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This is to certify that the
dissertation entitled

MODELING OF IN-BIN REVERSE-AIRFLOW EAR-MAIZE
DRYING: OPTIMIZING CAPACITY AND COST

presented by

Md. Taufiqul Islam

has been accepted towards fulfillment
of the requirements for the

PhD. degree in Agricultural Engineering

 

 

,‘afl/

' ”or Pro ssor’s Signature

bk 900,3

Date

MSU is an Affirmative Action/Equal Opportunity Institution

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Michigan State
niversity

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PLACE IN RETURN Box to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE DATE DUE DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6/01 c-JCIRC/DatoDuopBS-p. 1 5

 

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MODELING OF IN-BIN REVERSE-AIRFLOW EAR-MAIZE DRYING:
OPTIMIZING CAPACITY AND COST
By

Md. Taufiqul Islam

A DISSERTATION

Submitted to
Michigan State University
In partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Agricultural Engineering

2004

 

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ABSTRACT
MODELING OF IN-BIN REVERSE-AIRFLOW EAR-MAIZE DRYING:
OPTIMIZING CAPACITY AND COST
by

Md. Taufiqul Islam

Maize is an important food and feed crop. An optimum harvest of maize requires
high-quality seed. Seed-maize is harvested as car maize at 30-35% (w.b.) moisture
content in order to avoid field losses. For safe storage, it is necessary to reduce the
moisture content to 12-13%. An ear-maize dryer is an in-bin dryer in which the airflow
is reversed sometime during the drying cycle. The lack of automatic control of the
drying-air temperature. the air-reversal time. and the total drying time often results in
under- or over-drying. The goal of this study was to develop simulation and control
models for maximizing drying capacity or minimizing drying cost of in-bin ear-maize
dryers.

The ear-maize drying process was simulated using a modified version of the MSU
in-bin drying model. Bed shrinkage was incorporated in the model as a function of
drying time and moisture content. The In-Bin Reverse-Airflow Ear-Maize Drying Model
was validated with experimental data from 24 experiments conducted at a commercial

seed-maize processing facility.

 

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The (.‘omp/cx ()ptin-Iizution Method was used to obtain the optimal values of the
initial bed-depth. the up-air and the down-air temperatures. the up-air and down-air
airflows. and the reversal moisture content for maximizing the capacity or for minimizing
the cost of in-bin drying process at different initial moisture contents.

Comparing the drying model to the experimental data yielded standard errors of
prediction of2% for the transient local moisture content and 9 h for the total drying time.
The wet-bulb temperature had the largest effect on the capacity and on the cost.

The optimization results showed that ifa commercial 24—bin seed-maize dryer is
operated under an optimum-capacity scenario. given 30% initial ear-maize moisture
content. it will dry 24% more than when it is operated under minimum-cost control.
However. the maximum-capacity option costs 36% more than the minimum-cost

scenario.

Dedicated
To the sweet memory of my mother. Mosammat F azilatun Nesa
and
My father, Md. Gias Uddin Biswas
They sacrificed themselves for shaping my life and wished high for me.
and
To my beloved brother. Md. Tauhidur Rahman who came late but departed early.

May Allah forgive them all and reward them with the Jannah.

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ACKNOWLEDGEMENTS

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My deepest appreciation and gratitude to Dr. F. W. Bakker-Arkema for his
technical and moral support. During my study. he has been an advisor. a family friend.
and consoler. I am proud of being his last student. His example has profoundly
influenced me and will continue to influence all aspects of my life. I thank you Dr.
Bakker-Arkema for being available for me even after your retirement.

I would like to thank Dr. Bradley P. Marks for his valuable guidance and
assistance. The direction he has given me during his supervision was invaluable and
deserves appreciation. Above all. I thank you Dr. B. P. Marks for taking the
responsibility of acting as advisor after the retirement of Dr. F. W. Bakker-Arkema.

Gracious appreciation is extended to my guidance committee members: Dr.
Lawrence 0. Copeland. Crop and Soil Science; and Dr. Clark J. Radcliffe. Mechanical
Engineering. for serving on my guidance committee.

I acknowledge the partial funding of the project by Pioneer Hi—Bred International
Inc. and the facility they provided for experimental work. Special thanks to Dr. Cyrille
Precetti, and Dr. J. L. Hunter for extending the necessary help.

I also acknowledge the cooperation received from the staff at the Pioneer seed
plant at Constantine. MI without whom the field experiments could not have been

conducted.

 

 

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I would like to thank the graduate students of the Department of Agricultural
Engineering. especially those of room 6 for their moral support. in particular Qiang Liu.
Nicholas R. Friant. and Andrew J. Wood.

I acknowledge the support and the friendly environment extended by the
Bangladeshi community. especially Dr. Habibur Rahman C howdhury. and Dr. Md.
Nazrul Islam. for their suggestion and help.

My deepest appreciation goes to the Bangladesh Rice Research Institute and the
Bangladeshi people. Without their financial support this study would not have
materialized.

Thanks to my eldest brother Md. Mostafizur Rahman for shaping my life and
without whose help I would not have dared to dream about higher study. Loving thanks
is offered to my brothers. my sisters for all their sacrifices and support.

Finally. deep appreciation to my wife Showokotara Begum. my beloved daughters
Nabila Taufiq. Maliha Taufiq. and Anika Taufiq for their help. sacrifice. understanding.

and patience during my graduate study.

vi

 

 

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TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. x
LIST OF FIGURES ......................................................................................................... xii
LIST OF SYMBOLS ....................................................................................................... xv
1. INTRODUCTION ........................................................................................................ 1
1.1 Seed-Maize Production ......................................................................................... 1

1.2 Drying of Food and Feed Maize 3

1.3 Drying of Seed-Maize ........................................................................................... 3

2. OBJECTIVES ............................................................................................................... 6
3. LITERATURE REVIEW ............................................................................................ 7
3.1 Seed-Maize ............................................................................................................... 7
3.1.1 Harvesting ........................................................................................................ 11
3.1.2 Drying Methods ............................................................................................... 13
3.1.2.1 Sun Drying ................................................................................................ 13
3.1.2.2 Crib Drying ............................................................................................... 15

3.1.2.3 Reverse-airflow In-bin Drying .................................................................. 17
3.1.2.4 Continuous-flow Concurrent-flow Drying ............................................... 17

3.2 Modeling of Ear-maize Drying ............................................................................... 19
3.2.1 Thin-layer Ear-maize Drying ........................................................................... 19
3.2.2 Deep-bed Ear-maize Drying ............................................................................ 23

3.3 Additional Relevant Maize-drying Information ..................................................... 24
3.3.1 Temperature Effect on Seed Quality ................................................................ 24
3.3.2 Overdrying and Underdrying ........................................................................... 26
3.3.3 Shrinkage during Drying .................................................................................. 29
3.3.4 Seed Tempering ............................................................................................... 30

3.4 Optimization of Dryer Design ................................................................................ 30
4. THEORY ..................................................................................................................... 33
4.1 Modeling of In-bin Reverse-airflow Ear-maize Dryers .......................................... 33
4.1.1 Conventional Ear-maize Dryer Design ............................................................ 33
4.1.2 Hunter Design .................................................................................................. 37

4.2 Development of the Model Equations .................................................................... 39
4.2.1 Shrinkage Model .............................................................................................. 40
4.2.2 Thin-layer Drying Equation ............................................................................. 40
4.2.3 Equilibrium Moisture Content ......................................................................... 41
4.2.4 Psychrometric Air Properties ........................................................................... 41
4.2.4.1 Vapor Pressure Equation ........................................................................... 42
4.2.4.2 Relative Humidity Equation ..................................................................... 42

vii

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4.2.4.3 Humidity Ratio Equation .......................................................................... 42

4.2.4.4 Dry-bulb Temperature .............................................................................. 43
4.2.4.5 Wet-bulb Temperature Equation ............................................................... 43
4.2.4.6 Enthalpy Equation ..................................................................................... 43
4.2.4.7 Specific Volume Equation ........................................................................ 44
4.2.5 Other Parameter Values ................................................................................... 44
4.2.5.1 Physical/Parameter Values for Air. Ear—maize. At. and Ax ...................... 44
4.2.5.2 Airflow Resistance Equation .................................................................... 46
4.2.6 Numerical Solution of the Model .................................................................... 46
4.3 Optimization ........................................................................................................... 49
4.3.1 Capacity Optimization ..................................................................................... 51
4.3.2 Cost Minimization ........................................................................................... 51
4.3.2.1 Fuel Energy ............................................................................................... 52
4.3.2.2 Electrical Energy ....................................................................................... 55
4.3.2.3 Total Energy Cost ..................................................................................... 55
4.3.3 Constraints ....................................................................................................... 56
4.3.4 Mathematical Representation of the Optimization Problem ............................ 57
4.3.5 Flow Diagram ofOptimization Program ......................................................... 58
5. EXPERIMENTAL ...................................................................................................... 60
5.1 Overview ................................................................................................................. 60
5.2 Data Collected ......................................................................................................... 61
5.2.1 Moisture Profiling ............................................................................................ 61
5.2.2 Temperature Profiling ...................................................................................... 62
5.2.3 Bed-Depth Change Recording ......................................................................... 64
5.2.4 Ear versus Kernel Moisture ............................................................................. 64
5.3 Viability Tests ......................................................................................................... 66
6. RESULTS AND DISCUSSION ................................................................................. 67
6.1 Experimental Results .............................................................................................. 67
6.1.1 Viability Test Results ....................................................................................... 78
6.2 Validation ................................................................................................................ 80
6.2.1 Dryer Model ..................................................................................................... 80
6.2.2 Shrinkage Model .............................................................................................. 91
6.2.3 Transient versus Average Input ....................................................................... 96
6.3 Typical Simulation Output ...................................................................................... 97
6.4 Effect of ambient condition on the total drying time and the energy requirement 1 12
6.5 Effect of parameters on the capacity and the cost ................................................. 1 14
6.6 Optimization ......................................................................................................... 122
6.6.1 Capacity Maximization .................................................................................. 122
6.6.2 Cost Minimization ......................................................................................... 126
6.6.3 Sensitivity Analysis ....................................................................................... 129
6.6.3.1 Effect ofFuel and Electricity Price Increase 33

6.7 Projected Capacity of a Dryer if Operated under Optimal Conditions ................. 133
6.8 Rules for Dryer Control ........................................................................................ 136

viii

 

 

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7. SUMMARY ............................................................................................................... 137

8. RECOMMENDATIONS FOR FUTURE WORK ................................................. 139
9. REFERENCES .......................................................................................................... 140
10. APPENDICES ......................................................................................................... 145
APPENDIX A ........................................................................................................... 147
APPENDIX B ........................................................................................................... 167

 

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Table 1.

Table 1.

Table 3.

Table 3.

Table 4.

Table 6.

Table 6.

Table 6.

Table 6.

Table 6.

Table 6.

Table 6.

Table 6.

Table 6.

I»)

Is)

IO

b.)

0

LIST OF TABLES

Total maize and maize-seed production in Asia.

the USA and the World in 1995-2000 (1000 MT) .......................................... 2
Total and seed production for major crops in 2000 ( 1000 MT). ..................... 2
Shrinkage in metric tonnes and shrinkage costs

when 26.483 MT are dried below 1 1.9% .................................................. 28
Additional energy required per metric tonne and cost

(at different energy prices) resulting from various levels of

overdrying of 26.483 MT of 1 1.9% maize ............................................... 28
At. Ax. and physical property values of air and ear-maize used in the
simulation model ...................................................................................... 45
Experimental data collected at commercial ear-maize

dryer. Constantine. Michigan during the 2001 harvest season ....................... 68
Germination percentage of seed-maize dried in a commercial

ear-maize dryer ............................................................................................... 79
Experimental total drying time and as simulated by the model

with and without the bed-shrinkage equation ................................................. 95
Comparison between experimental and simulated total

drying times using transient and average drying air data ........................... 96
Simulated kernel moisture contents. depth change and exhaust

drying-air conditions over the drying period (before reversal) ................. 99
Simulated kernel moisture contents. depth change and exhaust

drying-air conditions over the drying period (After reversal). .................... 100
Effect of ambient relative humidity (at constant

ambient temperature.2 1 OC) .......................................................................... 1 13

Effect ofambient air temperature (at constant humidity ratio. 0.0055). ...... l 13
Standard drying conditions. ......................................................................... 1 15

 

 

 

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Table 6. 10 Standard input parameters used in the optimization program ...................... 123
Table 6. l 1 Capacity optimization ofthe com-*entinmt/ and the Hunter dryers. .............. 124

Table 6. 12 Capacity optimization ofthe conventiomi/ dryer at high zip-air
and down-air ten'zperatures. ......................................................................... 127

Table 6. 13 Cost minimization ofthe com'entimml and the Hunter dryers ................... 128

Table 6. 14 Sensitivity analysis of the capacityfimction at 300 0
initial moisture content. ............................................................................... 131

Table 6. 15 Sensitivity analysis of the cost/imction at 30% initial moisture content. . .. 132

Table 6. 16 Effect oft/ire] and electricity prices on the optimum capacity

and/minimum cost of an ear-maize drying system ....................................... 134
Table 6. 17 Dryer capacity per month at optimal parameter values for the

conventional and Hunter dryers ................................................................... 135
Table 6. 18 Rules for dryer control ................................................................................. 136
Table A.1 Transient kernel moisture contents (%w.b.). field data. on top and

at the bottom of beds ofear-maize during the in-bin drying process .......... 147
Table A2 Transient depths and moisture contents (%w.b.) during drying .................. 153
Table A3 Transient temperatures (°C). field data. at two

locations inside the beds during ear-maize drying ........................................ 154

xi

 

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LIST OF FIGURES

1 Schematic diagram ofa conventional commercial ear-maize dryer. ............ 5
l Ear-maize. cob. and kernels ............................................................................ 8
2 Schematic ofa seed-maize (Adapted from Copeland and McDonald. 2001 ). 9
3 X-ray ofear-maize showing its different parts (Islam et al.. 1996). ............ 10
4 Black layer in maize-kernels (FAO. 1982).3.1.2 Drying Methods .............. 12
5 Sun drying of maize kernels. ........................................................................ 14
6 Sun drying of ear-maize. .............................................................................. l4
7 Rectangular double crib for drying ear-maize (USDA. 1952). .................... 16
8 Schematic of one stage concurrent—flow dryer with counter-flow cooler. 18
9 Influence of seed-maize initial moisture content on seed temperature

during concurrent-flow drying and counter-flow

cooling (Lescano. 1986) ................................................................................ 20

. 10 Number of underdried and overdried bins at two commercial

in-bin ear-maize drying plants (Preeetti. 2001) ........................................... 27
1 Schematic diagram of a conventional ear-maize dryer. ............................... 34
2 Plan of a conventional ear-maize dryer with 24 bins. .................................. 36
3 Schematic diagram ofthe I-Iunter dryer ........................................................ 38
4 Flow diagram of the computer program for fixed-bed ear-maize dryer ....... 48
5 Energy diagram for a conventional ear-maize dryer in three different

cases (Top: up-air > down-air. Middle: up-air < down-air.

Bottom: up-air = down-air) .......................................................................... 53
6 Energy diagram for a Hunter ear-maize dryer. ............................................... 54

Figure 4. 7 Flow diagram of the computer program for optimizing the

fixed-bed ear-maize drying process ............................................................... 59

xii

 

 

 

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1 Data logger with temperature sensors looking down from

the top ofa drying bin ................................................................................... 63
2 Relationship between the ear-maize moisture content and the

maize-kernel moisture content (Precetti. 2001) ........................................... 65
1 A typical chart used by dryer operators of an ear-maize dryer

showing the inlet. the exhaust. and the wet-bulb temperatures.

and the static pressure (Pioneer. 2001) ........................................................ 69
2 Moisture contents at the top of the drying bed during

a typical drying run (air reversal after 42 h) ................................................. 71
3 Moisture contents at the bottom of the drying bed during

a typical drying run (air reversal after 42 h) .................................................. 72
4 Moisture content on top and at the bottom ofa bed of

ear-maize during the in-bin drying process (air reversal alter 42 h) .............. 73
5 Experimental temperature profile at the 0.6. 1.2. and 1.8 m

bed depth during in-bin drying ofear-maize (air reversal after 52 h) ........... 74
6 Depth versus drying time during a typical drying test (test # 2). .................. 76
7 Depth change versus moisture content change (test #2). ............................. 77
8 Simulated and experimental moisture content at the bottom

of the bed during drying (test #13) ................................................................ 81
9 Simulated and experimental moisture contents at the top

of the bed during drying (test #13) ............................................................... 82
10 Simulated versus experimental moisture contents at the bottom

of the dryer during drying (RMSE: 2.8%) .................................................... 84
1 1 Simulated versus experimental moisture content at the top

of the dryer during drying (RMSE: 3.5%) .................................................... 85
12 Simulated versus experimental/incl] moisture content (RMSE: 2.0%) ........ 86

13 Simulated versus experimental total drying time (TDT) (RMSE: 9.0 h). 88

14 Simulated versus experimental temperature (”O at 0.6 m depth

from the bottom of the drying bin (RMSE: 2.00C) ........................................ 89

xiii

 

 

 

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15 Simulated versus experimental temperature (°C) at 1.2 m depth

from the bottom of the drying bin (RMSE: 25°C) ...................................... 90
16 Simulated versus experimental shrinkage ofa bed ofear-maize. ................ 92

17 Experimental and simulated moisture content ofa bed of ear-maize
during drying with and without shrinkage .................................................... 94

18 Simulated relative humidity of the exhaust air for ear-maize
with four different initial moisture contents during drying ......................... 101

19 Simulated humidity ratio of the exhaust air for ear-maize with four
different initial moisture contents during drying ........................................ 102

20 Simulated wet-bulb temperature for ear-maize with four different
initial moisture contents during drying ...................................................... 104

21 Simulated dry-bulb temperature for ear-maize with four different

initial moisture contents during drying ..................................................... 105
22 Change in the average moisture content and bed-depth with time ............. 107
23 Change in bed-depth and airflow through the drying bed with time .......... 108
24 Change of airflow through the drying bed with time. ................................ 109

25 Humidity ratio and relative humidity of the exhaust air dttring

the first six hours of drying ......................................................................... l 1 1
26 Effect of up-air temperature on the capacity and the energy cost. ............. 1 16
27 Effect ofdown-air temperature on the capacity and the energy cost. ........ 1 17
28 Effect of static pressure (airflow) on the capacity and the energy cost. ..... 1 18
29 Effect of depth on the capacity and the energy cost. .................................. 1 19
30 Effect of wet-bulb temperature on the capacity and the energy cost. ........ 120

31 Effect of reversal moisture contents on the capacity and the energy cost.. 121
1 Grain Analysis Computer. Model GAC2000 ............................................. 171

2 HOBO"! H8 Outdoor/Industrial 4-Channe1 External Logger.
temperature sensors (TMC20-HA). HOBO Shuttle .................................... 171

xiv

 

 

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LIST OF SYMBOLS

Average ear-maize moisture content. decimal d.b.
Change in bed depth, m m'1

Shrinkage, m

Change in moisture content. %d.b.

Static pressure. Pa

Change in moisture content. %w.b.

Shrinkage, %

Dimensionless characteristic constants for ear-maize
Specific heat, kJ kg”l °C

Dry depth of maize bed. In

Energy, kJ kg'l

Airflow rate. kg h'1 m2

Humidity ratio, kg kg'l

Volumetric heat transfer coefficient. kW m‘3 °C
Latent heat of vaporization. kJ kg’l

Drying parameter, (h")

Bed depth. m

Initial bed depth, m

Initial ear-maize moisture content, decimal d.b.
Equilibrium ear-maize moisture content. decimal d.b.
Final ear-maize moisture content. decimal d.b.
Ear-maize moisture ratio

Ear-maize moisture at the dryer inlet. °C
Pressure. Pa

Atmospheric pressure, Pa

Partial pressure of vapor at saturation. Pa

Airflow rate. m3 m'2 s'l

Relative humidity. %

Temperature, °C

Time. h

Half response time of the grain moisture ratio (from 1.0 to 0.5) at T0
Inlet drying air temperature. °C

Mean inlet air temperature during pass. OC
Absolute temperature. K

Equilibrium air temperature, °C

Travel rate of the drying zone, kg dry grain h'1 m2
Price, $ kWh"

Grain moisture content. kg kg'I

Bed coordinate, m

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amb

cap

d.b.

down

e

ex

f

g

m

rev

tdown

therm

tup

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v
w
w.b.
d.b.

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km

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Ambient
Capacity
Dry basis
Down-air
Electricity
Exhaust-air
Fuel
Ear-maize
Motor
Reversal
Total down
Thermal
Total up
Up-air
Vapor
Water

Wet basis
Dry basis

Settlement constant, dimensionless
Efficiency

Grain temperature. 0C

Shrinkage coefficient. dimensionless
Density. kg m'3

Time constant. It

Corrected time constant. h

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1. INTRODUCTION

Maizel is an important food crop in many countries. Its main uses in the world
market are as a feed crOp and as raw material for the milling industry. Its uses have
become diversified — ranging from cereal to biodegradable fuel to cosmetics. To obtain

the desirable yield and quality. quality seed-maize is a prerequisite.

1.1 Seed-Maize Production

Table 1.1 shows the 1995 — 2000 total maize production and the seed-maize
production worldwide, in Asia. and in the USA. The world maize production in 2000
was 12% higher than in 1995; the seed-maize production increased during that period by
6% in the world. by 8% in Asia. and decreased by 2% in the USA. Comparing 1999 and
2000, worldwide maize production and seed-maize production changed by —3% and
+2%. respectively; the corresponding figures for Asia were —13% and —2%. and for the
USA +6% and 0%.

Table 1.2 shows the 2000 total maize production and the seed-maize production
in Asia. the USA. and the world. of the major grain crops. Maize ranked second in total
production and fourth in seed production in the world. In Asia it ranked third in both.
and in the USA it ranked first and the second. respectively. Also. 29% of the total cereal

production and 8% of the cereal-seed production was in maize.

 

' The word maize (corn) in this thesis refers to Zea mays L.

 

 

 

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Table l.

(TUp

\
Barley
Maize
Rice
ll'lieut
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S“UM: 1911

in 1995-2000 (1000 MT).

Table l. 1 Total maize and seed-maize production in Asia, the USA and the World

 

Total Maize Production

Seed-maize Production

 

Year Asia USA World Asia USA World

1995 148.550 187.969 516.438 2.638 518 5.471
1996 166.966 234.527 588.867 2.855 516 5.699
1997 143.307 233.867 585.001 2.777 518 5.591
1998 173.343 247.882 614.355 2.930 503 5.752
1999 168.668 239.549 605.204 2.925 506 5.745
2000 145.979 253.208 589.355 2.854 506 5.848

 

Source: FAO (2001 )

Table l. 2 Total and seed production for major crops in 2000 (1000 MT).

 

 

Asia USA World
Crop Total Seed Total Seed Total Seed

Production Production Production Production Production Production
Barley 18.093 1.733 6.921 207 132.897 9.048
Maize 145.979 2.854 253.208 506 589.355 5.848
Rice 543.737 16.012 8.669 186 597.155 17.535
Wheat 249.788 15.230 60.512 2.341 580.015 33.589
All Cereals 988.967 36.922 343.866 3.512 2.051.273 73.980

 

Source: FAO (2001 )

Ix)

 

 

 

1.2111

in min

11.11 1e '.

   
 

high L111

 

1133-5

principu

mixed-1

1.31)“

Shellin;
Pmduet

l“miter:

€de £11 .
alf IS IT
The lust

Ilelt‘r;

1.2 Drying of Food and Feed Maize

Food and feed maize are usually harvested at 20 — 38% moisture content (w.b.)2
to minimize field losses. Drying is essential to prevent the crop from molding. Usually.
maize is shelled in the field and then transported to a drying facility to be dried at a fairly
high air temperature. The drying-air temperature for feed maize ranges from 50-300°C
(122-572°F) and for food maize from 27-60°C (80-140°F) (Brooker et al.. 1992). The
principal high-temperature dryer types are: crossflow. concurrent-flow. counterflow. and

mixed-flow. Low-temperature drying usually is practiced in bins.

1.3 Drying of Seed-Maize

Producing seed with high viability is the primary objective ofthe seed producer.
Shelling before drying and high-temperature drying are harmful for quality seed
production (Copeland. 2002). Therefore. maize is dried on the ear at relatively low air
temperatures.

Figure 1.1 shows a conventional commercial seed-maize dryer. Freshly-harvested
cars at a moisture content of25-35% are placed in a bin with an angled floor. The drying
air is first forced for 30 — 60 h from the bottom to the top (the so-called zip-air): during
the last 30 — 50 h. the direction of the air is reversed (the so-called donut-air). The drying

temperatures ofthe up-air and the down-air are 30 — 45°C (86 - 1 13°F). depending on the

 

' Monsture contents in this IIICSIS are expressed on a wet basis (w.b.) unless dry basis (d.b.) is expressly
designated.

 

 

 

 

 

we

reach

of lb.

time 11

pruetie.

1Preeett

 

 

111C 111151

mildcfs ‘

type of hybrid and the initial moisture content ofthe maize. The total drying time to
reach the desired final average moisture content of 1 1.5 — 12.5% is 60 — 80 h.

The simultaneous operation ofa number of ear-maize dryers (ttsually several rows
of 16 — 24 dryers in series) is a complicated process. Operators have to select the correct
time for air-reversal. and for stopping the second stage in the drying process. Extensive
practical experience is required to operate an ear-maize drying system properly.

Underdrying and overdrying occurs at 40-55% of commercial drying facilities
(Precetti. 2000). Underdrying results in viability loss: overdrying is costly. because of
the wasteful weight loss and extra energy consumption.

Ear-maize drying has not been researched in depth. and no adequate simulation

models exist.

 

 

 

 

 

  
 

Upper air plenum

 

lLower air plenum

 

 

 

 

 

 

 

 

 

 

Figure l. 1 Schematic diagram of a conventional commercial ear-maize dryer.

 

 

 

 

simulation

in order to

l

-'- T0 OPIIIT

,
- T0 dete

Th

,_ I
1h.
. Toetnl

moistur

 

 

Capacity

2. OBJECTIVES

The general objective of the research reported in this thesis was to develop a

simulation model for the drying of seed-maize in a commercial. in-bin air-reversal dryer.

in order to improve dryer operation.

DJ

The specific objectives were:
To evaluate the change in ear-maize bed-depth during drying as affected by the
moisture change.
To develop an ear-maize in-bin drying model incorporating the changing bed-depth.
To optimize the conventional and recently-patented Hunter ear-maize dryers for

capacity and cost.

 

 

 

3.1 Seed-‘
Se.
says. the \
containing
I

the mature
The
embryo. en.
niary 14:111..
Endosperm

loud for the j

 

a dumtant )9
serves as the .

The e4
ehaffi tough \
60.3%. the pit
Lathrop. 1953

Cereal
itnrlduide. T 1
approximately
pmtlllCllttn (Ta

basis).

3. LITERATURE REVIEW

3.] Seed-Maize

Seed is the mature. ripened ovule of a seed plant before germination. In many
ways, the seed is a microcosm of life itself. “The seed is a neatly wrapped package
containing a living organism capable of exhibiting almost all of the processes found in
the mature plant” (Copeland and McDonald. 2001).

The seed-maize is a single fruit called the kernel (Figure 3.1). It includes an
embryo. endosperm. aleurone. and pericarp (Figure 3.2). The pericarp is the transformed
ovary wall, and covers the kernel and furnishes protection for the interior parts.
Endosperm usually makes up 80-85% of the kernel by weight, and is the store-house of
food for the young embryo. The embryo covers only 8-10% of the kernel, and is actually
a dormant young maize plant ready to germinate in a favorable environment. The pedicel
serves as the attachment point of the kernel to the ear.

The cob consists of four distinct parts (Figure 3.3) — the pith. woody ring. course
chaff (tough wood like flakes). and the light chaff. By weight. the woody ring makes up
60.3%. the pith 1.9%. the coarse chaff 33.7%. and the light chaff4.1% (Clark and
Lathrop. 1953).

Cereal seeds. apart from producing new plants. are a main source of food and feed
worldwide. The cereal plant (Large-seeded grasses) to which maize belongs comprises
approximately 90% of all cultivated seeds. Maize provides up to 29% of the world cereal
production (Table 1.2 of Chapter 1) and about 75% of the starch in production (dry

basis).

 

Kernels

Cob

Ears

Figure 3. l Ear—maize, cob, and kernels (FAO, 1982).

 

 

if ALEU A PERICARP
/ A

ENDOSPERM

 

SCUTELLUM

 

EMBRYO

 

 

Figure 3. 2 Schematic of a seed-maize (Adapted from Copeland and McDonald,
2001)

 

 

 

 

Figure 3. 3 I

Kernel

 

Pith
Woody ring
Chaff

Figure 3. 3 X-ray of ear-maize showing its different parts (Islam et al.. 1996)

 

 

 

3.1.1 Hz“
Sl
CncOUlllC 1'
I
mplSlUl’L‘ l

perlbrmetl

hla

 

moisture et
be detennit
splitting tht
no further e
ln s
mixing \titl
significant
l9hlzluget
suitable ‘pra
She
percentage

mMMmeco

3.1.1 Harvesting

Shattering. bird attack. and insect infestation are causes for maize damage
encountered with delayed harvesting (Desai et al.. 1997). Early harvesting (i.e.. high
moisture harvesting) may risk losses due to storage mold growth. if drying is not
performed quickly after harvesting.

Maize can be harvested at physiological maturity: however. at this stage the
moisture content may be as high as 38% (McDonald and Copeland. 1997). Maturity can
be determined by estimating the presence of the so-called ‘black layer‘ (Figure 3.4) by
splitting the kernel from the top to the bottom (FAO. 1982). Once this stage is reached.
no further development occurs. and ear-maize will start drying naturally.

In seed production, male rows are harvested first and kept separate to prevent
mixing with the female ears. Manual harvesting is preferred to avoid damage that may be
significant if harvested mechanically. especially at high moisture content (Airy et al..
1961; Jugenheimer. 1976). For large fields. mechanical pickers or combines are
suitable/practical alternatives.

Shelling at excessively high moisture contents can results in an unacceptable large
percentage of broken kernels; high moisture content seed needs rapid reduction in

moisture contents to below 12.9% to maintain high viability (Wilson et al.. 1996).

11

_‘ Starchy
» endosperm

 

 

 

Embryo

 

Black layer i”

Figure 3. 4 Black layer in maize-kernels (FAO, 1982).

 

. .v‘ll" "

 

 

u eatltef k"

tloii 41)“

3.1.2.1 5"

Stu
countries.
bundles or

the layer is

 

(herdrying

 

season. the 1
119921 intes
contents (211"
and 15 em 1.;

lle t‘c
maximum gr;

has little CIT‘Ct

3.1.2 Drying Methods

Depending on the harvest moisture contents. the locations. and the ambient
weather conditions. seed-maize can be dried by: sun drying. continuous-flow concurrent-

flow drying. and reverse-airflow in-bin drying.

3.1.2.1 Sun Drying

Sun drying (Figure 3.5 and Figure 3.6) ofgrain is still used in developing
countries. The seeds or cars are spread on mats. the ground. or on concrete floors. in
bundles or in bulk. and are exposed to the sun and wind in a thin layer of about 3-6 cm:
the layer is stirred intermittently to obtain a relatively uniform moisture content.
Overdrying and ttnderdrying are common in sun drying. Sun drying depends on the
season. the number ofstirrings. the grain type. and the thickness of the bed. Suhargo
(1992) investigated the sun-drying ofgrains (rice and maize) with four initial moisture
contents (20%. 22%. 24%. and 26%). four grain-layer thicknesses (5 cm. 8 cm. 10 cm.
and 15 cm). and three stirring levels (no stirring. 1 stirring. and 3 stirring).

He recommended for Indonesia a maximum initial moisture content of26%. a
maximum grain-layer thickness of6 — 7 cm. and concluded that the number ofstirrings

has little effect on the overall drying rate.

 

 

figure 3. .

 

 

 

 

Figure 3. 5 Sun drying of maize kernels.

 

flotsam- . .’ '

  

’ . ' i
3......- A -.- . - - .. ‘ g."-
.r.—-_..._,_....._._.-- w .~ ._

 

Figure 3. 6 Sun drying of ear-maize.

 

 

‘1) "‘ .. '-

'4.»
L.
L)
59
A

 

bi
maize. es
structuresl
(r

dimension

 

direction 0
recomment
in 11 C1111 (lg
Velocity, .11
1‘61“ch 3
\vi.
bins nfim C
(1191,14 111
Ah
the Burg bci
mold \ dill]
Mimi/‘0 i

allect llle v

3.1.2.2 Crib Drying

Small maize producers still use cribs (Figure 3.7) for drying and storage ofear-
maize. especially in developing countries. Cribs are usually rectangular slatted
structures. and are normally made of wood or wire mesh.

Cribs are designed to dry ear-maize with natural ventilation. The smaller
dimension (i.e.. the width) — typically 1.5 — 2.5 m (5 — 8 ft) — should be oriented in the
direction of the prevailing wind (Neubauer and Walker. 1961). The FAO (1982)
recommended narrow cribs for areas of high relative humidity; the optimum depth ofears
in a crib depends on the moisture content of the ears. the air temperature. the wind
velocity. and the average wind strength. Generally. the depth of cars in the cribs ranges
between 3 — 4.5 m and the height should not exceed 1 — 1.5 times the width.

Width ofthe crib is important; Shedd (1955) reported spoilage in the center of
bins of over 1.8 m (6 ft) wide in Northern Ohio and Southern Wisconsin. and in bins of
over 2.4 m (8 ft) wide in Missouri. Illinois. and Southern Indiana.

Mechanical picking followed by storage of non-husked ears resulted in 32% of
the ears being damaged due to molding: husked ears showed only 5.6% of the ears being
mold — damaged (USDA. 1952). Removing shelled kernels. httsks. and silks from the
ear-maize is important. as they tend to fill the spaces between the ear-maize and thus

affect the ventilation/drying rate.

v -;
__ d. .. i
T“

P -n' 1‘.

 

 

 

A R E
SLC-E r
dATCd

ntucn
AIR 3.
ran LI
CF 59'

O

TIGH' c
END W:
EvL“h‘

303? or

c:
-»AL an

F“! 0:} t
HEATiNG

 

CORN LEVEL

  
  

AIR ESCAPES THROUGH
SIDE WALLS AND .. .-...
HATCHES

REMOVABLE SLATTED
AIR DUCT ON FLOOR
THROUGH CENTER '
OF DRIVEWAY

TIGHT SIDED
END WALLS

BULKHEAD OR
DOOR USED TO
SEAL DRIVEWAY

FAN OR MR
HEATING UNIT

 

 

 

.1
SHELLING TRENCH J

Figure 3. 7 Rectangular double crib for drying ear-maize (USDA, 1952).

 

 

 

 

210010

 

maize at

ofthe airt
reversal. .

.-\ t

 

 

seeds. In a
second pas
al.. 1993).
Del
seedequalitj
mainly lcxr

metal sheet:

A drying rate of 1% in western Nigeria is achieved in-crib drying ofear-maize at
21% (Comes and Riley. 1962) in a well ventilated crib. Hall (1957) reported that car-

maize at about 20 — 25% moisture content can safely go into crib.

3.1.2.3 Reverse-airflow In-bin Drying

Figure 1.1 shows a reverse-airflow in-bin ear-maize dryer. Changing of direction
of the airflow from upward in the first pass to downward in the second pass is called air-
reversal. A more extensive description of the drying system is given in section 5.1.1.

A double pass in-bin drying system provides protection to the viability ofthe
seeds. In a double pass dryer. a temperature of 40°C -— 43°C can safely be used in the
second pass when the moisture content ofthe seeds is reduced to below 25% (Baker et
aL.1993)

Dekker et al. (1996) evaluated the seed-maize dryer for moisture content and
seed-quality variation. They concluded that unequal depth of cars in the angled bin was
mainly responsible for non-uniform drying of ear-maize. They suggested placing vertical

metal sheets. between the ear-maize. to prevent the cars from sliding down the incline.

3.1.2.4 Continuous-flow Concurrent-flow Drying

Seed-maize can also be dried in high-temperature concurrent—flow dryers
(Lescano. 1986). A schematic of this dryer type (with counter-flow cooler) is shown in
Figure 3.8. In the dryer section. the drying air and the seed flow in the same direction: in
the cooling section. they flow in opposite directions. The desired final moisture content
of the seed is attained by properly manipulating the grain flow and airflow rates along

with the drying-air temperature.

17

GRAIN IN AIR IN

 

HEATER

 

 

 

 

 

——_1

 

CONCURRENT-FLOW DRYING

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DRYING AIR OUT
l r :3?”
lul COOLING AIR OUT
COUNTER-FLOW COOLING
hI
l1
GRAIN OUT AIR IN

Figure 3. 8 Schematic of one stage concurrent-flow dryer with counter-flow cooler

18

 

 

Accordi
gensiIiV~

the sad"

 

OII6“3‘
moisturc
inIcI air IL
aIIous thc

\‘iabiIin (I

3.2 Moch'
I)CC
Iaycrs. Thi:

both In den:

3.2.1 Thin~I.

Thin~
uniform to
exceed three 5
humidity: 1hc

I 998 ).

 

 

According to O’Callaghan et al. (197 I ), concurrent-flow driers are useful for heat
sensitive materials such as seeds, because high inlet-air temperatures can be used without
the seeds reaching high temperatures.

Figure 3.9 shows the results of concurrent-flow drying and counter-flow cooling
of 16 — 30% initial moisture content seed-maize with 149°C air. Note that the 30%
moisture content seed never reached 65°C (due to evaporative cooling), even though the
inlet air temperature was well over 100°C. The inherent evaporative cooling effect
allows the seed to be dried safely in concurrent-flow dryers without affecting seed

viability (Lescano. 1986).

3.2 Modeling of Ear-maize Drying

Deep-bed drying is considered as the drying of a pile consisting of series of thin-
layers. Thin-layer drying is thus the core of deep-bed drying. and the section will discuss

both in detail.

3.2.1 Thin-layer Ear-maize Drying

Thin-layer drying assumes that the whole layer of bed particles is exposed
uniformly to an air stream during drying. The depth (thickness) of the layer should not
exceed three layers of particles. The air-stream should be uniform in temperature and
humidity; the air velocity approaching the product should be at least 0.3 1n 5'1 (ASAE.

1998).

19

 

I rqclumrmlbxmm

 

IE;

nu<<mmg-

A ..

m

H.

3
«this. J.

Figure 3. 9 In

C
I.

dunn

Inlet Air Temperature - 149 C

TEMPERATURE (c)

 

 

 

 

20 _16zu.c. F l l I l I l I
olo 015 do its 2.0 2 4 5 31012141513
TIME(min)

Figure 3. 9 Influence of seed-maize initial moisture content on seed temperature
during concurrent—flow drying and counter-flow cooling (Lescano. 1986).

 

 

 

    

dr} ing car
Shoucd Ll _\
.\I'.i

the drying 1
Idler modc .5
Shay

later ()l‘ CLIF-
QCCUILIIC “‘1'
"Commercial .
"0 >110
The ml’del \\
Ill

oisturc Cum

 

Maize dries entirely within the falling rate periods. because the harvest moisture
content is 25 — 40%. For grains. constant-rate drying only occurs when the moisture
content is sufficiently high to maintain a surface layer of free water (Parry. 1985); for
maize. this occurs at moisture contents over 50%.

Burris and Navratil (1981) used a batch-type experimental thin-layer ear-maize
dryer. Sample moisture contents ranged from 20 — 45%. Temperatures of 35. 40. 45.
50°C. an airflow of‘ll — 128 m3/mz-min (30 ~ 420 cfm/ftz). and a relative humidity of45
:t 5% were used in the study. No significant difference in germination was found in
drying ear-maize at 45°C or below; at 50°C . 25% and above moisture content seed-maize
showed a significant decrease in seed-germination.

Many theoretical. semi-theoretical and empirical models have been proposed for
the drying rate ofthe maize kernels (Brooker et al.. 1992). However. only a few thin-
Iayer models are available in the literature for maize ears.

Sharaf-Eldeen et al. (1980) developed a model for the drying behavior ofa thin-
Iayer of ear-maize. They concluded that the following two-term exponential model is
accurate over the full moisture and temperature range commonly encountered in

commercial ear-maize dryers.

M—M, _ _ .
‘ =Ac k’+(1—A)e 3’” ~

[‘40 — [VIC i
The model was reported to predict the drying behavior of ear-maize to within 1%

moisture content.

21

Ibrc

the 1

 

mai.

CUIh

IC”P

 

than
hlaj
PTCd

“hit.

Seed.
rep. tr
fihnc
dfied
I901p.

Ihdlll

 

Friant (2002) used both field and laboratory data to develop a thin-layer equation
for ear-maize drying. He considered the transient moisture content data collected from
the top during down-air and from the bottom during Lip-air in deep-bed drying of an ear-
maize dryer to represent thin-layer drying data. Combining these data with data
collected from laboratory thin-layer experiments. the author estimated (via non-linear

regression) the parameters for the drying equation:

M—Mc

 

______. : exp(—ktn) (3'2)
MO — Me
where
B .
k = exp(—A(( (Tu/M, )+ D) M0 + ) (3.3)
abs

According to F riant. this model predicts the drying rate of ear-maize more accurately
than the Sharaf-Eldeen equation. especially during the second half of the drying process
in a two pass ear-maize drying system. This model resulted in a standard error of
prediction of0.1 147 for the moisture ratio and 3.8% (dry basis) for the moisture content
while validated against field data.

Whole ear. half ear. and shelled maize samples ofthree hybrids of F 1 -generation
seed-maize were dried by Baker et al. (1991) in a thin-layer laboratory dryer. The
reported findings indicated that shelled maize dried three times faster than ears. The
same authors (1993) commented that seed-maize above 25% moisture content can be
dried at temperatures up to 43°C (1 10°F). and lower moisture seed can even be dried at
temperature up to 49°C (120°F) without germination reduction. Thus. they concluded

that the drying temperatures in a commercial ear-maize dryer can be increased by 5 to

 

 

‘—:¢---a-
-
r.

 

 

 

 

Ct‘

3\

8°C (10 to 15°F) over the conventional 35°C/40.5°C combination without reducing

germination.

3.2.2 Deep-bed Ear-maize Drying

Drying ear-maize is a process ofsimultaneous heat and mass transfer between the
ear-maize and the drying-air. Heat is transferred from the drying-air to the ear-maize.
whereas mass is transferred from the ear-maize to the drying-air. The transfer of heat
enhances the drying rate (i.e.. rate of moisture removal) during artificial drying.

Ear-maize is often dried in a deep bed. The moisture content of the ear-maize at
the top of the bed (ifair flows from bottom to top) may remain at the initial moisture
content level for an extended period. while the ear-maize at the bottom is being dried to
the equilibrium moisture content. In modeling. a deep bed is considered as a series of
thin-layers. The properties of the air and the material are assumed to remain constant
during short time periods. As the air leaves a layer. its humidity and temperature have
changed. The exhaust air enters the next layer. and the process is repeated until the air
exits the last layer.

Models for the deep-bed drying ear-maize have been proposed by Barre et al.
(1985) and by Dekker and Casada (1996). The studies by Barre and associates at Ohio
State University were of a proprietary nature. and thus have not been published in detail
in the open literature.

Dekker and C asada (19%) modified the high-temperature Thompson shelled-
maize drying model C ROSSF LOW (Thompson et al.. 1968) for the in-bin drying of ears

of sweet-maize. They used the equilibrium moisture content (MC) equation (developed

to
b.)

 

 

 

in

It)(

lllt‘

by Henderson. 1952. and modified by Thompson et al.. 1968) originally developed for
shelled-maize. and the Page equation for the thin-layer drying rate (dm/dt) of shelled-
maize. The change in bed-depth was assumed to be a function of the bed-average
moisture loss only. and to be unaffected by settling. Finally. the airflow rate was
assumed to be constant. In evaluating their model. Dekker and Casada concluded that
“the deviation of the (Thompson) model from the real (ear-maize) drying process makes
the model useless to calculate the reversal time and the end of drying."

In 1997. C asada and his associates published a second paper on the modeling of
ear-maize drying ( C asada et al.. 1997). Experimental data were collected on the in-bin
drying of different sweet-maize varieties. Modified relationships for the equations of ear-
maize moisture equilibrium. thin-layer drying. bed-depth change. and airflow rate were
incorporated in the Thompson CROSSFLOW model. Each of these “improved”
equations was based on limited experimental data. The overall average error or the
Casada/Thompson ear-maize drying model was 6.9% (w.b.). They remarked that
“further refinement of the (Thompson) model" is needed before it can become a valuable
tool for the ear-maize dryers. Dekker et al. and Casada et al. did not provide in their

models for the change in the relative humidity of the ambient air.

3.3 Additional Relevant Maize-drying Information

3.3.1 Temperature Effect on Seed Quality

The seed quality of artificially dried ear-maize is a function of air temperature. the

rate of drying. and the initial moisture content. Kiesselbach ( l 939) reported that

 

”'7

 

 

 

prolonged drying of maize at safe temperatures to a moisture content as low as 5% is not
harmful for seed viability. A safe temperature is 41 — ’ 3°C (105 — 1 10°F) when the
initial moisture content is 25 - 35%. but when the initial moisture content approaches
50% the safe temperature is as low as 38°C ( 100°F). No significant viability injury was
found in 26 hybrids ranging in initial moisture content from 16 — 38% dried at 44°C

( 1 12°F). McRostie ( 1949) reported significant seed damage at drying temperatures over
41°C (105°F) when drying maize with initial moisture content over 50%.

Harrison et al. ( 1929) reported that seed-maize dried to less than 10% moisture
content at temperatures of40-45°C (104-1 13°F) was not injured either in viability.
seedling growth. or field performance; maize dried at 50°C (122°F) was damaged. and at
60°C (140°F) had zero percent viability.

Baker et al. (1993) studied four Fl-hybrid maize lots and used two airflow
reversal strategies at a commercial ear-maize bin dryer. In the two tests the air
temperatures before airflow reversal were 35 and 40.5 °C (95 and 105°F) and after air-
llow reversal were 405°C and 46°C (105 and 1 15°F) respectively. No viability decrease
occurred in either in either test.

Burris and Navratil (1981) monitored drying in a conventional air-reversal deep-
bed ear-maize dryer. After 72 hours ofdrying. the ear temperatures throughout the bed
were nearly uniform. but the moisture content ranged from 8" 0 at the top to 12% at the

bottom of the bed. The viability ofthe seed was not affected by the drying process.

3.3.2 Overdrying and Underdrying

UnderdLying and overdrying occurs in 40-55% of the drying runs. [Notez

 

overdrying is considered by the authors to be drying to 1 1.9% or lower; underdrying is
drying to 13% or higher. Drying within the 12.0-12.9% range is rated as “acceptable".]
Figure 3.10 illustrates the considerable degree ofoverdrying and underdrying normally
occurring at two commercial ear-maize drying plants (Precetti. 2001).

Overdrying is costly. because of the unnecessary weight loss and extra energy
consumption. Table 3.1 shows the weight shrinkage and the cost of shrinkage at two
price levels. Overdrying 26.483 MT (one million bushel) by 1.0% moisture causes a
weight loss of297 MT. costing 827.642 at $93/MT and 832.100 at $108/MT.
respectively. Table 3.2 presents the additional energy and energy cost required for
overdrying ear-maize: a reduction of 1.0% below 1 1.9% for 26.483 MT of ear-maize
requires 87.8 MJ/MT extra energy at a cost of$10.960. assuming the natural gas price is

$5 per 1055 MJ.

80.0

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

Q)
3%
70.0 E.
8
3
60.0
.1)
E
S
50.0 g
m 8
E 0
a... 40.0 5
o .
o\° -} 1
30.0 33
C
3 33
:— "O
20.0 9; 5
O
10.0
0.0
Plant A Plant B

Figure 3. 10 Number of underdried and overdried bins at two commercial in-bin
ear-maize drying plants (Precetti, 2001).

Table 3. l Shrinkage in metric tonnes and shrinkage costs when 26,483 MT are

dried below 11.9%.

 

 

 

 

 

 

Overdrying Shrinkage Cost at two selling prices
(%) (MT) ($93/MT) ($108/MT)
0.25 74.9 6.969 8.093
0.50 1494 13.899 16.141
1.00 297.1 27.642 32.101

 

Table 3. 2 Additional energy required per metric tonne and cost (at different energy
prices) resulting from various levels of overdrying of 26,483 MT of 11.9%

 

 

 

 

 

maize.
Natural Gas Cost
Overdrying Energy Required (8/1055 MJ)
(%) (MI/MT) 5 I 7
0.25 22.1 2.770 3.873
0.50 44.1 5.520 7.707
1.00 87.8 10.961 15.262

 

 

 

 

33

\01

m0

.lr

“it

\thc
dh:
\ihc

dn~|

C3110
rcdlll
AX :

The}

3.3.3 Shrinkage during Drying

Ear-maize loses water during drying. and thus the drying bed shrinks. The total
volume shrinkage can be significant.

Boyce (1965) reported the following linear relationship between the average
moisture content and the shrinkage ofa bed of barley during drying:
Ax = ~66.10W + 25.21 (3.4)
with r = 0.94 and w from 0.34 to 0.14 kg kg"

Spencer (1972) proposed a linear relationship for bed shrinkage for drying of
wheat:

dh = D.dM (3.5)
where dh is the change in depth per unit depth (m/m). and M is the moisture content in
dry basis (kg mucr/kg dry mmm).

Lang et al. (1994) developed bed shrinkage equations for drying of wheat and

canola. They also proposed a linear relationship between shrinkage and moisture

reduction as follows:
Ax=0t + }.m Am (3.6)
They ignored bed settlement ((1) in further calculations.

Dekker and C asada (1996) proposed an exponential type relationship between the

depth of maize layer and the length of the drying time.

_.k[n 4
L=LO—D(MO—Mc)(1—e ) (3.7)

 

 

to]

None ofthe above shrinkage equations considered the settlement of the material

in a deep-bed that occurs during the first few hours.

3.3.4 Seed Tempering

Moisture and temperature gradients within individual kernels occurred as a result
ofhigh-temperature drying. This causes stress cracking. and thereby increases the
breakage susceptibility of the seeds.

To reduce stress cracking. seeds are usually tempered. which serves to equalize
the moisture concentration throughout the mass. Tempering in seed drying refers to the
holding of the seed between drying passes.

Pabis and Hall (1961) reported that various parts of an ear of maize dry
differently. The differences in the moisture content are small and decrease with drying
time. Thus. tempering does not serve a practical purpose in a commercial ear-maize

dryer.

3.4 Optimization of Dryer Design

Optimization is an essential tool in post-harvest and food process engineering for
yielding the best product through optimum operation of the processing variables.
Optimization of the ear-maize drying process has not yet been researched.

The complex optimization technique is a modified simplex method for finding

constrained optima (Box and Swann. 1969). Other iterative optimization techniques use

 

Ill:

alternative starting points. and need to satisfy all constraints; they differ substantially in
searching for the global optimum and require searches with different starting points to
ensure that the true global optimum is found. The complex method spans the total
feasible region in a multidimensional space by the initial set of random points. The
method evaluates the objective function values at each point and eliminates the most
undesirable point by the new feasible point. In this process ofelimination. it finds the
global optimum using the same initial point.

Liu (1998) developed a grain quality model that optimizes the drying air
temperature to produce acceptable and uniform grain quality. The optimized air
temperature varied with the inlet grain moisture content and the desired grain quality.
When the inlet moisture content was changed front 20 to 30% (w.b.). the new optimized
temperature was lowered by 30°C.

Schoenau et al. (1995) investigated the optimization of in-bin drying ofcanola by
using a computer simulation model and typical weather data for a prairie location of
North America. They optimized the drying system by considering the total annual cost of
a drying system within set bounds of drying time (:1 15 days) and spoilage index (SI <
1.0). They found that continuous fan operation with 1.5 — 2 mi/min tonne of ambient air
and 9 — 26 MJ/tonne of fan energy was best to dry canola at 19% initial moisture content
or less in August.

Trelea (1997) developed a predictive online control for on-line control ofa maize
batch drying process. An optimal command profile was computed offline using the
complex constrained optimization procedure on-Iine taking into account feedback

measurements. The control was implemented on a pilot-scale maize dryer that allowed

...k .- . m—‘fi. 4'0 5..
I .
.

n

 

air tcn‘
bathe:
The} 11
p10t‘t‘5\

COIISl‘le'

uptimi/U
method 1
milk. .\1

control p.

 

 

air temperatures between 20 and 120°C. velocities 0 and 5 m/s. relative humidities
between ambient (4 — 7%) and 100%. The product layer could be up to 0.5 m thick.
They found that their optimal off-line solution gave a 3.4% decrease for the total
processing time and a 4.1% decrease for the total fuel consumption over the profile with
constant temperature.

Lee and Park (1989) successfully applied the complex method for the quality-
optimization of red pepper dryer. Arteaga et a1. ( 1994) used the same optimization
method to obtain the optimal time-temperature combination for the heat treatment of
milk. Mishkin et al. (1984) applied this method to find the optimal dryer-temperature

control paths for minimizing ascorbic acid loss in the drying of potato disks.

'vJ
l J

 

to

6&1

4. THEORY

4.1 Modeling of In-bin Reverse-airflow Ear-maize Dryers

Drying of ear-maize in a fixed-bed dryer is accomplished by blowing heated air
through the pile of ear-maize stored in a bin. Heated air comes in contact with the ears.
transfers heat and carries the evaporated moisture from the ears. A drying zone
establishes at the entrance of the air and moves slowly through the bed of ears to the air
exit. Thus. drying is a process of simultaneous heat and mass transfer.

The ear-maize below the drying zone reaches temperature and moisture content
equilibrium with the drying air — 0c and M... respectively. The ears above the drying zone
remain at the initial moisture content (Mo). while slowly reaching the wet-bulb

temperature. Thus. a moisture gradient across the drying zone is established.

4.1.1 Conventional Ear-maize Dryer Design

A schematic diagram of a conventional two-pass in-bin ear-maize dryer is shown
in Figure 4.1. The dryer has several doors for controlling the airflow direction through
the ear-maize. Heated air blows through door-7 in the down-air mode through the
partially dried ears. The down-air exhaust is forced through door-6 to the lower plenum.
where it is mixed with some heated air that enters through door 5. From the lower
plenum. the air mixture flows to the up-air bin. where it is channeled upwards through the
wet ears. The up-air leaves the dryer through door-3. which is also used for loading the

ear-maize in the bin. Door-9 is for unloading the dried ears on a conveyor belt.

b.)
DJ

 

n2

 

 

  

Upper air plenum ' '

5

 

 

 

 

 

 

 

 

 

Lower air plenum.

 

 

 

 

Up-air

 

 

Figure 4. 1 Schematic diagram of a conventional ear-maize dryer.

34

 

 

 

 

The plan ofa complete conventional dryer building with burner cab (for heating
the air at entrance) is shown in Figure 4.2. A dryer building typically consists of 24 bins.
12 on each side. with a long air plenum in the middle. The figure also shows a hunter

cab. which houses heater and fan to heat and force the air to the upper plenums.

 

 

 

Flllure.

 

Burner cab

 

Bin 1

 

Bin 2

 

Bin 3

 

Bin 4

 

Bin 5

 

Bin 6

 

Bin 7

 

Bin 8

 

Bin 9

 

Bin 10

 

Binll

 

 

Bin 12

 

Air plenum

Bin 13

 

Bin 14

 

Bin 15

 

Bin 16

 

Bin 17

 

Bin 18

 

Bin 19

 

Bin 20

 

Bin 21

 

 

Bin 22

 

Bin 23

 

 

Bin 24

 

 

 

36

Figure 4. 2 Plan of a conventional ear-maize dryer with 24 bins.

4.1.2 Hunter Design

Besides the conventional ear-maize dryer. the recently patented Hunter design is
considered for optimization. The design. Patent number 5.893.218 (Figure 4.3). is a
modification of the conventional ear-maize dryer. The new design provides better
control over the drying operation. A vertically oriented air-mixing chamber is
sandwiched between the air plenum and the dryer bin. and improves the control ofair
conditions. The chamber draws hot air (through: door-1 or door-9) from the upper
plenum and cold air (through: door-2 or door-10) from the lower plenum. mixes the two
airflow and produces drying-air at the desired temperature. The air mixture enters the
bin in the up-air mode through door-3. moves through the ear-maize. and leaves the dryer
through door-6. The drying air enters through door-13. moves downward through the
ear-maize. and exits to the environment through door-l4. Doors-5 and 13 are for loading

the dryer. and doors-7 and 15 are for unloading.

 

 

_
I3 I 5
. ‘_ _ __ Upper .
14 Down-air 1 I 4 Up-atr 6

Plenum

 

 

    

 

 

 

 

I. .1. 'J':
l_" .' q PB. ‘
I. 0“... 0.. ",..0 .
a. - . .
I . _ . "0'1 .
10 2 *',_I'--'- .1
u '0
Lower " , '4 '4.
I aw
-.—‘. Oi .
Plenum I \{l 7
I
Mixing Chamber I 8

 

Figure 4. 3 Schematic diagram of the Hunter dryer.

b.)
00

 

 

til

4.2 Development of the Model Equations

Based on the fundamental laws ofsimultaneous heat and mass transfer. Bakker-
Arkema et al. (1974) at Michigan State University (MSU) developed models both for
stationary and continuous-flow grain drying systems — the so called MSU drying models.
The models are based on three partial differential equations and one thin-layer drying rate

equation. The MSU stationary-bed drying model has the following form:

 

 

 

 

3T - l.
_‘ = “f (T—()) (4.1)
EX G c + (1 C H
a a a it
It . +(' (T —6))
7 l. r , .
551 = 7" T] (T — 60+ 4‘ ‘ V a 1:, (4.2)
o ' + - .r / - + , '- ax
pg L (g p}: L “i [)(g L (1,, /)(g L H.
OH pg 6T4— .
—" = ‘7— (4.3)
8x (1 (71
a
6M . . . . . . .
7 = th1n layer equation (F riant. 2002 equation IS used in this study) (4.4)
(‘t'

The boundary conditions are:
T (0. t) = T (inlet)
0 (x. 0) = 0 (initial)
H (0. t) = H (inlet)
Hot. 0) = M (initial)
The assumptions made in the development of the MSU deep-bed drying model
are (Brooker et al.. 1992):

(1) negligible bed shrinkage
(2) uniform initial ear temperature and moisture content

39

 

innit

 

 

(3) no kemel-to-kcrnel heat conduction

(4) plug type airflow
(5) adiabatic dryer walls with negligible heat capacity
(6) constant properties of the grain and the drying air.

Assumption ( l ) is not valid for ear-maize drying. because significant shrinkage occurs in

the in-bin ear-maize drying process. Thus a shrinkage equation must be included with

the MSU stationary-bed drying model.

4.2.1 Shrinkage Model

The author observed that bed shrinkage during the first five hours in the ear-maize
drying process is due to the cumulative effects of the moisture loss and the settling of the
bed. After about five hours of drying. the shrinkage is mainly due to the moisture loss of

the ears. Therefore. the following linear bed-shrinkage equations were used in this study:

8’ =B( M0 —.«l'I’) + .4 + ( [.01]? for t 3 5h (4.5a)

S, =B(M0—MI)+A+D fort>5h (4.5b)

where D = 5 C Lo. L0 is the initial depth (m). and A. B. C are constants.

4.2.2 Thin-layer Drying Equation

A deep-bed can be considered as a pile ofthin-layers. The drying rate of an

individual layer of ear-maize can be expressed by (Friant. 2002):

 

1‘ —j‘/ ) ‘-
__.__’ I‘ :exp|:—kl()'ggb ] (4.6)
.1] —/’VI
0 e
. , 7947
where k = exp —-28.66 + 0.2744T + (—86.0032) M + (4.7)
abs 0 T

abs

with the standard error of prediction for the moisture ratio being 3.8% (d.b.).

40

 

the

 

 

 

JUIIh
li‘fet

1:111):

4.2.3 Equilibrium Moisture Content

The deep-bed model contains a value for the equilibrium moisture content (ML) of

the product to be dried. Sharaf-Eldeen (1980) proposed the following equation for the Mc

of ear-maize:
0.55
M. = 5.69 ill—’1) (4.8)
f
abs

Friant (2002) reported the accuracy of this equation to be i: 0.54% (d.b.).

4.2.4 Psychrometric Air Properties

The deep-bed drying model needs the psychrometrie properties of the drying air
in order to calculate the air temperature and humidity in the deep-bed of ear-maize. The
psychrometrie properties of the drying-air are expressed in several thermodynamic terms:
the humidity terms (1 ) vapor pressure. (2) relative humidity. and (3) humidity ratio; the
temperature terms (1) dry-bulb. (2) dew point. and (3) wet-bulb temperatures: the
enthalpy. and the specific volume. Lerew (1972) programmed the psychrometrie
equations for the above quantities. The program calculates psychrometrie property
values if any two property values and the atmospheric pressure are known. Several
authors have verified (Brooker et al.. 1992) and in some cases updated (F riant. 2002) the

Lerew psychrometrie equations. In this study. the Lerew equations as updated by Friant

2 002) are used. The following sections repeat those equations.

41

 

4.2.4.1 Vapor Pressure Equation

The partial pressure exerted by the water vapor molecules in moist air is vapor

pressure (P.) . Vapor pressure of air fully saturated with water vapor is called the

saturated vapor pressure (P..):

 

I P... _ A + BT + ("T2 + [373 + £74

“(7;— 7 _ e. .2 (4.9)
FT—OY

where

273.16 < T(K) :- 533.16

R=2.2105847380X107 D=1.255753189X10'4

A = -2.740552583610 x 104 E = 4.85017 x 10'8

13 = 9.754129373 x 10‘ F = 4.349028978 x 10"

C = - 1.46244 X 10'I G 2‘ 3.938107171X10'3

4.2.4.2 Relative Humidity Equation

Relative humidity is the ratio of the vapor pressure (1)..) of water vapor in the air

to the saturated vapor pressure (P...) at the same temperature and atmospheric pressure:

(4.10)

v

 

1‘11 =
my

Fhe value of relative humidity is used in equation 4.4.

42

 

 

 

 

til

4.2.4.3 Humidity Ratio Equation
The humidity ratio (H) is also known as the absolute humidity or the specific

humidity; it is defined as mass of water vapor in the moist air per unit mass of dry air:

rh I".
[720.6219() ‘1 ) (4.11)

- r 1 I’
aim 1's

 

4.2.4.4 Dry-bulb Temperature
The dry-bulb temperature is the temperature indicated by an ordinary

thermometer.

4.2.4.5 Wet-bulb Temperature Equation
The wet-bulb temperature (TM) is the temperature measured by a thermometer
whose bulb is covered with a wet wick. The velocity of the airflow passing over the wick

should be at least 4.6 m/s (Brooker et al.. 1992). The T“), can be calculated by:

 

1
7’”, : —_'(vati'b -Pv) + T (4-12)
B
P?
1.006.9254tl’ —I’ )(1+0.15577 .\_)
, l’Sll‘h arm p
where B = "’m (4.13)
0.62194 11 fig

4.2.4.6 Enthalpy Equation
The enthalpy of moist air is the heat content per unit ofdry air above a certain

'eference temperature. Enthalpy of moist air is given by (Brooker et al.. 1992):

h = 1.006.9T + W[2.512.131.0 +1.552.4T] for 0 <_< T (°C) 3 100 (4.14)

 

4.2.4.7 Specific Volume Equation

The specific volume (v) is the volume per unit mass ofdry air: the specific

density is the reciprocal of the specific volume:
(4.15)

v = 1.205 + 0.00475 (20 - Ta)

 

4.2.5 Other Parameter Values

In;

4.2.5.1 Physical/Parameter Values for Air, Ear-maize, At, and Ax

The physical/parameter values for air. physical parameters of ear-maize. At. and
Ax are given in Table 4.1. The values of Ax and At are the maximum layer depth (Ax)
and the maximum time step (At) that guarantee that the model is stable. At larger values

ofeither ofthese quantities. the differential-equation model becomes unstable. The

model runs at values of At = 0.05 h and Ax = L/N regardless ofthe other input parameter
values within the domain of interest for this study. At smaller values of Ax and /or At. the
simulated results are practically the same but require considerably longer computing

times. The value ofN (=100) was found empirically to serve well the purpose of the

selection of Ax.

44

Table 4. 1 At, Ax, and physical property values of air and ear-maize used in the

simulation model.

 

 

Parameter Units Value Source
At 11 0.05 Author
Ax 111 Equation 4.21
t“. kg m'3 901 ASAE (2001)
Pg k8 111-3 I (1.11.) Brooker et al.
it 100 (1992)
c. k1 kg'l K" 1.465 Brooker et al.
(1992)
eV kl kg'I K" 1.88 Brooker et al.
(1992)
c. k1 kg'| K" 1.0007 Brooker et a1.
(1992)
C.“ U kg'l K I 5 0 Brooker et al.
(1992)
h w m2 K" 1.35636 s (G..)“~“’ (0.0006 (1.72 + Liu (2002)
000463 T.) / d) "4'
p. kg m'-‘ 1.205 + 0.00475 (20 - T.) Liu (2002)
ht'g kJ/kg (2544-132586”) (1+435 exp(-28.25M)) Brooker et al-
c

( 1992)

 

l

 

 

 

4.2.5.2 Airflow Resistance Equation

The static pressure was measured during the experiments. The data were used to
calculate the airflow rate. i.e.. the mass ariflow rate through the ear-maize bed. using the
Shedd (I953) equation:
4

AP _ 1.04.1110 Q2
L 111(1 + 325Q)

 

(4.16)

where a = 1.04 x 104.1110 b = 325 (ASAE. 2001)

4.2.6 Numerical Solution of the Model
The forward-difference method was used to solve (simultaneously) equations 4.1

— 4.8. Thus.

— liu(TxJ —61 )

.‘CJ
T = .. '
.x + AX.’ (I , (C + C Vl/ )
(l (I l’ X.’

 

Ar + qut (4-17)

ha(TxJ _0x.t )_lh_/.il + cv (T31! _9x.l )](1u(AH/Ar)

 

t9 = A! + 61 (4.18
.X-g’ + A, p (L. + C A), ) -th )
g g 11' xx!

,0 if Ail/I AI

 

”Ham:— G A, +Hx.t (4:19)
a
= -1 .2
ill x.t+At' [(MR) "”6110 WC)]+MC (4 0)
L
N
where N = 100
46

I

 

L,=L0—-[B(.1I0—.‘l’l’) +A+C [501/2]

L, :1. —[B(M

(during the first 5 hours ofdrying) (4.22)

— M, )+ A + D] (after 5 hours) (4.23)

() 0

The model equations above have five unknowns. namely T. 0. W. M. and L. The

boundary conditions are the initial temperature and moisture content of the ear-maize. the

inlet temperature and humidity ratio of the drying air. and the initial depth of the bed of

ear-maize.

The computer program was written in FORTRAN-IV (The flow diagram of the

program is shown in Figure 4.4).

The program follows the following calculation scheme after the first step:

to

DJ

6.

initializes the bed conditions

calculates Ax using equation (4.21)

calculates ear-maize moisture content using equation (4.20)

calculates the air humidity using equation (4.19)

calculates the air temperature and ear-maize temperature using equations (4.17)
and (4.18). respectively

calculates the average ear-maize moisture content throughout the bed

calculates the new bed depth using equation (4.22 or 4.23) and thus new air flow

rate using equation (4.16).

stops the execution when the desired final moisture content of the ear-maize

reached.

47

 

 

Yes

m . Calculate Depth
1

No

- Reset Grid
Input
Reverse air-flow

I Unit Conversion é
‘

Use after reversal Ta (d.b.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Get Property Values & w.b.)
(ca.cv.rhoa.cg.c....hfg) 4 O
‘2 B
Calculate Depth
I Setup Grid ¢
Reset Grid
[ Initialize Values l

 

 

 

@ Calculate Air-flow & Ear-maize Density

 

 

 

1

Calculate Air-flow
& Ear-maize Calculate
Density (Tg. GMC. T... H.)

 

 

 

 

 

 

 

 

 

 

7
Calculate
(Tg. GMC. T... H.)

 

 

 

 

 

 

 

7

 

 

GMC: Ear-maize moisture content. RMC: Reversal
moisture content. F MC: Final moisture content

igure 4. 4 Flow diagram of the computer program for fixed-bed ear-maize dryer.

48

 

 

. Q7 —-. _-_.

d1

4.3 Optimization

Optimization is the selection of values of the control variables within a certain
range that will result in the desired maximum or minimum function value. The problem
takes the following form:

Maximize or minimize: Objective function

By proper manipulation of: Control variables

Subject to: Constraints.

Many techniques are available for solving optimization problems. Optimizing the
performance index (objective function) in an ear-maize drying is a multidimensional
problem. A multidimensional optimization problem can be solved either by using any of
the single variable search techniques or by using a multivariable search technique. Single
variable search techniques were not considered in this study. because the effectiveness of
'one-at-a-time‘ (alternating variable search) techniques "are restricted to cases where no

interaction of the independent variables exists" (Saguy. 1983). This is not the case for
seed-maize drying.

Among the multidimensional optimization techniques. the complex method (Box.

1969) is "one of the promising methods of direct search for finite-space problems. is
simple and offers easy handling of various inequality constraints" (U meda et al.. 1972).
The method is widely used for constrained optimizations. variational problems. and
derivations of optimal temperature profiles (Saguy. 1983).

The complex method generates an initial "complex“ ofk 2 n + 1 points that
includes a feasible starting point and an additional k — 1 points. one for each ofthe

independent variables. The additional points are found as follows:

49

 

Xi.1=Gi+ri.i(Hi-Gi) (434)
where i = l. 2.11 and j = l. 2. k-l
r1.) random numbers between 0 and 1
k number of points in the complex
11 number of independent variables.
The constraints are assumed to be inequalities of the form:

kaxkillk k: I2. ....111

where the implicit variables X n 4 1, X m are functions ofthe explicit independent
variables X (_ X n. The upper and lower constraints H k and G 1.. are either constants or

functions of the independent variables.

The selected points must satisfy the constraints. If explicit constraints are
violated. the point is moved a distance ‘5‘ inside the violated limit. If an implicit
constraint is violated. the point is moved one half of the distance to the centroid of the
remaining points. Thus.

X Li (new) = (X (j (old) + X 1C )/2 (4.25)

i= 1. 2. 11

where the location of the centroid (Xf) of the remaining points is computed by:

k
X." = [ Z X. .—.\'1. [(0/11)] 1

1
I k—l 1:1 1.1

l. 2.11. (4.26)

This process is repeated until all the implicit constraints are satisfied. The objective
function is evaluated at each ofthe points. and the vertex of the greatest function value is
determined. The point with the worst function value is replaced by a new point which is

found by:

U)
0

 

i 1 ' : C — . I C 7
ll 1.2/(mu) a’ [Xi Xi j (n/d)]+Ai (4...7)

q

i=1. 2.11
where 01 is the reflection coefficient (a value of 1.3 is recommended by Box et al.. 1969).
Iftlie point repeats in producing the worst value on consecutive trials. a move
halfway towards the centroid of the remaining points is made instead ofthe basic
iteration of over reflection. Convergence is assumed when the objective function values
at each point are within B units for y consecutive iterations. Usually. k = 211 is chosen to
ensure that the polyhedron (which contains 211 vertices) will not collapse or flatten near

the constraints.

4.3.1 Capacity Optimization
The capacity objective function was calculated from the drying time. the test
weight (TW) ofthe wet ear-maize and the volume dried. The test weight of the ears is
defined as the weight of0.03524 m3 (i.e.. 1 bushel or 1.244454 f‘t‘l) of wet ears. The
capacity or throughput is:
Capacity (tonnes m'2 h") = weight of wet ear-maize (tonnes/1113)

X total time ofdrying (h) (4.28)

4.3.2 Cost Minimization
The cost objective function has two components: (1) the cost associated with fuel
nergy for heating the drying air. and (2) the cost associated with the electrical energy for

1e fan. which forces air through the bed of ear-maize.

51

 

4.3.2.1 Fuel Energy
In a traditional two pass ear-maize dryer. the ambient air is heated and blown
downward (down-air) through the bed in the second pass; the exhaust air from the second
pass is blown upward (up-air) in the first pass. Thus. depending on the mass of up-air

relative to the mass ofdown-air. there are three different scenarios for calculating the fuel

energy requirements in ear-maize drying (Figure 4.5): case I: the mass of up-air is more

than the mass ofdown-air. case 2: the mass of up-air is less than the mass of down-air.
and case 3: the mass of up-air is equal to the mass of down-air. In case 3. the mass of air
heated for the down—air pass is enough to supply the mass ofair for the up-air pass. In

case 2. more air is heated during the down-air pass than is needed for the up-air pass; the

excess air from this case is used to supply energy in case 1. In case 1. the air mass heated
in the down-air pass is less than is required for the up-air pass.
The energy (kl) required for the three different cases is calculated using:
El”, 2 Gtup [(Ca Tu], + (lint. + Cy Tup) H1) - (Cu T111111) + (1111:. + CV Tamh) H())] (4.29)
Em...” = GM..." [(ca Tm..." + (ht-g + c.T.1.,....) HI) - (c.l T... + (ht-g + c... T...) [1(1)] (4.30)
The energy (kl) required for ear-maize drying in a llunler design (Figure 4.6) is
calculated from:
E..,, = Gmp [(c.| Tup + (hfg + cV Tl...) H1) - (c2| Tun... + (111g + c. Tumh) H())] (4.31)
Edown : thown [(Ca Tdown + (big + chdown) H1) ' (Ca Tamh + (big + Cv Tamb) HUI] (432)
The total energy (kl) is the sum of the up-air energy and the down-air energy:

‘

31“ : Eur) + Ednwn (4.33)

 

Temperature (°C )

A

d
2H case

Extra heating

Heat from exhaust of

Heat from down
air exhaust

 

Self heating

 

 

<_______

h — — — — 1
Extra heating
Heat from down

air exhaust

Up-Air

 

 

<— Up-Air 9

Extra heat

<— Down-Air ->

 

 

_--—-----‘

ing

Heat from down-air exhaust

 

é- Up-Air

 

 

 

\
/

 

 

 

Selfheating
Down-Air >
Self heating
< Down-Air ____>

(Case 1)

(Case 2)

(Case 3)

> Time (h)

 

— Ambient temperature

- - - - - Down-air temperature

Up-air temperature
Exhaust temperature

Figure 4. 5 Energy diagram for a conventional ear-maize dryer in three different

cases (Top: up-air > down-air, Middle: up-air < down-air, Bottom: up-air =

down-air).

53

Temperature (°C)
A

-i

 

 

{- Up-air

Time (h)

 

 

 

_ Ambient temperature

/ Down-air a.

V
l

- - Up-air temperature

. . . . . Down-air temperature

Figure 4. 6 Energy diagram for a Hunter ear-maize dryer.

54

 

fir .- —F§:T ‘

 

The fuel component ofthe cost function is calculated from the price of the fuel

( V1). the total fuel energy (Emmi). and the thermal efficiency (1111mm):

1 .E .

Fuelcost = ‘f f (4.34)
’7

 

I/icrm

4.3.2.2 Electrical Energy
The brake horsepower required per square foot of the bin area for the drying fan is

(Brooker et al.. 1992):

air delivery(cfm) X static pressure (in.)

 

Brake Horse Power = _ (4.35)
6330
or
Efan = [airdelivery(m3 /s) X Static pressure(Pa)Xdryingtime(h)] /1000 (4.36)

where E“... is the fan energy in kWh.
The electrical component of the cost function is calculated from the price per unit
ofelectricity (V.). the electrical energy consumption (1311...). the motor efficiency (11m). and

the fan efficiency (11m):

lye Elem
Electricilycost = ——'—— (4-37)

”m ”fan

3.2.3 Total Energy Cost
The total energy cost is calculated by summing the fuel energy and the electric
-rgy costs. The cost minimization objective function thus has the following form:

a] cost = Fuel cost+ Electricity cost (4.38)

55

’._

 

4.3.3 Constraints

dryer:

1.

1‘s)

9.)

The following constraints are pertinent in the modeling ofthe fixed-bed ear-maize

Depth:
Upper limit: 3.4 111 ( 1 1 ft). the maximum depth ofbins used for ear-maize drying.
Lower limit: 2.4 m (8 ft). selected for practical reasons.

Up-air temperature: [Baker et al.. 1993]

 

Upper limit: 405°C (105°F)
Lower limit: 35°C (95°F)

Down-air temperature: [Baker et al.. 1993]

 

Upper limit: 46°C (105°F)
Lower limit: 405°C (95°F)

U p-air and down-air pressure (Precetti. 2001):

 

Upper limit: 746 Pascal (3.0 inches of water)

Lower limit: 249 Pascal (1.0 inch of water)

(Note: For the conventional design. the air pressure of both up-air and down-air
are the same: they can vary for the Hunter design).

Reversal moisture content (Precetti. 2001 ):

 

Conventional design:
Upper limit: 0.42 M11+ 7.5 (found empirically that serves the purpose expressed in
the note below)

Lower limit: 15.5%

56

I

6. Final average bed moisture content: 12.5%

I lunler design:

Upper limit: 22% (Reversal moisture content (RMC) of22% was chosen for

practical reason. higher RMC would have brought more water inside the

lower chamber. and lower RMC would have decreased the capacity.)

Lower limit: 15.5%

(Note: The commercial seed industry follows the 60/40 rule of thumb for air-

reversal: i.e.. the up-air time should at least be 60% ofthe total drying time —

Precetti. 2001 ).

4.3.4 Mathematical Representation of the Optimization Problem

The problem of maximizing capacity or minimizing cost of drying ear-maize is

summarized below mathematically:

M'dX CBPaClt)’ (l... T11p- Tdowna I)up« Pdown~ Mrew M1) : Fcap

01'

Mill C051 ”-4- Ttip- Tdowth Pup- Pdoun- Mich IVll) :

Subject to:

#
0

is)

b)

depth (L). m

Up-air temperature (Tup). °C
Down-air temperature (Tamm). °C
U p-air pressure (Pup). Pa

Down-air pressure (Pdmm). Pa

FUN
2 4 <1 L “’ 3 4
355145405

: 40.5 < Tdm... 5 46

2249::ijg746

2249:3ngg746

(4.39)

(44»

6. Reversal moisture content (M .1). %
ru

Conventional design : 17.5 S MW. 5 0.42 M. + 7.5
Hunter design (See Chapter 5) : 17.5 5 Mr... S 22
7. Final moisture content (Mr). % : Mr S 12.5

4.3.5 Flow Diagram of Optimization Program

Figure 4.7 shows the flow diagram for the optimization of the ear-maize

58

< Start >

V

 

 

s/

pecify number and
limits of the constraints

@cify initial point

V

 

 

 

 

 

 

 

 

 

Establish initial “complex”
points (scattered
throughout feasible region)

 

 

1

 

 
  

Move distance 5

1 inside violated —@

limit

  
 

Satisfy
constraint

  

 

 

 

   

 

Call simulation model

(calculate function ‘__®
value)

Find points with
highest and lowest
function value

 

 

 

   

Converged

 

  
   

 

 

 

 

 

 

 

Move half distance
towards centroid of the
remaining points

 

 
 

Repeats
highest
value

 

Reject point with
worst function value

 
 

 

 

 

 

  

 

 

Yes

 

 

 

Construct new point

0)

Figure 4. 7 Flow diagram of the computer program for optimizing the fixed-bed ear-
maize drying process.

 

 

 

 

59

5. EXPERIMENTAL

5.1 Overview

For the validation of the ear-maize dryer simulation model. experimental data
were collected from a conventional commercial ear-maize dryer at Constantine.
Michigan. from September 4. 2001 to October 16. 2001. These data were collected
during commercial operation of a dryer facility. Because of the importance of facility
throughput and the high value of the product, it was not feasible to manipulate the control
variables of the facility solely for the purpose of this study. Therefore. data were
collected from 24 drying runs (selected merely by "opportunity"). with the goal of
assembling a data set that represented the entire range of values for input and output

parameters that might be encountered in typical commercial operations.

Transient data for the kernel moisture content and temperatures were collected to
validate the corresponding outputs from the simulation model. The transient data for the
change in bed-depth were collected to develop a shrinkage equation to be used in the
simulation model. The total data sets collected (for bed-depth change) were separated
randomly into a calibration set and a validation set. The validation data set was used to

determine the prediction error of the shrinkage equation.

60

5.2 Data Collected

The following data were taken during each drying test at Constantine:

(I) the initial and final average moisture contents of the ears in the drying
bed.

(2) the (local) moisture content of the ears at three locations at top and three
locations at the bottom of the bed during the drying process.

(3) the dry-bulb and wet-bulb temperatures of the ambient air. of the bin
inlet/outlet air. and of the air at several locations within the drying bed.

(4) the initial and changes in bed-depth.

( 5) the static pressure of the up-air and down—air.

(6) the time of air reversal. and

(7) the time when drying was stopped.

5.2.1 Moisture Profiling

To determine the initial bed-average moisture content. 10 samples were randomly
selected during loading. The kernels were stripped of the ears. and the moisture content
was measured using a Dickey-John GAC 2000 (Dickey-John C orp.. Auburn. IL) moisture
meter. which was calibrated (R2 = 099%) against oven moisture content determinations
(ASAE 2001) for 100 samples. The final bed-average moisture content was the moisture
content averaged and recorded in the sheller after shelling the ear-corn of the whole bin.

To monitor corn moisture content at the top of the bed. samples (36 ears) were
randomly grabbed during loading of the bin and placed in perforated plastic mesh bags.

These bags were placed on top of the drying bed at three locations. Every 8 h. the bags

61

a"

were pulled from the bed. three ears were sampled from each bag. and the bags were
returned to the original locations on top of the bed. Samples from the bottom of the
drying bed were collected at three of four unloading doors. Kernels were then stripped
and moisture content determined via the GAC 2000. The moisture content from the three
samples was averaged for each sample time and location.

(A [I moisture content data reported in this thesis are the oven-adjusted kernel moisture

content. )

5.2.2 Temperature Profiling

The ambient air conditions were read from Schmidt and Waite maps (ASAE.
2001). It may introduce error in the calculation ofenergy cost but negligible or no error
in the calculation of the drying time. because the humidity of the drying air was known.
The bin inlet drying air conditions were collected by the seed company.

To monitor the temperature profile at several locations within the drying bed.
three bins were used. A chain was stretched from the top to the bottom ofa bin. Three
temperature sensors (TMC20-HA. Onset Computer Corporation. Pocasset. MA) were
attached to the chain at 0.6. 1.2. and 1.8 m (2. 4. and 6 ft) from the bottom of the bin.
The other ends ofthe sensors were connected to a data logger (HOBO H8
outdoor/industrial 4-channel external logger. Onset Computer Corporation. Pocasset.
MA). The data logger recorded temperatures every half-hour throughout a drying run.
The data were transferred from the data logger to a computer disk using a shuttle (HOBO

shuttle. Onset Computer Corporation. Pocasset. MA) for further analysis. Experimental

62

setup of the sensors is shown in Figure 5.1; the specification and operating parameters

can be found in Appendix B.

. Rope with.
Plumb'bob

 

Chain
Temperature sensor

 

Figure 5. 1 Data logger with temperature sensors looking down from the top of a
drying bin.

63

5.2.3 Bed-Depth Change

The bed-depth was measured and recorded during seven tests using a plumb—bob
and pulley system (Figure 5.1). Three plumb bobs were hung from the roof of one bin.
At 8 h intervals. the plumb bob was lowered to the bed surface. and the length of the rope
holding the plump bob was measured. Three records from three points were averaged to
get the average bed-depth change during each time period.

The static pressure of the up-air and down-air were collected by the seed

company.

5.2.4 Ear versus Kernel Moisture

The term M in equations (3.1) to (3.4) refers to the moisture content (d.b.) of ear-
maize. Because the experimental moisture content data refers to the kernel moisture. the
relationship of ear versus kernel moisture content has to be known. Figure 5.2 shows this
relationship. The data were provided by Precetti (2001). The relationship can be
expressed by the following regression equation ( R2 = 0.9843):

MC..." = - 0.0126 MC”... 2 + 1.7644 Mthmcl - 6.1609 (5.1)

where moisture content (% w.b.).

64

P

60

 

  
 

 

 

 

0

a: 50
8
E 40
8
e
:3: 30
O
E
.D
3 20 j
g y = -0.0122x- + 1.7461x - 6.0021
'5 83:0.9875
2 10

0

0 10 20 30 40 50 60

Kernel moisture content (%)

Figure 5. 2 Relationship between the ear-maize moisture content and the maize-
kernel moisture content (Precetti, 2001).

65

5.3 Viability Tests

Seeds collected during drying experiments were stored at 4.4°C (40°F). The
seeds were tested for viability in a warm germination test and in a cold germination test.
The tests were conducted at the Michigan Crop Improvement Association laboratory at
Lansing. Michigan.

In the warm-germination test. four replicates each of 100 seeds were wrapped in a
wetted paper towel. rolled up and placed in an environmental chamber at 25°C (77°F).
After four days. the number of normal seedlings were counted and removed from the
towel; the towels with remaining seeds were returned to the chamber. After seven days
the final count of the normal seedlings were made. The warm-germination percentage
was determined from the sum of the two counts (AOSA. 1978).

In the cold-germination test, four replicates each of 100 seeds were placed in trays
containing wetted (70% water capacity) soil. The trays were placed in a 10°C (50°F)
dark chamber for seven days and subsequently in an environmental chamber for another
seven days. The number of normal seedlings were counted. and the cold-germination

percentage was calculated (AOSA. I983).

66

 

 

6. RESULTS AND DISCUSSION

This chapter includes three main sections: (1) the experimental results, (2) the
validation of the ear-maize drying model. and (3) the optimization of the drying process

with respect to dryer capacity and cost.

6.1 Experimental Results

The experimental drying data of 24 in-bin drying tests are summarized in Table

6.1. The initial moisture content varied between 28.6% and 37.7%; the initial bed-depth

between 2.1 m and 3.4 m (7.0 and 11.0 ft); and the reversal time between 36.4 and 64.8 h.

The up-air drying temperature was 36 — 38°C (96 — 100°F), and the down-air temperature
was 38 — 43°C (101 — 1 10°F). It required 67 - 100 h to dry the ear-maize to a final
average moisture content of 10.6% to 13.6%.

Graphs of the dry-bulb inlet and exhaust air temperatures. the wet-bulb inlet
condition, and the static pressure during up-air and down-air drying are shown in Figure
6.1. In typical operation, the operators observe the difference between the inlet and the
exhaust drying air dry-bulb temperatures in determining the reversal and shutoff times.
They use the wet-bulb temperature curve to control the heated-air temperature. [The
static pressure inside a dryer is maintained at approximately 498 Pa.] In the absence of a
dependable simulation model. the graphs appear to be the preferred “tool” employed by

the operators for operating the ear-maize dryer.

67

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69

The transient moisture contents in an ear-maize drying bed during the drying process are
shown in Figures 6.2 through 6.4. Figure 6.2 shows the transient moisture contents at
three locations on top of the drying bed; the moisture contents on top of the bed at any
time varied on average about two percentage points. The change in moisture content at
the bottom of a drying bed is presented in Figure 6.3: there. the moisture content varied at
any time about one percentage point. The difference in moisture contents between the
top and the bottom of a bed was maximum at reversal (i.e.. after 42 hours of drying. in

Figure 6.4).

Figure 6.5 presents the typical temperature profile at three locations within a bin
during drying; from an initial temperature of 30°C . it slowly increased to 40°C. The
temperature near the bottom was higher before airflow reversal and lower after airflow
reversal than a point near the top. The temperature difference across the bed decreased

slowly as the drying proceeded.

70

 

DX D
bl

DXCI
In

15.0

Moisture content (%)

10.0

5.0

 

 

0.0

 

0.0 20.0 40.0 60.0 80.0 100.0
Drying time (h)

D Top-Location I A Top-Location 2 x Top-Location 3

Figure 6. 2 Moisture contents at the top of the drying bed during a typical drying
run (air reversal after 42 h).

71

a!

 

00
OD

Moisture content (%)
u
a» 0

{Jr
0

 

 

 

0.0 20.0 40.0 60.0 80.0 100.0
Drying time (h)

0 Bottom-location 1 El Bottom-location 2 A Bottom-location 3

Figure 6. 3 Moisture contents at the bottom of the drying bed during a typical
drying run (air reversal after 42 h).

72

 

l-s

 

15.0 ° 0

Moisture content (%)
a
a

10.0

 

5.0

 

 

0.0

 

0.0 20.0 40.0 60.0 80.0 100.0
Drying time (h)

0 Top-average D Bottom-average

Figure 6. 4 Moisture content on top and at the bottom of a bed of ear-maize during
the in-bin drying process (air reversal after 42 h).

\l
1")

45.0
40.0
35.0
30.0 o
25.0 x
20.0
15.0
10.0
5.0
0.0

 

>00
>0 0
>00
> 00
>00
> 00
> 00
>00
>00
>0
00 >
00>
0CD
00>
Q
0
09
an

Experimental temperature (°C)

 

 

 

0.0 20.0 40.0 60.0 80.0 100.0

Drying time (h)

0 0.6m I; 1.2m A 1.8m

Figure 6. 5 Experimental temperature profile at the 0.6, 1.2, and 1.8 m bed depth
during in-bin drying of ear-maize (air reversal after 52 h).

74

Figure 6.6 shows transient bed-depth during a typical drying test. The depth
curve before airflow reversal (at 47 h) is steeper compared to the after reversal depth
curve; the slope is even more during the first few hours.

Shrinkage versus change in moisture content is shown in Figure 6.7. The trend is

similar to that in Figure 6.6.

75

3.0

 

2.0

l

1.5

Depth (111)

1.0

0.5

 

0.0

 

 

0 10 20 30 40 50 60 70
Drying time (h)

Figure 6. 6 Depth versus drying time during a typical drying test (test # 2).

76

80

 

1111

 

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1

Depth Change (my)

 

 

 

0.0
0.0

U)

10.0

15.0 20.0

MC content change (%)

25.0

Figure 6. 7 Depth change versus moisture content change (test #2).

77

Observation of the bed-depth change consistently revealed a two-phase pattern. A
relatively quick "settlement" of the bed (~7%) occurred in the first hours of drying. after
which the bed “shrinkage" appeared to be affected mainly by the ear-maize moisture-
content change. This observation led to the depth-change model of the bed as shown in
equations 4.5a and 4.5b. Thirty-three out of total of 51 data points (collected during
seven experiments) were selected randomly and used in the linear regression. It yielded
equations 6.1a and 6.1b (R2 = 67):

AL = 0.0212(14-1/0.» 0.1055 + 0.0140, 1,, 1_<, 5 h (6.1a)

AL =0.0212 (111—114,) +0.1055 +0070, t>5h (6.1b)

6.1.1 Viability Test Results

Table 6.2 shows the results of both warm-germination and cold-germination tests.
No experiments yielded average germination values of less than 98%. This indicates that
the temperature range (section 1.3) used in drying ear-maize in a commercial ear-maize
dryer was not detrimental to seed viability.

The germination results suggest that the drying temperature may be increased in

order to achieve high capacity and low cost without a loss of viability.

78

 

Table 6. 2 Germination percentage of seed-maize dried in a commercial ear-maize

 

 

 

 

dryer*.
Experiment Warm Cold
No./Rep. germination germination
1/ 1 1 00 98
1/2 1 ()0 99
1/3 98 99
1/4 99 99
Average 99 99
2/1 99 98
2/ 2 95 97
2/ 3 98 97
2/4 97 97
Average 98 98
3/1 1 00 99
3/2 100 98
3/ 3 I ()0 98
3/4 99 99
Average 99 98
4/1 98 98
4/2 99 97
4/ 3 1 00 98
4/4 99 99
Average 99 98

 

(Air-temperature range: 30 - 45°C)

79

1 .

6.2 Validation

For validation. the experimental data sets are compared with the simulated data

sets and the root mean square errors (RMSE) for the data sets are calculated by:

 

W

lei — X1)
(11 — l)

where the numerator is the sum of square errors (SSE). and the denominator is the

 

RMSE = (6.2)

degrees of freedom (DOF).

6.2.1 Dryer Model

The ear-maize dryer model (Equations 4.1 — 4.3. 4.5a. 4.5b. and 4.7) was checked
for accuracy with the experimental data collected during the 24 experiments conducted in
2001 at a commercial ear-maize dryer. Constantine. Michigan.

The simulated and the experimental moisture content values for the bottom. and
top of the dryer over time are shown in Figures 6.8 through 6.9. respectively (test #13).
In general the figures show good agreement between the experimental and the simulated

moisture contents.

80

 

 

Bottom moisture content (%)

 

 

 

5
0
0 20 40 60 80
Drying time (h)
0 Experimental —Simulated

Figure 6. 8 Simulated and experimental moisture content at the bottom of the bed
during drying (test #13).

81

 

 

Top moisture content (%)

 

 

 

0 20 40 60 80

Drying time (h)

0 Experimental -—Simulated

Figure 6. 9 Simulated and experimental moisture contents at the top of the bed
during drying (test #13).

82

Figures 6.10 to 6.12 show the simulated moisture contents plotted against the
experimental values for the bottom. top. and final for all the 24 tests. In each Figure. a
perfect simulation would result in a 45° angle with the x-axis.

Figure 6.10 shows the simulated versus experimental moisture content at the
bottom of the dryer. The simulated data are in fair agreement with the experimentally-
measured data. The RMSE for the moisture content at the bottom was 2.8%. and the bias
was 1.1% (w.b.).

Figures 6.1 1 shows the simulated versus experimental moisture content at the top
of the dryer. The root mean square error (RMSE) between the predicted and
experimental data was 3.5% (w.b.); the bias was 1.8% (w.b.). Thus. the simulated data
are in fair agreement with the experimentally-measured data.

Figure 6.12 shows the simulated versus final moisture contents. The RMSE for

the final moisture content was 2% (w.b.); the bias was 0.9% (w.b.).

83

40

 

 

 

 

o
o
o
o
30 0°
A o
c,\"’ 0 o o
5 ° .0 °
2 0 0 8g 9
-o 20 o “o m
3 o 9% 0
52 Q 0°
2 ° %%0° 0
E73 1 o °6€
0 0° 0
0
0 5 10 15 20 25 30 35 40

Experimental MC (%)

Figure 6. 10 Simulated versus experimental moisture contents at the bottom of the
dryer during drying (RMSE: 2.8%).

84

 

 

 

 

 

40
0
35 o 8
0
30 ° o 0
83 0 °
5 25 0 0 °° o o
2 o ‘5
3 20 g «:03 °°
a $0 88> 00
E 15 0 egg:
m 00 g
10 °
5
0
0 5 10 15 20 25 30 35 40

Experimental MC (%)

Figure 6. 11 Simulated versus experimental moisture content at the top of the dryer
during drying (RMSE: 3.5%).

85

 

 

 

Simulated MC (%)

Figure 6. 12 Simulated versus experimental final moisture content (RMSE: 2.0%).

#
'JI

6

 

 

 

 

0
00
0
o 0
0
0
0
0
0
0
0
6 9 12

Experimental MC (%)

86

 

Simulated versus experimental total drying times for the 24 experiments are
plotted in Figure 6.13. The simulations were run to end at a target average moisture
content corresponding to each experimental test. The RMSE between the simulated and
actual total drying time was 9 h; the bias was 4 h. indicating that the model was on
average slightly under predicting total drying time.

Figures 6.14 and 6.15 show the simulated and the experimental temperatures at
two points within the bed of ear—maize (0.6 111 and 1.2 1n from the bottom). The RMSE
for the temperature at 0.6 m was 20°C; the bias was 02°C. For the temperature at 1.2 m
the RMSE was 25°C; the bias was 05°C.

The ear-maize drying model has proven sufficiently valid for predicting the
transient moisture content. the reversal and the final moisture contents. the total drying
time. and the temperatures within the bed of the author‘s experimental tests. It is
concluded that the differential-eaualion ear-maize diying model is validated and can be

used/Or analyzing the dryer per/brmance.

87

 

120

 

 

 

o
100
o
o
A o
5 80 9 0g»
1,: 0 0 o o 0
1': 6’
“o 60
2
Cd
".32
E 40
Cl)
20
0
0 20 40 60 80 100 120

Experimental TDT (11)

Figure 6. 13 Simulated versus experimental total drying time (TDT) (RMSE: 9.0 h).

88

 

 

 

 

 

45
H 40
0U 080 300
v o 000 o
95’ 00 go 8 0828‘) o
g 35 o 00 ° °° °
8.. o o 00 8
E 0 0° 0
g 000 0
'8 30 o
a: o
'5'
.§
m 25 o o

20 °

20 25 30 35 4O 45

Experimental temperature (°C )

Figure 6. 14 Simulated versus experimental temperature (°C) at 0.6 m depth from
the bottom of the drying bin (RMSE: 2.0°C).

89

 

 

 

 

 

45
0
0

”‘40

‘5: 0° 0°°88:§

2 00 0 888°0

:3 00 0 0

.. 00 0000

i335 °83 ° 0° 0

g 08 30 0 0 03

E 0 80° 0 000

3 00 0 00

525 0

20 °
20 25 30 35 40 45

Experimental temperature (°C)

Figure 6. 15 Simulated versus experimental temperature (°C) at 1.2 m depth from
the bottom of the drying bin (RMSE: 25°C).

90

6.2.2 Shrinkage Model

A total of 51 data points from seven experiments were recorded for the depth
change: 33 data points were used to develop the shrinkage model. and 18 were used for
validation.

The RMSE for the calibration data set is called the standard error ofcalibration
(SEC); for the validation of the (independent) data set. the RMSE is called the standard
error of prediction (SEP). For the shrinkage model. the value of the SEC was 0.22 m (0.7
ft). This means that the average expected prediction error of the model is $0.22 m.
Figure 6.16 shows the comparison between the model-simulated and the experimentally-
measured values of the bed shrinkage during the drying process. An almost equal
number of points fall below and above the theoretically predicted line. The SEP was 0.27
m. The SEP/SEC ratio is 1.2. which reflects the robustness of the model against the

validation data set.

91

1.5

 

 

 

 

1.2
E
c)
:0
.2 0.9 0 0
.g 0 0
43’ 0 °
2 0.6 0
2
2 0
.35 0 0
‘ 0.3 °

0
0.0
0.0 0.3 0.6 0.9 1.2 1.5

Experimental shrinkage (m)

Figure 6. 16 Simulated versus experimental shrinkage of a bed of ear-maize.

92

r

The shrinkage sub-model (Equations 6.1a and 6. 1 b) has been incorporated in the
drying model: it calculates the bed-depth after each time step using the total bed-moisture
loss.

Figure 6.17 shows the moisture content predicted by the ear-maize drying model
with and without shrinkage; the model with the shrinkage equation predicts better than
the model without it.

Table 6.3 shows the simulated total drying time for the 24 experimental tests. The
ear-maize drying model without shrinkage predicts a longer drying time than the
experimental. In general. there is better agreement between the experimental data and the
model containing the shrinkage equation than between the experimental values and the
model without the shrinkage model. The SEP without the shrinkage sub-model was 33 h
with a bias of +30 11: for the model that includes the shrinkage equation. the SEP was 9.0
h and bias was -3.9 11 respectively. Thus. adding the shrinkage equation into the basic

MSU drying model is essential.

93

fl

 

Moisture content(%)

 

 

 

 

0.0 20.0 40.0 60.0 80.0 100.0
Drying time(h)

 

------- No shrinkage model Including shrinkage model o Experimental

Figure 6. 17 Experimental and simulated moisture content of a bed of ear-maize
during drying with and without shrinkage.

94

Table 6. 3 Experimental total drying time and as simulated by the model with and
without the bed-shrinkage equation.

 

 

 

 

Total drying time(h)
Initial Initial Without With
Test MC depth shrinkage- shrinkage-
number (%) (m) Experimental equation equation
Drying yearz2001

1 30.7 2.7(9)* 88.7 112 77

2 31.2 2.7(9) 89.3 121 82

3 33.4 2.7(9) 83.4 125 81
4 29.0 2.7(9) 72.5 115 81

5 32.5 3.4(11) 93.6 137 105
6 28.7 2.1(7) 72.1 117 82

7 37.2 2.7(9) 80.5 113 68

8 37.6 3401) 93.8 135 88
9 34.5 2.1(7) 70.6 92 69
10 31.3 2.7(9) 69.2 102 71
11 31.5 3.4(11) 94.0 109 80
12 33.4 2.1(7) 79.3 87 66
13 32.0 2.1(7) 68.9 91 69
14 29.8 3.4(11) 69.0 115 85
l 5 33.6 2.7(9) 91.3 121 80
16 30.4 3.4(11) 87.0 103 76
17 34.9 2.1(7) 82.1 97 73
18 31.9 3.0(10) 67.4 107 73
19 32.6 2.7(9) 89.8 119 77
20 34.9 2.1(7) 76.4 102 73
21 32.7 3.4(11) 90.5 118 84
22 36.5 2.1(7) 78.2 106 77
23 36.0 3.4(11) 100.0 141 94
24 35.] 27(9) 86.2 115 70
RMSE 33 9.0

* Figures in parenthesis are feet.

95

6.2.3 Transient versus Average Input

The program can handle either the transient or the average drying air conditions
as inputs. Table 6.4 shows the comparison between the predicted total drying times
resulting from using transient and average data. The RMSE using transient data was 1 1.6
h with a bias of 3.9 h. and the RMSE using average data was 9.4 h with a bias of 3.9 h.
Thus. the difference between the two modes of input was small. and therefore either input

can be used.

Table 6. 4 Comparison between experimental and simulated total drying times using
transient and average drying air data.

 

 

 

Shutoff time (h)
Initial Simulated Simulated
MC Depth (transient (average
(%w.b.) (ft) Experimental input) input)
30.7 9 88.7 84 77
3 l .2 9 89.3 89 82
33.4 9 83.4 87 81
29.0 9 72.5 83 81
32.5 H 93.6 llO l05
28.7 7 72.] 69 82
37.2 9 80.5 73 68
37.6 I l 93.8 95 88
34.5 7 70.6 57 69
31.3 9 69.2 74 7|
3 l .5 l I 94.0 8] 80
33.4 7 79.3 59 66
32.0 7 68.9 60 69
29.8 I I 69.0 88 85
33.6 9 9| .3 85 80
30.4 I 1 87.0 82 76
34.9 7 82.1 64 73
3 l .9 l0 67.4 77 73
32.6 9 89.8 81 77
34.9 7 76.4 58 73
32.7 I I 90.5 89 84
36.5 7 78.2 58 77
36.0 ll 100.0 100 94
35.1 9 86.2 77 70
RMSE 1 L6 9.4

 

96

6.3 Typical Simulation Output

The validated model will be used in this section to analyze the model output and
to explore the effect ofchanges in the major input parameters. i.e.. air conditions. airflow
rate. bed-depth. initial moisture content. upon the simulated results.

The simulated change in moisture content. depth. and exhaust drying-air are
shown in Tables 6.5 and 6.6. The predicted airflow reversal is at 40 hours and of drying
completion at 65 hours. Before reversal (Table 6.5). the ear-maize moisture content
changes from 30.0 to 20.0%. and depth changes from 2.7 to 2.0 m. At the exhaust. the
drying-air conditions are: (l) the dry-bulb temperature increases from 16 to 30°C. (2) the
wet-bulb temperature after first 3 hours remains constant at 21 OC. ( 3) the relative
humidity decreases from almost saturation (the first two hours) to 45% after 40 hours.
and (4) the humidity ratio reaches 0.0135 (kg/kg) in two hours and then decreases to
0.01 18 (kg/kg) after 40 hours.

During after-reversal~drying (Table 6.6). the ear-maize moisture content changes
from 20 to 12.4%. and the depth changes from 2.0 to 1.6 m in 25 hours. The drying air
conditions at the exhaust are: (l ) the dry bulb temperature changes from 31 to 38°C. (2)
the relative humidity decreases from 38 to 22%. and ( 3) the humidity ratio decreases
from 0.0104 to 0.0087 kg/kg. The after-reversal-drying air condition suggests that this
air is suitable for recycle.

Figures 6.18 to 6.21 show the drying air conditions at the exhaust for four
different initial moisture contents. The relative humidity (Figure 6.18) increases to

saturation in two hours. falls rapidly to around 60% in the next two hours. and declines

97

steadily thereafter. After-reversal. the drying-air exhausts at a much lower relative
humidity. because the drying air during this period is not recycled air and passes through
relatively dry ear-maize. The humidity ratio graph (Figure 6.19) shows a similar trend

except that it has no rapid-fall phase.

98

5‘.

Table 6.5 Simulated kernel moisture contents, depth change and exhaust drying-air
conditions over the drying period (before reversal).

 

Exhaust drying-air conditions

 

 

 

Drying Moisture Dry-bulb Wet-bulb Relative

time content Depth temperature temperature humidity Humidity ratio

(h) (%) (m) (°C) (°C) (%) (kg/kg)
1 30.0 2.7 16 16 99 0.0115
2 29.8 2.7 19 19 100 0.0135
3 29.6 2.6 23 20 77 0.0135
4 29.3 2.6 25 21 67 0.0135
5 29.1 2.5 26 21 65 0.0135
6 28.8 2.5 26 21 64 0.0135
7 28.5 2.5 26 21 63 0.0134
8 28.3 2.4 26 21 63 0.0134
9 28.0 2.4 26 21 62 0.0133
10 27.7 2.4 27 21 61 0.0133
11 27.4 2.4 27 21 61 0.0132
12 27.2 2.4 27 21 60 0.0132
13 26.9 2.4 27 21 59 0.0131
14 26.6 2.3 27 21 59 0.0131
15 26.4 2.3 27 21 58 0.0130
16 26.1 2.3 27 21 58 0.0130
17 25.8 2.3 27 21 57 0.0129
18 25.5 2.3 27 21 56 0.0129
19 25.3 2.3 28 21 56 0.0129
20 25.0 2.3 28 21 55 0.0128
21 24.7 2.3 28 21 55 0.0128
22 24.5 2.2 28 21 54 0.0127
23 24.2 2.2 28 21 54 0.0127
24 23.9 2.2 28 21 53 0.0126
25 23.7 2.2 28 21 52 0.0126
26 23.4 2.2 28 21 52 0.0125
27 23.1 2.2 29 21 51 0.0125
28 22.9 2 l 29 21 51 0.0124
29 22.6 2 l 29 21 50 0.0124
30 22.4 2 l 29 21 50 0.0123
31 22.1 2.1 29 21 49 0.0123
32 21.8 2.1 29 21 49 0.0122
33 21.6 2.1 29 21 48 0.0122
34 21.3 21 29 21 48 0.0121
35 21.1 2.0 29 21 47 0.0121
36 20.8 2.0 30 21 47 0.0120
37 20.6 2.0 30 21 46 0.0120
38 20.3 2.0 30 21 46 0.0119
39 20.1 2.0 30 21 45 0.0119
40 19.9 2.0 30 21 45 0.0118

 

 

99

Table 6.6 Simulated kernel moisture contents, depth change and exhaust drying-air
conditions over the drying period (After reversal).

 

Exhaust drying air conditions

 

 

Drying Moisture Dry-bulb Wet-bulb Relative

time content Depth temperature temperature humidity Humidity ratio

(h) (%) (m) (°C) (°C) (%) (kg/kg)

Reversal

41 19.5 2.0 31 20 38 0.0104
42 19.2 2.0 33 21 34 0.0105
43 18.8 1.9 33 21 33 0.0104
44 18.5 1.9 34 21 32 0.0103
45 18.1 1.9 34 21 31 0.0102
46 17.8 1.9 34 21 31 0.0102
47 17.4 1.9 34 21 30 0.0101
48 17.1 1.8 34 21 30 0.0100
49 16.7 1.8 35 21 29 0.0099
50 16.4 1.8 35 21 28 0.0098
51 16.1 1.8 35 21 28 0.0097
52 15.8 1.8 35 21 27 0.0096
53 15.5 1.7 35 21 27 0.0096
54 15.2 1.7 36 21 26 0.0095
55 14.9 1.7 36 21 26 0.0094
56 14.6 1.7 36 21 25 0.0093
57 14.3 1.7 36 21 25 0.0092
58 14.0 1.7 36 21 24 0.0092
59 13.8 1.6 37 21 24 0.0091
60 13.5 1.6 37 21 23 0.0090
61 13.3 1.6 37 21 23 0.0089
62 13.1 1.6 37 21 23 0.0089
63 12.9 1.6 37 21 22 0.0088
64 12.6 1.6 37 21 22 0.0087
65 12.4 1.6 38 21 22 0.0087

 

100

 

60

40

Relative humidity (%)

 

20

 

 

 

0 20 40 60 80 100

Drying time (h)

 

--—-25%1MC ----- 30%1MC ---35%1MC 40% [MC

Figure 6. 18 Simulated relative humidity of the exhaust air for ear-maize with four
different initial moisture contents during drying.

101

0.0160

 

0.0140

)

0.0120

n
t:

(kg/k

0.0100

 

0.0080

J

0.0060

Humidity ratio

0.0040

0.0020

 

 

0.0000

 

0 20 40 60 80 100
Drying time (h)

--—-25%1MC ----- 30%IMC ---35%1MC

 

40% [MC

Figure 6. 19 Simulated humidity ratio of the exhaust air for ear-maize with four
different initial moisture contents during drying.

102

The exhaust wet-bulb (Figure 6.20) virtually remains constant throughout the drying
period. The exhaust dry-bulb temperature (Figure 6.21) drops to 16°C in two hours. rises
to 26°C in the next two hours. and then increases steadily.

The exhaust drying-air conditions. irrespective ofthe initial moisture contents.
follow a specific trend. The step change in the graphs indicates the airflow reversal. and
the differences in the after-reversal parts of the graphs are due to the differences in the

reversal moisture content.

 

 

 

 

 

 

25
A» \f' V v v ............
g) 20
If
‘5
g 15
8.
E
a
Q 10
'5
.D
‘5
3 5

0

0 20 40 60 80 100

Drying time (h)

30% IMC - - - - 35% IMC ----- 40% IMC

 

- --25% [MC

Figure 6. 20 Simulated wet-bulb temperature for ear-maize with four different
initial moisture contents during drying.

104

b;

 

emperature (°C )

 

 

 

 

é
P
g 10
5
0
0 20 40 60 80 100

Drying time (h)

 

25% [MC — - - 30% IMC ----- 35%1MC - - - '40% IMC

Figure 6. 21 Simulated dry-bulb temperature for ear-maize with four different
initial moisture contents during drying.

105

Figure 6.22 shows the change in bed-depth and moisture content during drying.

Figure 6.23 expresses the relationship between the bed-depth and the airflow. Figure

6.24 shows the change of airflow with time with four initial ear-maize moisture contents.

The graphs indicate that as the bed-depth and moisture content decreases the airflow

increases.

106

N

 

Depth (m)

 

Moisture content (%)

5 0.5

 

 

 

0 10 20 30 40 50 60 70
Drying time (h)

—- Moisture content - - - Depth

Figure 6. 22 Change in the average moisture content and bed-depth with time.

107

3.0 1600

 

 

 

 

 

2.5 1400
1200 A
2.0 £5
1? 1000 E
‘5’ 15 800 E
g- a
o 600 E
1.0 g
400
0.5
~200
0.0 -0

0 10 20 30 40 50 60
Drying time (h)

 

Depth - - - - Airflow

Figure 6- 23 Change in bed-depth and airflow through the drying bed with time.

108

 

1

 

..
)

Airflow rate (m'/m'/h)

 

 

 

0 20 40 60 80 100

Drying time (h)

 

----- 25%1MC ---30%1MC ----35%1MC 40%1MC

Figure 6- 24 Change of airflow through the drying bed with time.

109

Figures 6.18 and 6.19 raise concern about condensation during drying. The water
vapor in moist air. when cooled to the dew point temperature at constant humidity ratio
and atmospheric pressure. condenses. [The program adds the condensate to the product
moisture content and uses the new product moisture in further calculation] However.
Figure 6.25 shows that after about three hours of drying the humidity ratio starts
decreasing slowly. coupled with a gradual decrease of the relative humidity. Therefore.

no condensation occurs in the dryer. mainly due the hiin airflow rate.

110

 

 

 

 

 

 

 

0.018 120
0.016 Humidity ratio
. . 100
0.014
’65
2?; 0.012 RH 80
:16 --------
,2 0.01 0;;
93 50 E
_+__>.~ 0.008 (I
.12
E 0.006 40
0.004
20
0.002
0 0
0 1 2 3 4 5 6

Drying time (h)

Figure 6. 25 Humidity ratio and relative humidity of the exhaust air during the first
six hours of drying.

111

6.4 Effect of ambient condition on the total drying time and the energy requirement
For the in-bin ear-maize drying process. the effect of small changes in the

ambient weather conditions was investigated. namely of: (1) the ambient relative
humidity. (2) the ambient temperature. The results are tabulated in Tables 6.7 — 6.8. The
following are the main conclusions regarding the effect of the weather on the shutoff time
and the energy requirement of the in-bin ear-corn drying system:

( 1 ) The relative humidity of the ambient air has a significant effect on the total time

of drying and the energy requirement.

( 2) The increase in the ambient temperature reduces energy requirement with minor

effect on the total drying time.

112

Table 6. 7 Effect of ambient relative humidity (at constant ambient temperature,

 

 

21°C).
RH Shutoff time Energy
(%) (h) (kJ/kg)
50 65 5854
65 72 6598
80 83 7736
95 101 9800

 

Table 6. 8 Effect of ambient air temperature (at constant humidity ratio, 0.0055).

 

 

mbient
Temperature Shutoff time Energy
(°C) (h) (kJ/kg)
10.0 41 3957
1 5 .5 42 3626
21 .0 44 3294

26.5 48 3158

113

6.5 Effect of parameters on the capacity and the cost
The effect of small changes in the standard drying conditions (Table 6.9) of six
input values. namely of: ('1) the bed-depth. ( 2) the up-air temperature. (3) the down-air
temperature. (4) the static pressure. (5) the reversal moisture content. and (6) the drying-
air wet-bulb temperature was investigated. The results are shown in Figures 6.26 to 6.31.
The following are the main conclusions regarding the effect of the various parameters on
the capacity and the energy cost of an in-bin ear-maize reverse-airflow drying system:
( 1) The wet-bulb temperature has a significant negative effect on the capacity and the
energy cost.
(2 ) An increase in up-air and down-air temperatures increases the capacity and
decreases the energy cost: the effect of up-air is larger than that of down-air.
(3 .) An increase in airflow and an early reversal affects the capacity positively: the

airflow has a positive and the reversal moisture content has a negative effect on

the energy cost.

114

Table 6. 9 Standard drying conditions.

 

Drying air parameters Standard value

 

 

 

Ambient temperature (°C ) 16
Ambient relative humidity (%) 70
U p-air temperature (°C) 35
Down-air temperature (°C) 41
Drying-air wet-bulb temperature (°C) 21
Bed depth (m) 2.7
Static pressure (Pa) 498
Initial moisture content (%) 30
Reversal moisture content (%) 22
12.5

Final average moisture content (%)

‘

115

16.0
14.0
A 12.0
e
"g 10.0
:1 8.0
a 6.0
8
4.0
2.0
0.0

 

 

 

 

 

Lu
[J

35 38 41

Up-air temperature ( °C )

 

Capacity ------- Cost

18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0

Cost ($/tonne)

Ifigure 6. 26 Effect of up-air temperature on the capacity and the energy cost.

116

 

 

16.0 14.8

 

 

 

14.0 14.6
‘ .. 14.4
A 12.0
5 14.2 A
“a 10.0 °
:11 14.0 g
.24 ‘. H
: 8.0 13.8 .7;
'5 13.6 23
a 6.0 C3
3 13.4
4.0
13.2
2-0 13.0
0.0 12.8
41 43 46 49

Down-air temperature (°C)

 

Capacity ------ Cost

l‘Tigure 6. 27 Effect of down-air temperature on the capacity and the energy cost.

117

 

 

 

 

 

13.0 18.0
12.5 16.0
14.0
E 12.0
.t 12.0 1;-
5 - «i5.
.940. 11.3 10.0 .9
:. e
g 11.0 8.0 9.
C3 0
S- , 6.0 U
L) 10.5
4.0
10.0 20
9.5 0.0
249 373 498 622

Static pressure (pa)

 

Capacity ----- Cost

Figure 6. 28 Effect of static pressure (airflow) on the capacity and the energy cost.

118

 

 

 

 

 

 

12.3
12.2
.75 12.1
:3“ 12.0 ‘
34»: 11.9
L)
('3
8* 11.8 ~
U
11.7
11.6
11.5.
2.4 2.7 3.0 3.4
Depth(m)
Capacity """ Cost

 

Figure 6. 29 Effect of depth on the capacity and the energy cost.

119

Cost (Sr/tonne)

 

 

16.0 25.0
14.0
20.0
A 12.0
E A
E- 10-0 15.0 E
.3.“ a
r; 8.0 a.
E; 6.0 10.0 g:
8
4.0
5.0
2.0
0.0 0.0

 

 

 

1 0 1 8 27 3 5
Wet bulb temperature (°C)

 

Capacity ----- Cost

Figure 6. 30 Effect of wet-bulb temperature on the capacity and the energy cost.

120

 

 

 

7

Capacity (kg/m"/h)

14.0

13.5

13.0

12.5

12.0

11.5

11.0

 

 

 

 

 

20 22 24 26

Reversal kernel moisture content (%)

—Capacity """ Cost

16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0

Cost (iii/tonne)

Figure 6. 31 Effect of reversal moisture contents on the capacity and the energy cost.

6.6 Optimization

The standard ear-maize dryer operating parameters are presented in Table 6.10:
(1) the heater thermal efficiency. (2) the motor efficiency. (3) the fan efficiency. (4) the
natural gas price. and (6) the electricity price.

The limits (ranges) for the constraints discussed in section 4.3.3 are inputs to the
optimization program along with the initial moisture content. The optimization program
searches for the optimal combination of the control variables: (1) the bed-depth. (2) the
up-and down-air temperatures. (3) the up-and down-air pressures. and (4) the reversal
moisture content. The program then calculates the objective function value in terms of
the maximum capacity or the minimum cost at the values of the optimal control variables.

The results of the optimization of the ear-maize drying operation are presented in
the next three sections: the first deals with the capacity maximization. the second with the
cost minimization. and the third presents the sensitivity analysis of the operating

parameters.

6.6.1 Capacity Maximization

Table 6.1 1 shows the values of the dryer control variables at which the maximum
dryer capacity is obtained for initial ear-maize moisture contents of 20 — 35%. and lists
what this optimum capacity will be. This information is given for both the conventional

and the Hunter ear-maize dryer.

122

I'd fm:1_- . “Q
A
1

 

 

 

Table 6. 10 Standard input parameters used in the optimization program.

 

 

Inputs / parameters Value Reference

Heater thermal efficiency. % 70 Hall. 1957

Motor efficiency. % 70 (65-85%. Anon.. 1958)

Fan efficiency. % 70 (40-70%. Perry et al.. 1963)
Natural gas price. $/mmbtu 5.9 The Wall Street Journal. 2003
Electricity price. $/megawatt hour 43.9 The Wall Street Journal. 2003

 

123

 

 

 

 

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124

For example. the maximum capacity of a conventional unit drying ear-maize from
25% to 12.5% is 25.1 kg of wet ears per m2 of dryer surface per hour (kg/mZ/h) when the
bed-depth is 3.3 m. the up-air temperature is 41°C. the down-air temperature is 46°C. the
up-air/down-air pressure are 746 Pa. and the average reversal ear-maize moisture content
is 18.0%. For the Hunter dryer. the drying capacity of this maize is 27.8 kg/mZ/h when
operating at 3.4 m bed-depth. 41°C up-air temperature. 746 Pa up-air/down-air pressure.
and reversal moisture content of 22%. The data show that the maximum capacity of the
Hunter dryer is slightly higher than the conventional dryer (27.8 kg/mZ/h vs 25.1 kg/mZ/h)
while operating at very similar operating conditions (except for the reversal moisture
content); however. the energy use and thus the drying costs are significantly higher for
the Hunter dryer (8541 kJ/kg and $13.6/tonne vs 5485 kJ/kg and $9.2/tonne). Note also
that the total time for drying is about 10% longer for the conventional dryer than for the
Hunter dryer.

Close inspection of the control variables in Table 6.11 shows that for most
moistures the maximum parameter values (i.e.. the constraint values) were selected (as
expected). Only at very high initial moisture contents (i.e.. 35%) do some of the control
variables. such as the bed-depth and static pressure. not reach their maximum values.

An interesting result of the data in Table 6.1 1 is that the optimum air-reversal
moisture content varies in the conventional dryer from 15.9% for 20.0% initial moisture
content ear-maize to 22.0 for 35% initial moisture content ear-maize. Thus, the industry
rule-of-thumb of reversing at 19 — 20% moisture content (Precetti. 2001) is not optimal.

The maximum up-air and down-air temperatures, upon which the results in Table

6.11 are based, are 41°C and 46°C. respectively. These are the maximum temperatures

125

commonly used in the seed-maize industry. However. Baker et al. (1993) showed that
these temperature values are conservative; that values of 43°C for the up-air and 49°C for
the down-air temperature will not seriously affect seed-maize viability. Therefore. a
capacity maximization analysis was conducted allowing the up-air and down-air
temperatures to reach 43°C and 49°C respectively. The results are shown in Table 6.12.
A comparison between the data in Tables 6.1 1 and 6.12 shows that the increased up-air.
down-air temperatures lead to an increase in capacity by about 12%. a decrease in cost by

20%.

6.6.2 Cost Minimization

Table 6.13 shows information how best to operate conventional and the Hunter
ear-maize dryers to minimize cost. For 25% initial moisture content maize. the optimum
values for the conventional dryer of the bed-depth and the up-air/down-air temperatures
are at their maximum (3.2 m. 41/46°C. respectively) while the air pressures and thus the
airflow rates are at the minimum (274 Pa).

It is instructive to compare the output figures in Tables 6.1 1 and 6.13 under the
capacity optimization scenario. the total drying time for 25% moisture content ear-maize
in conventional dryer is 42 h; Operating under cost minimization conditions. it requires 51
h. But. it costs $9.2/tonne at maximum capacity conditions and only $7.2/tonne if the

costs are minimized.

126

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6.6.3 Sensitivity Analysis

In an operating-system design. a constraint can rarely be known with absolute
precision. Thus. it is essential to know the effect of uncertainty on the objective function.
The analysis that determines “the rate of change of the optimum capacity and cost value
with respect to a perturbation in the constraint is called the sensitivity analysis (Beightler
et al., 1979)." A sensitivity analysis provides insight in a physical process and is useful
in manipulating the control variables without resolving the problem all over again. In
this section, the effect of small changes in the constraints on the optimum capacity and
the minimum cost of an ear-maize dryer is considered for the case of 30% initial moisture
content ear-maize. The small changes in the constraints are termed (in this thesis) as
standard error. Small change of 0.15 m for the depth, 0.25% for the initial moisture
content, l.l°C for the temperatures. and 50 Pa for the air-pressure are considered one

standard error.

The constraints are modified to one and two standard errors above and below the
selected values. and the effects on the optimum-capacity and the minimum-cost values
are calculated. The result obtained by varying the constraint above the selected value is
subtracted from the result obtained by varying the constraint below divided by the
number of standard error variation (i.e.. either 2 or 4) gives a normalized sensitivity. The
results are presented in Table 6.14 and 6.15. The effects of changes in the bed-depth, the
up-air/down-air temperatures. the static pressure and the reversal moisture content

(RMC) are considered. The capacity is sensitive (in descending order) to the down-air

129

 

temperature. the up—air temperature. and the down-air pressure. If the up-air temperature
varies or the measurement is uncertain by one standard error. the capacity is affected by

0.9 kg/mZ/h.

Normalized sensitivity of other control variables are to be interpreted in a similar manner.
From Table 6.14. it is clear that air temperature has a greater influence on capacity than

do the other operating parameters.

By adopting similar reasoning. it can be inferred from Table 6.15 that the cost
function value is most affected by the change in the down-air pressure. The negative
normalized sensitivity value indicates that the control variable affects the cost function
value inversely: i.e.. ifthe down-air temperature increases than its optimal value, the cost

function value is decreases.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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132

6.6.3.1 Effect of Fuel and Electricity Price Increase

The effect of a price increase in the fuel and electricity prices is shown in Table
6.16. A change in the fuel price affects the total drying cost the most. For example. if
the fuel price is increased by 20%. the cost for drying ear-maize at an initial moisture
content of 30% is increased by about 18%. while it increases only 2.0% if the electricity

price is increased by the same percentage.

6.7 Projected Capacity of a Dryer if Operated under Optimal Conditions

Table 6.17 shows the monthly drying capacity for different initial moisture ear-
maize under optimum capacity and minimum cost conditions. taking into account the

filling/drying/shelling times.

If a dryer consisting of24 bins. and is operated so that the control variables are
maintained at their optimal values for the maximization of the capacity. and if the maize
reaches the dryer at 30% initial moisture content, then 26.975 tonnes of wet-ears can be
dried per month in the commercial dryer. and 27,996 tonnes of wet-ears is dried per
month in the Hunter design. If the dryer is operated while minimizing cost, the figures
are 21,678 and 22.402 tonnes of wet-ears for the conventional dryer and the Hunter
design. For the conventional design. the capacity increases by 24% if the drying strategy

is switched from cost minimization to capacity maximization.

133

Table 6.16 Effect of fuel and electricity prices on the optimum capacity and
minimum cost of an ear-maize drying system.

 

 

 

 

Cost ($/tonne) Cost ($/tonne)
Cost Cost
For 20% increase increase
electricity over For 20% over
IMC price standard fuel price standard
Optimization 1% w.b.) Standard increase 1%) increase (%)
20 5.2 5.3 1.9 6.1 17.3
. 25 5.1 5.2 2.0 5.9 15.7
Capac1ty
30 10.1 10.3 2.0 11.9 17.8
35 14.7 15.0 2.0 17.4 18.4
20 3.9 3.9 0.0 4.6 17.9
25 4.5 4.6 2.2 5.4 20.0
Cost
30 8.1 8.1 0.0 9.6 18.5
35 11.4 11.5 0.9 13.7 20.2

 

134

Table 6.17 Dryer capacity per month at optimal parameter values for the
conventional and Hunter dryers.

 

 

 

 

 

Time of
filling
Total & Waiting Number Capacity
IMC time Depth shelling time of tonnes/dryer/
(%w.b.) (h) (m) (h) (h) Time/cycle fills/month month
CONVENTIONAL DESIGN
Capacity optimization
20 32 3.3 2.0 22.0 56.0 13 40348
25 42 3.4 2.1 22.0 66.1 ‘ l 1 32473
30 48 3.4 2.1 22.0 72.1 10 26975
35 45 2.9 1.8 22.0 68.8 10 22642
Cost optimization
20 41 3.3 2.0 22.0 65.0 11 34015
25 51 3.2 2.0 22.0 75.0 10 25760
30 63 3.2 2.0 22.0 87.0 8 21678
35 72 3.3 2.0 22.0 96.0 7 18469
HUNTER DESIGN
Capacity optimization
20 29 3.2 2.0 22.0 53.0 14 42134
25 38 3.4 2.1 22.0 62.1 12 34638
30 47 3.4 2.1 22.0 71.1 10 27996
35 46 2.9 1.8 22.0 69.8 10 22948
Cost optimization
20 36 3.3 2.0 22.0 60.0 12 38093
25 50 3.2 2.0 22.0 74.0 10 27936
30 60 3.2 2.0 22.0 84.0 9 22402
35 70 3.3 2.0 22.0 94.0 8 18944
Total hour available per month 720
Number of bins per dryer 24
Filling time (average) per meter of depth (min) 19.7
Shelling time (average) per foot of depth (min) 17
Waiting time between shelling and next filling (h) 22
Floor area of one bin (m1) 117.8
Total floor area per dryer (m2) 2827.2

135

6.8 Rules for Dryer Control

The control rules that are currently practiced in a commercial ear-maize dryer.

and those which are proposed in this thesis. are presented in Table 6.18.

Table 6. 18 Rules for dryer control

 

 

“Rule"
No. Current control Proposed control
1 using of special indices no indices required
2 measuring of exhaust air temperature no exhaust temperature required
3 monitoring of differences in inlet and no differences monitored in inlet and
exhaust temperatures exhaust temperatures
4 reversing of airflow when the difference reversing of airflow at time
(mentioned in #3) is 4 — 6°C calculated by the program for desired
reversal moisture content
5 stopping of drying when the difference stopping of drying at time calculated
(mentioned in #3) is 4 — 6°C by the program for desired final
moisture content
6 restarting of drying if the moisture restarting of drying if the moisture

content of the dried ear-maize is more
than the desired final moisture content

content of the dried ear-maize is
more than the desired final moisture
content

 

136

7. SUMMARY

The following are the main conclusions drawn in this seed-maize drying study:
A model for the shrinkage of ear-maize bed during drying has been developed.
The standard error of calibration (SEC) of the shrinkage model was 0.22 m.
Validation of shrinkage model against field data showed a standard error of
prediction (SEP) of 0.27 m. The ratio of SEP to SEC was 1.2.

A differential-equation simulation model for an in-bin reverse-airflow ear-maize
dryer has been developed. incorporating the shrinkage equation.

Experimental data were collected at a commercial ear-maize drying facility for
validating the bed-shrinkage and the ear-maize dryer models.

The model predicted the transient moisture content. the reversal moisture
content and the final moisture contents, the shutoff time. and the temperatures
within the ear-maize bed well. The SEP without the shrinkage model was 33.0 h
with a bias of +3 h; the SEP without the shrinkage model was 9.0 h with a bias of
-3.9 h.

An optimization procedure has been developed to either maximize the capacity or
minimize the energy cost of ear—maize drying.

The maximum capacity is achieved when the bed depth. air temperature, and
air pressure are maximized, except at the highest initial moisture content (3 5%).
The minimum cost is achieved when the bed depth and the air temperatures are

maximized and the air pressure is minimized.

137

The sensitivity analysis has shown that the air temperature has the greatest
influence on the dryer capacity and the airflow on the drying costs.

When investigated for the expected capacity ofa 24-bin ear-maize dryer
maintaining the control variables to maximize capacity. given 30% initial
moisture content. found a 24% increase in capacity over where the control
variables are maintained to minimize cost. Conversely. a 36% decrease in cost
can be achieved when the dryer is operated under cost minimization strategy.
The newly-developed ear-maize drying model can be used with confidence in

systems analyses of commercial seed-plant facilities.

138

8. RECOMMENDATIONS FOR FUTURE WORK

The following are the principal recommendations for follow-up research:

. The newly developed in-bin reverse-airflow ear-maize dryer model needs to be

used for dryer control during a full drying season at a commercial seed plant.

. The effect of transient weather data on the ear-maize dryer model needs further

investigation.

. The effect of hybrid on in-bin ear-maize drying needs additional research.

139

DJ

10.

11

12.

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144

10. APPENDICES

145

APPENDIX A

Field Data

146

 

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Table A.2 Transient depths and moisture contents (%w.b.) during drying.
Raw data for bin 307, moisture conetent data measured by GAC 2000.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Drying Depth Kernel Drying Depth Kernel
time (h) (m) MQ%) time (h) (m) Mfl/o
Fill - 1 Fill - 5
0 2.6 29.3 0 2.1 30.5
6 2.3 27.8 5 2.0 29.7
14 2.1 26.8 13 1.8 28.2
54 1.8 20.6 21 1.7 24.8
62 1.7 19.3 29 1.6 23.3
70 1.6 17.0 37 1.5 22.3
78 1.6 16.8 Fill - 6
Fill - 2 0 3.5 29.0
0 2.7 27.6 17 2.9 25.5
7 2.5 26.3 25 2.8 24.6
15 2.5 24.4 33 2.8 22.8
23 2.3 22.8 41 2.7 20.7
31 2.2 21.1 49 2.7 20.4
39 2.1 18.5 57 2.6 18.0
47 1.9 16.9 81 2.4 13.8
55 1.9 15.7 87 2.3 13.0
63 1.8 13.4 Fill - 7
71 1.8 13.2 0 2.2 34.8
Fill - 3 18 1.7 28.2
0 2.7 31.6 26 1.7 26.3
34 1.9 23.8 42 1.8 23.7
42 1.9 21.5 50 1.5 22.4
50 1.8 21.0 74 1.4 14.8
58 1.7 14.8
66 1.6 15.8
74 1.6 14.6
Fill - 4
0 2.5 29.8
11 1.8 26.3
19 1.6 26.7
27 1.4 22.3
35 1.3 22.3
43 1.3 19.5

 

 

153

Table A.3 Transient temperatures (°C), field data, at two locations inside the beds
during ear-maize drying.
Raw data for Bin 307 measured by HOBO data logger.

 

 

 

 

 

 

 

 

 

Temperature (°C) Temperature (°C) Temperature (°C)
Drying Drying Drying
time time time
(h) 0.6 m 1.2 m (hi 0.6 m 1.2 m (h) 0.6 m 1.2 m
Fill - 1 35 27.9 29.1 70 42.0 44.4

0 30.3 26.7 36 26.7 28.3 71 42.0 44.4
1 32.8 27.9 37 26.3 27.9 72 41.5 43.9
2 34.0 29.5 38 31.5 27.5 73 39.7 41.1
3 34.0 30.3 39 34.0 30.7 74 42.0 44.4
4 34.4 29.9 40 34.9 31.9 75 41.5 42.5
5 33.2 29.1 41 34.9 32.3 76 38.8 39.7
6 33.2 29.5 42 34.9 32.3 77 37.9 39.7
7 33.6 28.7 43 34.4 31.9 78 38.3 41.5
8 32.8 28.7 44 34.4 32.3 79 40.1 42.5
9 33.6 29.1 45 34.9 32.8 80 41.5 42.5
10 33.6 29.1 46 31.9 31.1 81 41.1 42.9
11 33.6 29.5 47 31.1 29.5 82 41.1 42.9
12 33.6 29.5 48 31.5 29.9 83 41.5 43.4
13 33.6 29.1 49 31.1 29.5 84 41.5 42.9
14 33 .6 29.1 50 31.5 29.9 85 40.6 42.0
15 33.6 29.5 51 32.3 30.7 Fill -3

16 33.6 29.1 52 32.8 31.1 0 20.2 20.2
17 33.6 29.5 53 33.2 31.5 1 29.1 25.2
18 33.6 29.9 54 33.2 31.5 2 31.5 27.1
19 33.6 29.5 55 33.2 31.5 3 32.8 28.3
20 34.0 29.5 56 31.9 34.0 4 33.2 29.9
21 34.4 29.9 57 34.4 39.2 5 32.8 29.5
22 34.0 30.3 58 35.7 39.7 6 33.6 29.1
23 33.6 29.9 59 36.1 40.1 7 34.4 29.5
24 34.0 30.3 60 36.6 40.1 8 34.9 29.5
25 34.0 30.7 61 37.0 40.1 9 34.9 30.7
26 33.6 30.7 62 38.3 42.5 10 35.3 30.7
27 33.6 30.7 63 39.2 43.4 11 33.2 30.3
28 33.6 30.7 64 40.1 43.9 12 35.7 32.8
29 33.6 31.1 65 40.1 43.4 13 35.3 32.8
30 34.0 31.5 66 39.7 42.5 14 35.3 32.8
31 33.6 31.1 67 40.6 43.9 15 35.3 32.3
32 31.9 30.7 68 41.5 43.9 16 34.9 31.5
33 30.3 30.3 69 41.5 43.9 17 34.4 31.5
34 29.1 29.9

 

154

 

Table A.3 (Cont’d)

 

Temperature (°C)

Temperature (°C)

Temperature (°C

 

 

 

 

 

 

 

Drying Drying Drying
time time time
Lh) 0.6 m 1.2 m (h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
18 35.3 32.3 56 36.6 39.7 12 32.8 31.5
19 35.3 32.3 57 36.6 38.8 13 34.4 31.9
20 35.7 32.3 58 36.6 38.8 14 35.3 32.8
21 34.0 31.5 59 36.6 38.8 15 35.7 33.2
22 34.0 29.5 60 36.1 37.9 16 35.7 34.0
23 34.0 29.9 61 35.3 36.6 17 34.9 33.6
24 34.0 30.3 62 34.9 36.6 18 34.9 33.6
25 34.0 30.7 63 34.9 37.0 19 35.7 33.2
26 34.0 31.1 64 34.9 37.0 20 34.9 33.6
27 34.0 31.5 65 36.1 39.2 21 34.4 33.2
28 33.6 31.5 66 37.4 40.6 22 34.4 33.2
29 33.6 31.5 67 37.4 40.6 23 34.4 32.8
30 34.0 32.3 68 38.3 40.6 24 34.4 33.2
31 34.0 31.9 69 38.8 41.1 25 33.2 32.8
32 34.4 31.9 70 39.2 41.1 26 34.0 32.8
33 34.9 32.3 71 39.2 41.1 27 34.0 32.8
34 34.9 32.8 72 39.2 41.1 28 33.6 32.8
35 35.3 33.2 73 38.8 40.6 29 33.2 32.3
36 35.3 32.8 74 37.9 39.7 30 33.6 32.3
37 35.3 33.2 75 37.4 39.2 31 33.6 32.8
38 31.5 34.4 76 38.8 41.5 32 34.4 33.6
39 33.2 37.0 77 39.7 41.5 33 34.4 33.6
40 34.0 37.4 78 39.7 42.0 34 34.9 34.0
41 34.4 37.9 79 40.1 41.5 35 34.9 34.4
42 34.9 38.3 80 40.1 41.5 36 34.9 34.4
43 34.0 37.4 Fill - 4 37 34.9 34.4
44 34.4 38.3 0 23.6 24.0 38 35.3 34.4
45 35.3 38.8 1 28.3 22.1 39 35.3 34.9
46 35.3 39.2 2 33.2 29.9 40 36.1 38.3
47 35.7 39.2 3 34.0 31.9 41 37.4 39.2
48 35.7 39.2 4 34.4 32.3 42 37.4 38.8
49 36.1 39.7 5 34.9 32.3 43 37.9 38.8
50 36.1 39.2 6 34.9 32.8 44 37.9 39.2
51 34.9 37.0 7 34.9 32.3 45 37.9 38.8
52 35.3 37.9 8 34.9 33.2 46 37.9 39.2
53 35.3 38.3 9 34.9 31.9 47 37.9 39.2
54 36.6 40.1 10 34.9 31.5 48 39.2 40.6
55 36.6 39.7 1 I 34.9 31.9 49 39.7 41 .l

 

155

 

Table A.3 (Cont’d)

 

Temperature (°C)

Temperature (°C)

Temperature (°C)

 

 

 

 

 

 

 

 

 

Drying Drying Drying
time time time
(h) 0.6 m 1.2 m (h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
50 40.1 41.1 17 31.9 30.7 55 40.1 41.5
51 40.1 41.5 18 32.3 31.1 56 40.1 41.5
52 39.2 40.6 19 31.5 30.3 57 40.1 41.5
53 40.6 42.0 20 31.9 31.1 58 40.6 41.5
54 41.1 42.5 21 32.8 31.5 59 40.1 41.1
55 40.6 42.0 22 32.8 31.9 60 39.7 40.1
56 41.1 42.0 23 33.2 32.3 61 38.3 38.8
57 41.1 42.0 24 33.6 32.8 62 37.9 38.8
58 40.6 41.1 25 33.6 32.8 63 38.3 39.2
59 39.7 40.1 26 34.9 34.0 64 38.3 39.2
60 38.8 39.7 27 33.6 33.2 65 38.8 39.2
61 38.3 39.2 28 33.6 32.8 66 38.3 38.8
62 38.8 39.7 29 33.6 32.8 67 38.3 38.8
63 39.2 40.1 30 34.0 33.2 68 38.3 38.8
64 40.6 41.5 31 35.7 34.4 69 38.3 38.8
65 41.1 41.5 32 35.3 34.4 Fill - 6
66 41.1 41.5 33 33.6 33.2 0 10.6 8.6
67 4| .5 42.5 34 33.6 32.8 I 31.9 19.0
68 41.5 42.0 35 35.3 33.6 2 30.3 25.2
69 39.2 39.7 36 35.7 34.4 3 30.3 25.2
Fill - 5 37 35.3 33.6 4 29.9 25.2
0 17.5 17.5 38 36.6 34.9 5 31.9 26.0
I 34.9 30.3 39 36.6 35.3 6 31.9 26.7
2 34.9 32.8 40 35.7 34.4 7 31.5 26.3
3 34.4 31.9 41 35.3 34.0 8 31.9 26.3
4 34.4 31.9 42 35.7 34.4 9 32.3 27.1
5 34.9 31.5 43 36.1 34.9 10 31.5 26.0
6 34.0 31 .l 44 36.1 34.9 11 32.8 27.5
7 34.9 31.9 45 35.3 34.4 12 32.8 28.3
8 34.9 32.8 46 35.3 34.4 13 32.8 28.7
9 35.7 33.2 47 35.3 34.0 14 32.8 29.5
10 35.7 33.6 48 35.3 38.8 15 33.6 29.9
11 35.7 33.2 49 39.2 41.1 16 33.2 29.9
12 35.3 33.2 50 39.7 41.1 17 32.8 29.5
13 34.9 32.3 51 39.7 41.5 18 32.8 29.9
14 32.8 31.1 52 40.1 41.5 19 33.2 29.9
15 32.8 30.7 53 40.1 41.5 20 32.8 29.9
16 32.3 30.7 54 40.1 42.0 21 33.6 30.7

 

156

 

 

Table A.3 (Cont’d)

 

Temperature (°C)

Temerature (°C)

Temperature (°C)

 

 

 

 

 

 

 

Drying Drying Drying
time time time
(h) ' 0.6 m 1.2 m (h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
22 33.6 30.7 60 31.1 34.0 9 34.4 32.3
23 32.3 30.3 61 31.5 34.4 10 34.0 31.1
24 31.1 30.3 62 31.5 34.4 11 32.3 30.3
25 31.1 27.9 63 31.5 34.9 12 30.3 28.7
26 31.9 28.7 64 32.3 35.3 13 30.7 29.1
27 3 I .1 27.9 65 32.3 35.3 14 31.5 29.5
28 31.5 28.7 66 32.8 35.3 15 32.8 30.3
29 31.9 29.5 67 31.9 34.4 16 33.2 30.7
30 32.3 29.9 68 31.9 34.4 17 33.2 30.3
31 32.8 29.9 69 32.8 36.6 18 32.8 30.3
32 32.8 29.9 70 34.4 37.4 19 33.2 30.3
33 33.2 30.3 71 34.9 37.0 20 33.2 31.1
34 33.6 30.7 72 34.9 37.0 21 33.2 31.1
35 32.8 30.3 73 34.9 37.0 22 32.3 30.7
36 33.6 30.7 74 33.6 33.6 23 32.8 30.7
37 34.0 31.5 75 27.1 26.7 24 33.6 30.7
38 34.4 31.9 76 32.3 35.7 25 33.6 31.1
39 34.4 32.3 77 34.9 36.6 26 34.0 31.1
40 34.4 32.3 78 35.3 37.0 27 34.0 31.5
41 34.4 32.3 79 35.7 37.0 28 32.8 31.1
42 34.9 32.3 80 35.7 37.4 29 33.6 31.1
43 34.9 32.8 81 36.1 37.9 30 34.0 31.1
44 34.9 32.8 82 36.1 37.9 31 34.0 31.1
45 34.9 32.8 83 35.7 37.0 32 34.0 31.1
46 35.3 33.2 84 36.1 37.9 33 34.4 31.5
47 34.9 33.2 85 36.1 37.4 34 34.9 31.9
48 27.5 28.7 86 36.1 37.0 35 34.0 32.3
49 27.5 31.1 87 36.6 37.9 36 34.9 32.3
50 27.5 30.7 Fill - 7 37 35.3 32.8
51 27.9 31.5 0 27.5 22.5 38 35.3 32.8
52 28.7 31.9 1 25.6 21.7 39 33.6 31.5
53 29.1 32.3 2 34.4 29.9 40 34.9 31.9
54 29.5 32.8 3 34.4 30.7 41 35.3 32.3
55 29.5 32.8 4 33.2 30.7 42 35.3 32.8
56 30.3 33.2 5 34.9 31.1 43 35.3 32.8
57 29.5 32.8 6 35.3 31.5 44 35.3 32.8
58 30.3 33.2 7 35.3 32.3 45 35.3 33.2
59 30.7 34.0 8 34.4 32.3 46 34.9 33.2

 

157

 

Table A.3 (Cont’d)

 

 

 

 

 

Temperature (°C) 1 Temperature (°C)
Drying ti 0.6 m 1.2 m Drying ti 0.6 m 1.2 m
47 35.7 33.2 85 39.2 40.1
48 35.7 34.4 86 39.2 39.7
49 36.1 33.6 87 38.8 39.7
50 31.5 34.9 88 38.3 38.8
51 33.2 36.6 89 38.3 39.2
52 34.4 37.4
53 34.9 37.9
54 34.9 37.9
55 35.3 37.9
56 35.3 38.3
57 35.7 38.3
58 36.1 38.3
59 36.1 38.3
60 36.1 38.3
61 36.1 38.3
62 36.6 38.8
63 37.0 39.2
64 37.9 39.7
65 37.9 39.7
66 37.9 39.7
67 37.9 39.2
68 37.4 38.8
69 37.4 38.8
70 36.6 37.4
71 36.1 37.4
72 36.1 37.4
73 36.6 37.9
74 36.6 36.6
75 35.7 37.4
76 36.1 37.4
77 36.6 37.4
78 36.6 37.4
79 36.6 37.4
80 36.6 37.4
81 34.4 34.9
82 36.1 37.4
83 37.4 38.8
84 38.3 39.7

 

 

 

158

Table A.3 (Cont’d)

 

 

 

 

 

 

 

Raw data for Bin 317 measured by HOBO dag Logger.
Temperature (°C) Temperature (°C) Temperature (°C)
Drying Drying Drying
time time time
(h) 0.6 m 1.2 m (h) 0.6 m 1.2m (h) 0.6 m 1.2 m
Fill - 1
1 31.5 28.3 36 27.5 29.1 71 42.0 43.4
2 31.9 29.5 37 26.7 28.3 72 42.5 44.4
3 32.8 29.1 38 26.7 26.0 73 42.0 41.5
4 32.8 29.5 39 33.2 29.9 74 42.9 44.9
5 32.8 29.9 40 34.0 31 .5 75 43.4 44.9
6 32.8 29.9 41 34.4 31.9 76 40.6 42.0
7 31.9 29.5 42 34.4 31.9 77 39.7 40.6
8 31.9 28.7 43 34.4 31.9 78 38.8 39.7
9 32.8 29.1 44 34.4 31.9 79 41.5 42.5
10 33.2 29.9 45 34.9 32.8 80 42.5 43.4
1 1 33.6 29.9 46 32.8 31.9 81 42.0 42.9
12 33.6 29.9 47 31.1 30.3 82 42.0 42.9
13 33.6 30.3 48 31.5 29.9 83 42.0 43.4
14 33.6 30.3 49 30.7 29.5 84 42.0 42.9
15 33.6 30.3 50 31.1 29.5 85 41.1 41.5
16 33.6 30.3 51 32.3 30.7 86 41.1 41.5
17 33.2 30.7 52 33.2 31.5 87 40.6 41.5
18 32.8 30.7 53 33.2 31.5 Fill - 2
19 33.2 30.7 54 33.6 31.9 0 29.9 27.5
20 33.6 30.7 55 33.6 31.9 1 31.5 27.9
21 34.0 30.7 56 34.4 31.9 2 31.5 28.7
22 34.4 30.3 57 34.4 39.7 3 32.3 28.7
23 33.6 30.3 58 36.1 41.1 4 32.8 28.7
24 33.6 30.3 59 37.0 40.6 5 32.8 29.5
25 34.0 30.7 60 37.0 41.1 6 33.6 29.5
26 34.0 31.1 61 37.4 41.1 7 34.4 29.9
27 34.0 31.5 62 38.3 41.5 8 34.4 30.3
28 34.4 31.5 63 40.6 43.9 9 34.9 30.7
29 34.0 31.9 64 40.6 44.4 10 34.9 30.3
30 34.4 31.9 65 41.5 44.4 I 1 34.9 30.7
31 34.4 32.3 66 41 .1 43.4 12 35.3 30.7
32 32.8 31.5 67 40.6 44.4 13 35.3 30.7
33 3| .5 31.1 68 42.9 45.4 14 34.9 30.7
34 30.3 30.7 69 42.9 45.4 15 34.4 30.3
35 28.7 29.9 70 42.9 45.4 16 34.4 30.3

 

 

 

 

 

159

Table A.3 (Cont’d)

 

 

 

 

 

 

Temperature (°C) Temperature (°C) Tempergure (°C
Drying Drying Drying
time time time

(h) 0.6 m 1.2 m (h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
17 34.0 30.3 55 34.9 32.8 Fill - 3
18 34.0 30.3 56 35.3 33.2 0 22.5 21.0
19 34.0 30.7 57 36.1 34.0 1 29.9 26.0
20 34.0 30.7 58 36.1 34.4 2 31.5 27.5
21 34.0 30.7 59 36.6 34.4 3 31.9 27.5
22 34.0 30.7 60 35.7 34.4 4 32.8 26.7
23 34.4 31.1 61 35.3 33.6 5 31.1 27.1
24 33.2 30.7 62 35.3 33.6 6 31.5 27.9
25 32.8 29.9 63 35.3 33.6 7 3| .5 28.3
26 33.6 29.9 64 35.7 34.0 8 31.9 28.3
27 34.4 29.9 65 31.1 31.9 9 31.9 28.7
28 34.0 30.3 66 31.9 35.3 10 32.3 28.7
29 33.6 30.3 67 33.2 36.1 11 32.8 28.7
30 34.0 30.3 68 34.0 36.6 12 32.8 28.7
31 34.4 30.7 69 34.0 36.6 13 33.2 28.7
32 34.0 30.3 70 32.8 35.7 14 33.2 29.1
33 34.0 29.9 71 34.9 37.4 15 34.4 29.5
34 34.0 30.3 72 36.1 38.3 16 33.6 29.5
35 34.0 30.3 73 36.1 37.9 17 33.2 29.5
36 32.3 30.7 74 36.6 38.8 18 33.2 29.5
37 31.9 29.9 75 37.0 39.2 19 34.9 30.7
38 32.3 29.9 76 37.4 39.7 20 36.1 31.9
39 32.8 30.3 77 37.9 39.7 21 35.3 31.5
40 32.8 29.9 78 37.4 39.2 22 36.1 31.9
41 33.2 30.7 79 37.9 39.7 23 35.3 31.9
42 33.6 30.7 80 38.3 40.1 24 36.1 32.8
43 34.0 31.1 81 38.3 40.1 25 36.1 33.2
44 34.0 31.5 82 38.3 40.1 26 35.7 33.2
45 34.4 31.5 83 38.8 40.6 27 36.1 33.6
46 34.0 31.5 84 39.2 41.1 28 35.3 32.3
47 34.4 31.9 85 39.7 41.1 29 35.7 34.0
48 34.9 32.3 86 39.7 41.1 30 34.0 32.8
49 34.9 32.3 87 39.2 40.6 31 34.9 33.6
50 34.4 32.3 88 40.1 41.5 32 35.3 33.6
51 34.9 32.3 89 40.6 41.5 33 35.7 34.0
52 34.9 32.3 90 40.1 40.6 34 35.7 34.0
53 34.4 31.9 91 39.2 40.1 35 35.7 34.0
54 34.9 32.8 92 39.2 40.1 36 36.1 34.4

 

160

 

Table A.3 (Cont’d)

 

Temperature (°C)

Temperature (°C)

Temperature (°C)

 

 

 

 

 

 

 

Drying Drying Drying
time time time
(h) 0.6 m 1.2 m (h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
37 36.1 34.4 75 36.6 38.3 21 34.9 34.0
38 35.3 34.0 76 35.7 37.4 22 34.9 34.0
39 35.7 34.4 77 35.3 37.4 23 36.1 34.4
40 35.3 34.0 78 36.6 38.8 24 36.6 35.3
41 34.4 32.8 79 37.4 39.2 25 36.6 34.4
42 36.6 34.4 80 37.9 39.7 26 35.3 34.0
43 37.0 34.9 81 37.9 39.7 27 35.7 34.0
44 36.1 34.4 82 37.9 39.7 28 35.7 34.0
45 37.4 35.3 83 38.3 40.1 29 34.9 33.6
46 28.3 30.7 84 38.3 40.1 30 34.9 33.6
47 29.5 32.8 85 38.3 39.7 31 35.3 34.0
48 29.9 33.2 86 38.8 40.1 32 35.3 34.4
49 30.3 33.6 87 38.8 40.1 33 35.3 34.4
50 3 | .1 34.4 88 37.9 38.8 34 35.3 34.4
51 31.1 34.9 89 37.4 38.3 35 34.9 34.0
52 31.5 34.9 90 37.9 39.2 36 34.4 33.2
53 31.1 34.0 Fill - 4 37 34.9 33.6
54 31.1 34.4 0 26.3 24.4 38 34.0 33.2
55 31.5 35.3 1 31.1 28.7 39 34.0 32.8
56 32.8 36.1 2 32.8 29.5 40 33.6 32.8
57 32.8 36.1 3 32.8 29.9 41 34.0 32.8
58 33.2 36.1 4 33.2 29.9 42 33.6 32.3
59 33.2 36.1 5 33.6 30.7 43 34.0 33.2
60 33.6 36.6 6 33.2 31.1 44 34.4 33.6
61 33.6 36.1 7 33.2 31.5 45 34.9 33.6
62 32.8 34.4 8 34.0 31.9 46 30.7 34.0
63 32.8 34.0 9 33.6 31.9 47 31.9 35.3
64 31.5 34.0 10 34.4 32.3 48 32.8 35.7
65 31.9 34.9 11 34.4 32.3 49 32.3 34.9
66 32.3 34.9 12 34.0 31.9 50 33.2 36.1
67 33.2 36.1 13 34.0 31.5 51 33.6 37.0
68 32.8 34.4 14 34.9 32.3 52 34.0 37.0
69 34.0 37.4 15 35.3 32.8 53 34.4 37.0
70 34.9 37.9 16 34.9 32.8 54 34.4 37.4
71 35.3 37.9 17 34.9 32.8 55 34.9 37.9
72 35.7 38.3 18 34.0 32.8 56 34.0 36.6
73 36.1 38.3 19 33.6 33.2 57 33.6 36.1
74 36.1 38.8 20 34.0 32.8 58 33.2 35.3

 

161

 

Table A.3 (Cont’d)

 

 

 

 

 

Temperature (°C)
Drying

time

(h) 0.6 m 1.2 m
59 33.2 35.7
60 33.6 36.6
61 34.9 37.9
62 35.7 38.3
63 36.1 38.3
64 36.6 38.8
65 36.1 38.8
66 35.3 37.4
67 35.7 37.4
68 35.3 37.0
69 35.3 37.0
70 34.9 37.0
71 35.3 37.0
72 35.3 37.0
73 35.7 38.3
74 37.9 40.1
75 37.9 40.1
76 38.3 40.1
77 38.3 40.1
78 38.8 40.1
79 38.8 40.6
80 38.8 40.6
81 38.3 39.7
82 38.3 39.7
83 37.9 39.2
84 38.3 39.2
85 37.9 38.8
86 37.4 37.0
87 37.4 39.2
88 38.8 40.6
89 40.1 41.5
90 40.1 41.1
91 40.6 40.6
92 40.6 40.1
93 40.6 39.7

 

 

162

Table A.3 (Cont’d)

 

 

 

 

 

 

 

 

 

Raw data for Bin 319 measured by HOBO data logger.
Temperature (°C) Temperature (°C) Temperature (°C)
Drying Drying Drying
time time time
(h) 0.6 m 1.2 m (h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
Fill - 3
0 17.9 15.6 35 34.0 31 .9 70 41.1 41.5
1 30.7 22.5 36 35.3 33.6 Fill - 4
2 31.9 25.2 37 36.6 38.8 1 29.9 22.9
3 33.2 27.5 38 37.4 39.7 2 32.3 29.1
4 32.8 27.9 39 37.9 40.1 3 33.2 30.7
5 31.5 27.5 40 38.8 40.6 4 34.0 31.9
6 31.5 27.5 41 38.8 41 .5 5 34.4 32.3
7 31.9 27.1 42 38.3 41.1 6 34.4 32.3
8 32.3 28.7 43 38.8 41.1 7 34.0 31.9
9 32.3 28.3 44 39.2 41.1 8 34.4 33.2
10 31.9 28.3 45 39.2 41.1 9 34.0 31.9
1 1 32.8 29.5 46 39.7 41.1 10 34.0 32.3
12 33.2 29.5 47 39.7 41.1 11 34.4 32.8
13 33.2 29.5 48 39.7 41.1 12 34.9 32.8
14 32.8 29.5 49 39.2 40.6 13 35.3 32.8
15 32.8 29.9 50 37.9 39.2 14 35.3 32.3
16 33.6 31.5 51 38.3 39.7 15 35.3 32.3
17 34.4 31.9 52 38.8 40.6 16 35.3 32.3
18 34.0 31.9 53 39.2 40.6 17 35.3 33.6
19 34.4 32.3 54 38.8 40.1 18 35.3 33.2
20 33.2 30.7 55 39.2 40.1 19 35.7 33.2
21 33.2 31.5 56 38.8 39.7 20 34.4 33.6
22 33.6 31 .5 57 39.2 39.7 21 35.7 33.6
23 33.6 31.9 58 39.2 39.7 22 36.6 34.0
24 34.0 31.5 59 37.9 38.8 23 35.3 33.6
25 34.4 31.9 60 37.0 37.9 24 35.7 34.0
26 34.4 31 .9 61 37.0 37.4 25 34.9 33.6
27 32.8 31.1 62 37.4 38.3 26 33.6 32.3
28 33.2 30.7 63 37.4 38.3 27 34.0 32.3
29 33.2 31.1 64 39.7 40.6 28 34.4 32.8
30 33.6 31.9 65 41.1 42.0 29 34.0 32.3
31 33.6 31.9 66 41 .| 42.5 30 34.0 32.8
32 34.4 32.3 67 41.1 42.0 31 34.0 32.8
33 34.4 32.8 68 41.1 41.5 32 34.4 32.8
34 34.9 32.8 69 41.5 42.5 33 33.6 33.2

 

 

 

Table A.3 (Cont’d)

 

 

 

 

 

 

 

 

Temperature ( °C) Temperature ( °C) Temperature ( °C)
Drying Drying Drying

time time time

(h) 0.6 m 1.2 m (h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
34 32.8 31.9 72 40.6 41.5 30 35.3 33.6
35 32.8 31.9 73 40.6 41.5 31 34.9 33.6
36 32.3 31.9 74 41.1 41.5 32 35.3 33.6
37 34.4 32.8 75 41.5 42.0 33 35.3 32.8
38 34.4 33.2 76 41.] 41.5 34 35.7 33.2
39 34.9 33.6 77 39.2 39.7 35 36.1 34.0
40 33.6 37.0 78 39.2 39.7 36 36.1 33.6
41 37.0 39.7 79 38.8 38.8 37 36.6 34.0
42 37.9 39.7 Fill - 5 38 36.1 34.0
43 37.9 39.7 1 31.1 27.1 39 36.1 34.4
44 37.9 39.2 2 31.5 28.7 40 36.1 34.0
45 37.9 39.7 3 32.3 29.5 41 36.1 34.4
46 37.4 39.2 4 32.8 29.9 42 36.1 34.4
47 37.9 39.2 5 32.8 30.3 43 36.1 34.4
48 37.9 39.2 6 33.2 31.5 44 36.1 34.9
49 37.9 39.2 7 33.6 31.5 45 36.1 34.4
50 37.9 39.7 8 33.2 31.1 46 36.6 34.4
51 38.3 39.7 9 33.2 30.3 47 36.6 34.9
52 38.3 39.7 10 34.9 31.1 48 36.1 34.9
53 38.3 39.7 11 35.7 31.5 49 33.2 33.2
54 38.3 39.2 12 35.7 32.3 50 31.1 30.7
55 38.3 39.7 13 33.2 30.7 51 31.1 30.3
56 39.2 40.1 14 33.2 30.7 52 34.9 37.0
57 39.7 40.6 15 33.2 30.7 53 35.3 37.0
58 40.1 41.5 16 33.2 30.7 54 34.9 36.1
59 40.6 41.5 17 34.0 30.7 55 36.1 37.9
60 39.7 40.6 18 34.4 31.5 56 36.6 38.3
61 40.1 41.1 19 34.0 31.5 57 36.1 37.9
62 40.6 41.5 20 33.2 31.5 58 36.1 37.9
63 40.6 41.5 21 32.8 31.1 59 35.7 37.9
64 40.6 41.5 22 33.2 31.9 60 36.6 38.3
65 40.6 41.5 23 33.6 31.9 61 36.1 37.9
66 40.6 41.5 24 33.6 31.1 62 36.1 37.9
67 40.1 41.1 25 34.0 31.5 63 36.6 38.8
68 40.1 41.1 26 34.4 32.3 64 37.4 39.2
69 40.1 40.6 27 34.4 32.3 65 37.0 38.8
70 40.1 41.1 28 34.4 33.2 66 37.9 39.2
71 40.6 41.1 29 34.9 33.6 67 37.0 38.8

 

164

 

Table A.3 (Cont’d)

 

 

 

 

 

 

 

 

Temperature ( °C) Temperature ( °C) Temperature ( °C)
Drying Drying Drying
time time time
(h) 0.6 m 1.2 m (h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
68 37.0 38.8 14 33.6 30.7 52 35.3 37.4
69 37.4 38.8 15 32.3 29.9 53 35.3 37.0
70 37.4 39.2 16 31.5 29.9 54 35.7 37.4
71 38.8 40.1 17 30.7 29.5 55 30.3 28.3
72 38.8 40.1 18 29.9 29.1 56 29.1 33.2
73 38.8 40.6 19 29.1 28.7 57 34.9 37.0
74 39.2 41.1 20 28.3 27.9 58 35.7 37.4
75 38.3 39.2 21 27.5 27.1 59 36.1 37.4
76 38.8 39.7 22 31.1 27.1 60 36.1 37.4
77 40.1 41.5 23 33.6 29.9 61 36.6 37.9
78 40.1 41.5 24 34.0 30.7 62 36.6 37.9
79 40.1 41.1 25 34.4 31.1 63 36.6 37.4
80 39.7 40.6 26 34.4 31.5 64 35.3 37.0
81 39.7 40.6 27 34.0 31.9 65 36.6 37.9
82 39.7 40.6 28 34.4 31.9 66 36.6 37.4
83 40.6 41.5 29 35.3 32.8 67 36.1 37.4
84 39.7 41.1 30 35.3 32.8 Fill - 7
85 39.7 41.1 31 34.9 31.9 0 27.1 26.7
86 40.1 41.1 32 34.9 32.3 1 30.3 25.6
87 40.1 41.1 33 35.3 32.3 2 31.9 28.7
88 40.1 41.1 34 35.3 33.2 3 30.7 28.7
89 39.7 41 .l 35 35.7 33.6 4 31.9 28.7
90 40.1 41.1 36 35.7 33.6 5 33.2 29.9
Fill - 6 37 35.7 33.6 6 33.6 30.3
0 24.4 23.2 38 35.3 32.8 7 32.8 29.5
1 29.5 23.6 39 31.5 31.1 8 33.2 29.5
2 31.1 26.3 40 30.7 34.0 9 33.2 29.5
3 32.3 27.5 41 31.5 34.4 10 30.7 28.7
4 32.8 28.3 42 31.5 34.4 11 30.3 27.5
5 33.2 29.1 43 31.9 34.9 12 30.7 28.3
6 33.2 29.1 44 32.3 35.3 13 31.9 29.1
7 33.2 29.1 45 32.8 35.3 14 32.3 29.9
8 33.2 29.1 46 32.8 35.3 15 31.9 29.9
9 33.2 29.5 47 33.2 35.3 16 32.3 30.3
10 33.2 29.9 48 32.8 34.4 17 32.3 30.3
11 33.2 29.9 49 32.8 35.7 18 32.3 30.3
12 33.2 29.9 50 35.3 38.3 19 32.8 30.7
13 33.6 30.3 51 35.7 37.9 20 32.8 30.7

 

 

 

 

165

Table A.3 (Cont’d)

 

 

 

 

 

 

 

Temperature (°C) Temperature (°C)
Drying Drying

time time

(h) 0.6 m 1.2 m (h) 0.6 m 1.2 m
21 33.2 30.7 59 35.3 37.4
22 33.2 31.1 60 35.3 37.9
23 34.9 32.3 61 35.7 38.3
24 35.3 33.6 62 35.7 37.9
25 35.7 34.0 63 35.7 38.3
26 36.1 34.0 64 36.6 38.8
27 35.3 34.0 65 37.0 38.8
28 34.4 33.2 66 37.0 38.8
29 34.4 33.2 67 37.0 38.3
30 34.4 33.2 68 36.6 38.3
31 32.3 31.1 69 36.1 37.0
32 34.9 33.6 70 35.3 36.6
33 35.3 34.4 71 35.3 36.6
34 35.7 34.4 72 35.7 37.0
35 35.7 34.9 73 35.3 37.0
36 36.1 34.9 74 34.0 35.7
37 36.1 35.3 75 35.3 36.6
38 35.7 34.9 76 35.3 37.0
39 35.7 34.9 77 35.7 37.0
40 35.7 34.9 78 35.7 36.6
41 35.7 34.4 79 35.3 37.0
42 35.7 34.4 80 34.9 34.4
43 29.1 31.1 81 34.4 36.1
44 30.7 33.2 82 36.1 37.0
45 3 | .5 34.4 83 37.9 38.8
46 31.5 34.0 84 38.3 39.2
47 31.9 34.4 85 38.8 39.2
48 32.3 35.3 86 38.3 38.8
49 31.9 34.9 87 37.9 38.3
50 32.3 35.3 88 37.9 38.3

32.8 35.7 89 37.9 38.3

52 33.6 37.0 90 40.1 41.1
53 34.0 37.0
54 34.4 37.4
55 33.6 37.0
56 34.9 37.9
57 34.9 37.9
58 35.3 37.9

 

 

166

APPENDIX B

Equipments Specifications

167

8.] Grain Analysis Computer, Model GAC2000 (Figure 8.1).
(DICKEY-John Corporation. Auburn, Illinois)

 

Measurement
Method:

Applications:

Measured
Components:

Accuracy:
Repeatability:
Resolution:

Grain
Temperature:

Temperature
Difference:

Sample
Presentation:

Sample
Adjustment:

Languages:

Display Format:

Display Content:

Construction:
Operating
Temperature:
Humidity:
Power Supply:

Power
Consumption:

Capacitance. conductance. weighing device

C orn. Soybeans, Wheat. Barley, Sorghum. Cereal Grains. Oilseeds.
Beans. etc. Over 450 grain calibrations on file.

Moisture Content: 0 to 50%

Moisture Content: Meets or exceeds NTEP requirements
Moisture Content: i0.1%
0. 1% moisture

NTEP 0 to 40°C (32 to 104°F)
Non-NTEP: 0 to 50°C (32 to 122°F)

20°C (68°F). room to grain
Whole Grain

None

English. F rench. German. Spanish. Italian. Portuguese. Flemish and
more

Large LCD (240 x 64 pixels)

Grain Name. Calibration Name. Moisture %. Sample Temperature.
Approximate Density (approximate Test Weight)+-1-

Painted steel. ABS plastic. polycarbonate

NTEP: 10 to 40°C (50 to 104°F)
Non-NTEP: 5 to 45°C (41 to 113°F)

5 to 95% (non-condensing)
85—264 Vac. 48-62 Hz

20 VA

 

168

8.2 HOBO® H8 Outdoor/Industrial
4-Channel External Logger (Figure B.2).
(Onset Computer Corporation. Pocasset. Massachusetts)

 

Operating environment: weatherproof case. tested to NEMA 6
AC current. 0-2.5 Volt DC and 4-20 mA

Capacity: 32.520 measurements total

Sampling interval: 0.5 seconds to 9 hours. recording times up to 1 year
Readout and relaunch with HOBO Shuttle

Programmable start time/date

Operating range: -20°C to +70°C (-4°F to +158°F)

Size: 5.50" x 5.38" x 1.25" (140 x 137 x 32mm)

Weight: approx. 7 oz. (200 grams)

User-replaceable battery lasts 1 year

Time accuracy: :tl minute per week at +20°C (+68°F)

 

 

B.3 Wide-range temperature sensors (TMC20-HA) (Figure B.2).

 

Range: -40°C to 100°C (-40°F to 212°F) in air

Accuracy: 105°C at 20°C (i0.9°F at 70°F). (insert probe 0.9" min.) Accuracy
specifications for sensors reflect the combined performance of the logger and sensor
Drift: < 01°C (02°F ) per year

Resolution: i0.41°C at 20°C. (i0.7°F at 70°F)

Response time in air: 2.5 minutes typical lm/sec (22mph)

Response time in stirred water: 30 seconds typical

5mm (0.2") diameter sensor (fits in 6.4mm (1/4") holes)

 

169

 

B.4 HOBO Shuttle (Figure B.2).

 

Verifies operation of HOBO H8 Family

Synchronizes HOBO data logger's clock with the PC's clock

Resynchronized relaunch: sets the HOBO's sample times to be synchronized with the
prior launch's sample times

Uploads the same data to a PC as if the data were read out directly from the logger
User-replaceable batteries last 1 year

Data offload time from logger: 90 seconds typical from 8K logger. about 5 min from
32K logger and about 1 min from 64K HOBO Pro logger

Data readout time to PC: 2 minutes typical for full shuttle

Operating temperature range: -4°F to +158°F (-20°C to +70°C)

Operating humidity range when case is open: 0 to 95% relative humidity. non-
condensing

Time accuracy: il minute per week at +68°F (20°C). The HOBO Shuttle's clock is
synchronized to the computer clock when the HOBO Shuttle is read out or launched
Size: 108 x 89 x 19 mm (3.5 x 4.3 x 0.8"). adjustable cord attached

Weight: approx. 83 grams (2.9 oz.)

 

 

B.5 BoxCar Pro 4.3 Software (Figure B.2).

Windows application for logger and data management. It offers added features for
graphing. data analysis. data export and simultaneous management of multiple loggers.

170

 

Figure B. 1 Grain Analysis Computer, Model GAC2000.

  

Single
button

operation

9080 .

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Sensor Logger Shuttle . LEDs

Show
Cable (20 ft. long) status

Figure B. 2 HOBO® H8 Outdoor/Industrial 4-Channel External Logger,
temperature sensors (TMC20-HA), HOBO Shuttle.

171

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