepection and standard there-
of as conforming to the standard set, before it is sold
and/or diSposed of in any manner, either for local. distri-
bution and/or for export abroad.

To inspect and sample commodities projected for shipment

abroad for which no standard has or shall have as yet been

established in order to detemine if, and to certify that,
the whole shipment satisfies the buyer's or importer's
requirement as to kind, class, grade, quality or standard.

To inspect, sample and certify to the quality of conuodities
imported into the Philippines to determine the country of
origin and to determine if they satisfy the buyer's or im-
porter's requirements or-Specifications for domestic con-
sumption. -

To confiscate any article which are the growth, raw materials,
manufacture, process or produce'of countries without trade
relation with the Republic of the Philippines.

To fix and collect fees for the services of inapection and/or
testing or- analysis of commodities, and for other services.,

\

2'9 '4'

‘hDVAL'I‘aGES 4ND PURPOSE OF sTANDflRDIZATION

. \

Standardization:

3.

b.

C.

8.

Helps in increasing the productivity of industry of which
economic progress ultimr tely depends.

Imme quality of products contin usly, for in standard-
ization, technical knowledge appli is recorded from
Which to base the desired improvement.

Promotes scientific research and improves basic knowledge
in physical constants and preperties of materials .

Effects economr on the use of labor, materials and time;
thus reduces costs of goods and services.

Conserve efforts that would otherwise have to be used in
rediscovering and reinventing elements involved in quality
and production of goods.

Eliminates needles: variation of types, sizes, designs, or
excmsive quality, which lead in turn to production and
distribution economics, and brings dividends through such
simplification.

Makes possible the use of cheaper materials, eliminates
painstaking production steps, reduces inspection require-
ment, and makes possible employment of simpler equipment
and operation.

Prevents waste of efforts among competing establishments

in their duplicating, or even in their conflicting systan

i.

.1 of standards.

Make possible interchangeability of component parts,
assemblies and complete products; thus facilitating repair
and replacement, amking possible long-run highly repetitive
manufacturing. ,

Gives buyers adequate reliance on the consistent quality of
standardized products, thus simplifying purchase or sale
thereof or facilitating the consummation of business
transaction.

Promotes truthful branding and advertising of goods and
services by providing suitable safeguards against the use
of confusing or spurious elements just for purposes of huge,
though sliortramge, profit motive. .

APPENDIX 0

NORMAL VALUES OF LOS BANDS

WEATH ER DATA

APPENDIX 0

NORMAL VALUES 0F LOS BANOS WEATHER DATA

Appendix Table 01 gives the normal (mean) values of weather

elements in L05 Banos which is included in the Southern Luzon type of

weather. This table was used in determining the variation of relative

humidity on a month to month ba51s.

295

2996’

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APPENDIX P

ECONOMIC VARIABLES USED IN THE

NETWORK EQUATIONS

fiat

297

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APPENDIX Q

COST OF RUBBER ROLLS

 

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APPENDIX R

PADDY PLANTING AREA

IN THE PHILIPPINES

APPENDIX R1

Palay (Rough Rice) Planting Area in 1977 Compared with that in 1976 By
Region, PhiIippines

 

Crapyear Cropyear Increase/

Region 1977 1976 (decrease)

(hectage) (percent)
Philippines 3,547,500 3,579,320 (0.9)
Ilocos 310,860 342,590 (9.3)
Cagayan Valley 432,600 418,700 3.3
Central Luzon 412,210 464,720 (11.3)
Southern Tagalog 456,120 461,080 (1.1)
Bicol 334,410 338,590 (1.2)
Western Visayas 474,170 448,730 5.7
Central Visayas 88,000 89,600 (1.8)
Eastern Visayas 180,530 181,200 (0.4)
Northern Mindanao 157,810 163,840 (3,7)
Southern Mindanao 164,700 162,840 1.1
Central Mindanao 392,480 367,140 6.9
Western Mindanao 143,610 140,290 2.4

 

Source:

Bureau of Agricultural Economics

302

APPENDIX S

RICE MILL STATISTICS
IN THE PHILIPPINES

__

APPENDIX 51

STATISTIG ON THE

RICE INDUSTRY IN'THE PHILIPPINES
Statistical Datta. Based on an Early 1973 Survey

E - haleberg Rico Milling Unit
C - Comercial Rice Mill (Cone Type)

 

 

 

3()3

Name of the Total Rioeland Number of mommlives‘ Rice Mills Warehouses
Province in hetero .Farmer No. Members E C No. m
4+ Emilies
Ilocos Norte 30,023 350613 23 3, 351 332 - 8 155,200
Ilocos Sur 27,344 26,157 58 5,764 128 1 14 225,00
Abra 22,436 15, 605 11 865 79 1 5 37, 600
La Union 23,941 23,744 35 2.335 202 2 12 322,000
Pangasinan 124.902 81.359 35 10,449 449 85 107 2,484,761
Zambalee 19,826 12,082 5 130 117 19 5 63,000
Batanee 79 611
0383333 871145 417 776 25 1a 591 523 70 255 1, 985, 250
Kalinga-
Apayao - u? —? 1 521 1 ?
Mountain
Province 551 034 451 593 99 3 16 479 .400
Benguet
Ifugao
Isabela 112.379 45.445 22 5.750 227 33 128 2,516,600
Nueva
Vizoaya 30.970 14.767 9 936 86 13 82 1.409.000
Tarlac 97.114 34.448 17 5.279 152 55 66 1,136,000
Pampansa 73.593 22.023 24 2.763 81 93 141 4.217.100
Bataan 20,111 6,405 6 318 25 47 64 1,390,750
Bulaoan 67.730 29.778 19 2.512 63 198 239 3,048,200
Nueva Ecija 167,261 55.465 31 15,875 125 170 477 9,379,080
Rizal 15.046 7.954 39 3. 656 49 46 56 522,100
Gavite 34.557 17, 995 12 386 73 93 153 943,000
Batangaa 69,798 47,356 16 3,293 221 11 - 96 201,780
manna 39.325 15.259 18 1.182 93 89 127 1,192,320
Quezon 60,633 39, 931 10 2, 171 31 2 34 62 148, 240
I-iarindumie 16,474 10,453 5 1, 103 45 1 28 3, 693, 880
Mindoro 0r. 45.957 21,232 6 384 107 18 25 1,012,000
nindoro 000. 21,581 7,576 3 182 84 2 40 1,194,300
Camrines Norte 14,970 7,324 3 330 45 15 17 142,500
Camrinea Sur 112,277 57,320 15 2.914 462 64 142 1,501,600
Sorsogon 27,298 15,617 5 83 62 12 60 241,800
Catanduanes 10, 275 7, 968 8 176,800
Mas‘oate 28, 602 12, 261 2 529 66 5 50 125, 000
11.11. a E. Samar 102,133 60.396 10 738 117 22 65 127,807
- Northern Ley‘be 74,689 45,195 12 907 151 53 112 987,170
Southern Ley'te 8,509 8,334 2 543 23 11 4 4,310

"1

3(14

Statistics on the Rice (cont'd)

 

 

 

 

 

Name of the Total mceland Number of Coomrativee Rice Hills Warehouses '
Province in Baotare .Famer N0. Hembere E C No. Capacity
..- gEeeiliee.
Bohol 44,883 40,535 10 9,600 49 46 30 67,342
Cebu 3,818 3,800 7 70 61 26 98 3,693,880
Negros 0r. 17,911 12,101 4 505 72 33 25 59,770
Negros 066. 83,477 36,748 35 25,246 97 41 99 921.200
Iloilo 157.699 69.634 30 2.152 444 70 45 1.157.090
Capiz 54,214 27,634 10 915 160 29 49 527,680
Antique 39.977 25,640 2 626 148 5 11 52,700
Aklan 24,149 19,129 2 138 66 1 6 13,000
Romblon 10,114 8,367 1 240 45 9 17.800
Palawan 19,853 17,207 73 5 3 14,000
Sulu 14,916 10,504 14 2 25,000
Surigao del
Norte 14,092 8,728 2 427 30 5 11 104,700
Surigao del
Sur 25,939 14,910 4 250 39 3 10 101,500
Agusan N. 4.5. 18,449 10,822 5 2,332 30 18 47 284,000
Bukidnon 23,555 13,788 4 474 128 29 12 406,000
Davao N. 2,5. 43,911 30,384 11 2,423 239 110 53 635.050
Misamie 0r. 8,202 5,843 4 216 20 23 25 1,012,000
Hisamie coo. 13.784 12,117 2 6o 54 25 31 473.410
Lanao del Norte 24.852 12,221 3 182 83 33 38 672,450
Lanao del Sur 54,416 24,285 4 240 35 2 31 75,608
Cotabato N. e S. 251,278 105,389 19 1,744 505 89 178 8,720,117
Zamboanga del
Norte 18, 547 15, 941 6 604 48 9 13 44, 700
Zanboanga del
TOTAL 2,760,402 1,469,757 661 117.296 7.383 1.929 3,662 63,876,045