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This is to certify that the

thesis entitled

Drying of Alfalfa Under Controlled Temperatures and
Vapor Pressure Conditions.

presented by
Natalie Jo Wentz-Carroll

has been accepted towards fulfillment
of the requirements for

M . S . degree inAgricultural Engineering

5 /7/ ///"'

Major professorLS

Date W

0-7 639

MS U is an Affirmative Action/Equal Opportunity Institution

 

 

MSU

LIBRARIES

v

 

 

RETURNING MATERIALS:

Place in book drop to
remove this checkout from
your record. FINES will
be charged if book is
returned after the date
stamped below.

 

DRYING OF ALFALFA UNDER CONTROLLED

TEMPERATURE AND VAPOR PRESSURE CONDITIONS

BY

Natalie Jo Wentz-Carroll

A THESIS

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

MASTER OF SCIENCE

Department of Agricultural Engineering

1984

ABSTRACT

DRYING OF ALFALFA WITH CONTROLLED
TEMPERATURE AND VAPOR PRESSURE

BY

Natalie Wentz-Carroll

A controlled environment drying chamber was built and
used to study the drying of alfalfa at various temperatures
and vapor pressures. Several samples were treated with
potassium carbonate to determine the effect this chemical
had on the drying rate of alfalfa.

Alfalfa samples were cut and placed in the drying
chamber where the weight change was monitored to determine'
the rate of drying. Drying rates were compared by
calculating an average drying constant, an average mass
transfer coefficient and by exponential curve fitting.

Results show that drying constant and the mass transfer
coefficient both can be used to model the drying curves of
alfalfa. Drying curves were analyzed in three intervals,
with cutoff points of 2.0 and 1.0 % moisture (dry basis).
Drying temperatures had a direct relationship to the drying
rate.

Potassium carbonate treatments speeded the drying of
alfalfa, espically for moistures above 1.0 %.

This work is Dedicated to My Husband,

Dale Michael Carroll

ii

ACKNOWLEDGMENTS

Throughout the course of this graduate program the
author has been very appreciative of the exceptional
assistance provided by her major professor, Dr. C. Alan
Rotz. His help, concern and consideration were unremitting.

The author also wishes to express her gratitude for the
time and involvement of Dr. R. Brook and Dr. J.W. Thomas
throughout the duration of her research.

The faculty and staff of the Michigan State
Agricultural Department were always an infinite source of
support, suggestion and concern for which the author thanks
them.

The assistance of Dr. R. Byler and D. Sprott was
always useful and appreciated.

iii

TABLE OF CONTENTS

LIST OF TABLES .0.00.000000000000000000000...0.0.0.0....v1

LIST OF FIGURES I.0..0....O...OOOOOOOOOOOOOOOOOOOOOOOOOOVj-ii

CHAPTER PAGE

1. INTRODUCTION O...O0.0.0.0....OOOOOOOOOOOOOOOOOOOOOOO 1

PrOdUCtion 0..OOOOOOOOIOOOOOOIOOOOOOOOOOOOOOOOOOOOO.

DrYing OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO0.0.00.0.0...

NM

2. OBJECTIVES 0..0......0....OOOOOOOOOOOOOOOOOOOOOOO0.. S

3. LITERATURE REVIEW

Plant Structure .................................... 6
Stomata and Guard Cells ........................ 8
Cuticle ....................................... 10

Water Loss ........................................ 11

Dry Matter and Respiration Losses ................. 13

Increasing Drying Rates ........................... 14
Mechanical Treatments to speed drying ......... 15
Chemical Treatments to speed drying ........... 16

Radiation ......................................... 18

Drying Analysis ................................... 18

4. EQUIPMENT
Chamber and Air Handling 0.0.0000000000000000000000 22

Microcomputer and Control ......................... 29
SO£tware 0.0.00IOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO. 36

iv

5. DATA COLLECTION 0......OOOOOOOOIOOOOOOOOO00.0.00... 39

6. ANALYSIS OOOOOOOOOOOO0.0...OOOOOOOOOOOOOOOOOOOOOOO. 44
DrYing constant OI.0.0.0.000...OOOOOOOOOOOOOOOOOOOO‘

. . 45
Mass Transfer Coefficient .........................
Exponential curve Fitting O O O O O O O O O O O O O O O O O O O O O O O O 0 ga

7. RESULTS AND DISCUSSION ............................ 51
crop Maturity 0....0.0.0....OOOOOOOOOOOOOOOOOOOOOOO 56
DrYing constant 0 O O O O O O O O O O I O O O I O O O O O O O O O O O O O O O I O O O 59
M355 TranSfer COeffiCient I O O O O O O O O O O O O O O O O I O O O O O O O 63

Exponential Curve Fitting ......................... 66
Chemical Treatment O...OOOOOOOOOOOOOOIOO0.0.00.0... 66

8. SUMRY 00....COO....0.0...OOOOOOOOOOOOOOOOOOOOOOOO
BIBLIOGRAPHY OI...OO...OOOOOOOOOOOOOOOOOOOOOOOOOOOOO... 80

APPENDI XES
A. Computer Programs ............................. 86a
B. Hourly Listing of Sample Data ................. 90
C. Drying Constants and Mass Transfer Coefficients 100
D

. Plot of the Sample Moisture Content over Time 109

LIST OF TABLES

Table

1. Regression Coefficients for the Temperature
sensor calibration OOI.00......OOOOOOOOOOOOOOOOOOO.

2. Regression Coefficients to Obtain the Sample
Mass used in Trials 1-25 OOOOOOOOOOOIOOOOOOOOOOOOO.

3. Alfalfa Maturity and Time of Cutting for all

Trials 0......O...00.0.00...OOOOOOOOOOOOOOO00......

4. Temperature Settings, Drying Temperatures,
Vapor Pressures 'and Concentration of
Moisture in the Drying Air for all Trials .........

5. Volume of Water Displaced by Alfalfa at
Different MOiSture contents OOOOOOOOOOOOOOOOOOOOOOO

6. Drying Constant and Mass Transfer
Coefficient for Alfalfa Trials at Two Vapor
Pressures and Different Temperatures ..............

7. Drying Constant and Mass Transfer
Coefficient for Alfalfa Trials at Two
Temperatures and Different Vapor Pressures ........

8. Mean Drying Constant for Three Moisture
Content Intervals with Different
Temperatures and Vapor Pressures, and the '
Results of an Analysis of Variance on this

Data .0.COO...O0......0..OOOOOOOOOOOOOOOOOO00......

9. Mean Mass Transfer Coefficient for Three
Moisture Content Intervals with Different
Temperatures and Vapor Pressures, and the
Results of an Analysis of Variance on this

vi

10.

11.

12.

13.

Data 0.00.0000...0.0.0.0...OOOOOOOOOCCOCOOOCOOO0.0.

Coefficients of the Two Exponential Curve
Fit to the Data, in Order of Increasing
Temperatures 00......OOOOOOOOOOOOOOOOOOOOOOO0.0.0..

Coefficients of the Two Exponential Curve
Fit to the Data, in Order of Increasing
vapor Pressures 0.00.0.0...OOOOOOOOOOOOOOOOOOOOOOO.

Mean Drying Constant and Mass Transfer
Coefficient for Three Moisture Content
Intervals at Four Environmental Conditions
With Treated Alfalfa 0.0000000000000000000000......

Comparison of the Drying Constant and the
Mass Transfer Coefficient Values for Treated

' Alfalfa Dried Simultanously with Control

Alfalfa OOOOOOOOOOOOOOOOOOCOOOOOOOOOOOOOOOOOC0.0...

CALCKHD, Program to Calculate the Drying
Constant and the Mass Transfer Coefficient ........

BMDPBR, Program to Calculate a Two
Exponential Best Fit Curve using BMDP
(BiOMedical Data PaCkage) 00......OOOOOIOOOOOOOO...

Listing of the Moisture Contents on an
Hourly Basis for all Trials .......................

Output form CALCKHD, Drying Constants and
Mass Transfer Coefficients .......................

vii

68

69

71

863

89

90

100

LIST OF FIGURES

Figure Page

1. Plan View of the Equipment, Drying Chamber,
Aminco Aire Unit, and the Microcomputer ............... 23

2. Cutout View of the Drying Chamber Showing
Transducers, Sensors, Baffle and Alfalfa
”Olders 0.0.0.0000...0.0.0.000...OOOOOOOOOOOO0.00...... 24

3. Detail View of the Alfalfa Holders and
TranSducer Mount 0.0.0.0....OOOOOCOOOOOOOOOIO00......O. 26

4. Block Diagram of the Process Controller and
Data COlleCter .0.0000000000000000000000000000000000.0. 31

5. Plot of the Change in Moisture Content of
the Actural Data over Time for Different
Temperatures at Two Vapor Pressures ................... 57

6. Plot of the Change in Moisture Content of
the Actual Data over time for Different
Vapor Pressures at Two Temperatures ................... 58

7. Example of the Two Exponential Curve Fit
(amp) for Tria151and2 OOOOOIOOOOOOOO0.0.0.0000...O. 67

8. Linear Regression Curves for Drying Constant
Based on the Drying Temperature for Treated
and Control Samples at Three Moisture
Content Intervals ..................................... 73

9. Plot of the Two Exponential Equations of

Best Fit Generated Data on Potassium

carbonate 0.00.0.0.0.00000000000000000000000000......O. 75
D. Plot of the Actual Change in Moisture over

viii

Time for all Trials 0000......OOOOOOOOOOOOOOOOOOOOOOOO 109

ix

CHAPTER 1

INTRODUCTION

Alfalfa has been cultivated throughout recorded history
(Bolton, 1962). Columella (approximately 60 AD) praised it
for its longevity, soil improving qualities, ability to be
cut four times a year, and for its feed value for cattle and
horses. Alfalfa is now grown worldwide in the temperate
zone and has been adapted to many soil types, rainfall
amounts and a range of elevations.

The major benefits of alfalfa over grasses are an
increase in protein, vitamin and mineral content as well as
greater yield, drouth resistance and nitrogen fixing
capability. Compared to other forage legumes in the North
Central area of the United States, alfalfa provides a higher

yield.

Production

In 1981 alfalfa hay (including alfalfa mixes) was grown
on 10.7 million ha in the United States yielding 7.6 million
tonnes of hay (Agricultural Statictics, 1982). This was 44%
of the total area harvested for hay (24.4 million ha) and
58% of the total number of tonnes produced. The production
value was given as 9.2 billion dollars.

In Michigan, alfalfa production exceeds that of all
other forages combined. In 1981, 0.5 million ha were
planted in alfalfa (79% of total hay area) which yielded 3.0
million tonnes (85% of all hay production). Michigan
produces about 5% of the alfalfa grown in the United States.
Michigan averaged 6 tonnes of hay and haylage per hectare in
1981 generally produced with a 3 cut system. (Michigan

Agricultural Statistics, 1982).

Drying

When alfalfa is cut the plant generally contains 75-80%
water. For safe storage as baled hay, the moisture content
must be reduced to below 20%, wet basis. Usually this is
accomplished by leaving the cut crop to dry in the field
with occasional turning. When the desired moisture content

is achieved, the alfalfa is baled and taken off the field.

The drying process usually takes 2-4 days if no rain occurs.
Rain on a cut plant causes leaching and microbial growth.
This reduces protein and nutrient value, and increases dry
matter loss.

Respiration, the oxidation of organic compounds causing
a release of energy, occurs in the uncut plant for
maintenance and development. This process continues in the
cut plant until it has dried to about 40% moisture content
causing further dry matter losses as the plant continues to
oxidize organic compounds (Wolf and Carson, 1973).

The total losses during hay making have been estimated
as high as 30%. At this rate 4.3 million tonnes of alfalfa
were cut in 1981 to yield 3 million tonnes produced. This
substantial loss (1.6 million dollars) would be decreased by
reducing haying losses. Many methods to reduce these losses
are being studied. Early investigation to speed drying and
thereby reduce the chance of loss due to rain damage
involved mechanical conditioning such as crushing, crimping,
or laceration. These methods increased dry matter losses
because increased mechanical manipulation caused greater
leaf loss (Jones and Palmer, 1932; Kepner et al., 1960).
Some chemicals, such as fusicosin, organic phosphates and
potassium carbonate showed increased drying rate of forages
and grasses in the laboratory (Turner, 1969; Harris, 1978;

Tullberg and Angus, 1972; Wieghart et al., 1980).

Investigation is currently being performed to determine the
adapability of carbonate solutions to field use (Rotz et

al., 1984).

CHAPTER 2

OBJECTIVES

The overall objective of this research was to quantify
the affects of temperature and vapor pressure on the drying
of alfalfa, with the intention of obtaining an equation
based on envionmental factors to determine the necessary
drying time for baling. The specific goals were:

1) To develop equipment for monitoring alfalfa drying rates
in a drying chamber which allowed control of air
temperature and vapor pressure.

2) To develop a procedure for data collection and analysis
in order to compare drying rates under various

treatments .

3) To quantify the affect of potassium carbonate solution on
the drying rate of alfalfa under controlled conditions.

CHAPTER 3

LITERATURE REVIEW

Plant Structure

 

The alfalfa plant is a perennial legume capable of
fixing nitrogen (with rizobia bacteria). It is a dicot with
a compound leaf consisting of three leaflets attached to the
petiole that is attached to a true stem. The plant has a
tap root which makes it more drouth resistant than grass.
The tap root, however, increases the plant's subjectivity to
winter heaving and therefore less winter hardy than grasses.
Regrowth is by crown buds which allow alfalfa to be cut up
to 10 times each year, with 3-4 cuts generally used.. As the
crop matures the stem tissue strength is increased and the
leaf to stem proportion decreases (Harris and Tullberg,
1980).

The alfalfa stem is mainly a support and a conduction
organ. Xylem transports water and dissolved materials up to

6

the leaves and phloem carries dissolved food material
downward. The xylem and phloem are separated by the
cambium. The plant stem is coverd by an epidermis, as is
the leaf. The cuticle (or integument) is a multilayered
membrane which covers the epidermal cells. Cutin is a
waterproof waxy layer which covers the entire plant above
ground. It is responsible for the tough, durable nature of
the cuticle. The cutin density is related to many
environmental conditions during plant growth, including
temperature, radiant energy, humidity, and soil moisture
availibility (Hadley, 1980). Hamilton (1975) adds air
movement, stress conditions, elevation, soil nutrients and
weathering to the list of factors which affect the wax
density on a growing leaf.

The cuticle makes the leaf nearly impermeable to water
and carbon dioxide except by way of openings in the leaf
called stomata which are surrounded and controlled by turgor
of the guard cells. Turgor is controlled by water take-up
or water loss by the guard cells (Zelitch, 1967). The plant
Cuticle protects cellular tissues, absorbs ultraviolet
light, conserves water and nutrients, participates in
respiration, protects against infection, parisitic and
herbicide damage and influences plant appearance (Hamilton,
1975).

Water vapor must leave the plant through the stomata,

through breaks in the surface, or by diffusion through the
cuticle, (Mears and Roberts, 1970). Moisture moves as a
vapor from regions of higher partial vapor pressure to
regions of lower partial vapor pressure (Barre, 1938). The
rate of diffusion is, therefore, proportional to the vapor
pressure gradient and inversely proportional to the
resistances of moisture movement in the plant material.
Moisture diffusion through the cuticle is much slower than
water loss through the stomata. Therefore, when the live

plant is under water stress the stomata close.

Stomata and guard Cells

As was previously mentioned the stomata control uptake
of CO2 as well as water .loss. The extent of stomatal
opening is due to many different metabolic activities in the
guard cells (Walker and Zelitch, 1963). Whitney et al.
(1969) showed that some stomata do not change apperature
with changing stimuli and seem "locked" in place.

In the growing plant light is the major factor causing
guard cell turgor to increase which opens the stoma.
Zelitch (1961) found that 4300 dekalux of light for a 90
minute exposure fully opened the stomata of tobacco leaf
discs floating on water. Covering the leaf disc closed the
stomata in 30 minutes. Zelitch floated the discs on water

as he found an abundent supply of water essential to obtain

’7‘

fully open stomata. He further stated that the substance
controlling the guard cells was generated in or near these
cells since covering half a leaf disc yielded no open
stomata in the covered portion and 70% open stomata from the
half in the light, within 90 minutes. Hense the controlling
mechanism was affected by the light/dark conditions at each
stomata and not for the leaf as a whole. Turner (1969)
suggested potassium ions moving into guard cells could
account for the increase in turger which opens the stomata.
This would also indicate that stoma work independently of
each other. Under field conditions stomata have been shown
to close rapidly for the first two hours after a plant is
cut to conserve water (Jones and Palmér, 1932; Pederson and
Buchele, 1960).

Oxygen is necessary for normal stomatal opening (Walker
and Zelitch, 1963). Temperature and water availability also
affect stomatal opening (Whitney, et al., 1969). Carbon
dioxide causes closure of stomata in light (Staflet, 1957).
As a plant wilts guard cells loose turgor which cause the
stomatal closure in an effort to save internal water.
(Zelitch, 1967; Kramer, 1963). The internal water balance
and degree of water stress depends on the relative rates of
water absorption and water loss (Kramer, 1963).

Different methods have been used to indicate the

relationship between the stomatal opening and water loss.

10

Staflelt (1957) suggests that guard cell width is a better
indicator of water use than stomatal width. Meyer and
Anderson (1939) stated that the rate of water diffusion is
proportional to the perimeter of the stomata and not the
area of the opening. Others, however, continue to use width
of stomatal opening to indicate changing transpiration rates
(Zelitch, 1961 and 1967; Walker and Zeltich, 1962;
Schonherr, 1976; Whitney, et al., 1969). Whitney found that
for alfalfa dried at high temperatures the degree of
stomatal opening (partially vs. fully open) did not
significantly influence drying rates although there was a
significant difference in the drying rates of alfalfa leaves
when the stomata were open as compared to when they were

closed.

Cuticle

The cuticle plays the major role in water conservation
by epidermal cells (Schieferstein and Loomis, 1956). It is
the main factor affecting the deposition, distribution and
retention of chemicals applied to forage as aqueous
solutions or suspensions (Holloway, 1969). Other functions
have been discussed previously. Once the stomata close
cuticular resistance to water loss causes the reduced
transpiration rates (Schieferstein and Loomis, 1956). For

this reason much research has focused on reducing cuticular

11

resistance.

Schonherr and Bukovac (1973) found that water
permeability depends more on the state and distribution of
soluable cuticular lipids than on their chemical
composition. Hartel (1947 and 1951, translated from German
and discussed in the paper by Schonherr and Bukovac, (1973))
observed that the maximum cuticular transpiration occured
when pretreated with buffer ions at pH = 7. Schonherr
(1976) found that water permeability was dependent on pH and
cations of the buffer solution.

The efficiency of cuticular waxes in limiting cuticular
transpiration is a function of both the quantity and form of
wax on a leaf surface (Denna, 1970). The amount of wax is
higher in plants grown on soils low in phosphorus, potassium
or nitrogen. Waxes are also denser and more compact if the
plants are exposed to excessive winds. Waxes on the crown
of the cuticle are often removed by weathering (as when two
leaves rub together). Brushing leaves increases
transpiration in the cuticular phase after the stomata have
closed (Hall and Jones, 1961; Hamilton, 1975). Hamilton

found that exposure to hexane vapor increased transpiration.

Water Loss
The amount of cutinization is also increased by

continued plant water stress (Kramer, 1963). As mentioned,

12

water stress causes stomatal closure in an effort to retain
water. Water loss appears to be linearly related to vapor
pressure differences with the major limitation being
cuticular resistance (Leshem et al., 1972; Mears and
Roberts, 1970). Hence if the living plant is stressed, the
amount of wax on the leaves increased in order to decrease
water loss.

Movement of water in a living plant is due to changes
in the water potential. Water potential is the chemical
potential of water in the plant or conceptually an
expression of the Gibbs free energy of water in the plant
(Slayter and Taylor, 1960; Merva, 1975). Under normal
growing conditions, water is taken up by plant roots from a
region of higher potential to a region of lower potential.
It changes state from a liquid to a vapor both within the
atmosphere outside the plant as well as within the plant.
The rate of water uptake is limited by available water and
the amount of water being transpired.

Water vapor exits the plant primarily through stomata
and to a smaller degree through the cuticle. The cut plant
looses water fairly rapidly until the stomata close. Then
drying is slowed as water must be moved through the
resistive cuticle layer, or through breaks in the plant
(Mears and Roberts, 1970). After the initial drying period

is primarily from the stems through the leaves in the cut

13

plant (Harris and Tullberg, 1980).

251 Matter and Respiration Losses

After afalfa is cut it continues to respire until it
reaches 35-40% moisture content (Greenhill, 1959; Wolf and
Carson, 1973).

Most dry matter, nutrient and protein losses occur in
the field, and these losses increase if the crop is damaged
by rain (Shepherd et al., 1954). These researchers found a
38.5% loss of leaf dry matter occured for hay which was not
damaged by rain. This value was increased to 47.3% for hay
wetted by two showers and 74.5% if wetted by three showers.
Klinner (1975) reported up to 30% dry matter losses to be
expected during hay making.

At any temperature dry matter losses are inversely
related to the saturation deficit which is related to the
drying rate. In other words, as the drying rate increases
dry matter losses decrease (Greenhill, 1959). Greenhill
also showed that as temperature increases dry matter losses
increase. He sights a 7% dry matter loss due to continued
respiration. Greenhill suggests that continued
photosynthesis may offset respiration effects. Respiration
is a linear function when related to moisture content but it
increases exponentially with temperature to 25'C (Wood and

Parker, 1971). Respiration ceases at tissue temperatures

14

(greater than°55 C (for 15 minutes) or at a moisture content
less than 40% (Wolf and Carson, 1973). Microwave treatment
for 30 seconds can also stop respiration (Priepke and Bruhn,

1970).

Increasing Drying Rates

Various treatments have been used to speed the drying
rate of grasses and forages. Byers and Routley (1966)
showed that steaming increased water movement from alfalfa.
Whitney et al. (1969) showed that drying rates increased in
grass specimans when leaves were steamed, cut, split, or
exposed to petroleum vapor for 60 seconds. Exposure to
petroleum ether vapor was shown to be a more effective
treatment to increase the drying rate than dry heat, steam,
or steam and petroleum ether vapor. The treatment has a
similar effect to longitudinal splitting of the stem as
studied on ryegrass (Harris et al., 1974). The effect of
petroleum ether increased as the temperature of the ether
increased since the waxes on a leaf were more easily removed
with the hotter vapor. This study showed that holding
leaves over hot petroleum ether vapor for 10 seconds was as
effective as dipping for 60 seconds in a cold liquid
petroleum ether solution.

Harris and Thaine (1975) showed that mechanical and

thermal treatments increased the drying rate in leaves and

15

to a greater extent in stems. Application of a flame to
standing and cut plants also reduced drying time (Person and
Sorenson, 1970).

Mechanical Treatments To Speed Drying
Mechanical field treatments such as crushing, crimping

and maceration increase drying time but also normally
increase dry matter losses (Jones and Palmer, 1932; Kepner
et al., 1960; Shepperson, 1974). Mechanically treated
plants initially dry faster since more cells are exposed to
the air. Once the exposed cells are dry the drying the
rates are the same as for untreated plants (Byers and
Routley, 1966). Dry matter losses can be reduced by using
less severe mechanical treatments such as tedders or turners
(Shepperson, 1974). Chop length (25-100mm) had no
observable effect on drying rates (Menzies and O'Callaghan,
1971) so minimal chopping is preferable. A method of
turning after some drying has occured is essential since air
movement through the swath is restricted by a seal formed by
the top leaves as they wilt (Barrington et al., 1970). The
rate of carotene losses is increased by crushing but the
final carotene content is not affected since the plant dries
faster (Fairbanks and Thierstein, 1966). Other
investigation concerning mechanical treatment is not
discussed here as the intended research does not involve

this aspect of forage drying.

16

Chemical Treatments gg §pggg Drying

Chemical treatments have also been used to alter drying
rates. Herbicides caused no significant increase in drying
under ideal conditions but increased the drying rate in
unfavorable weather conditions when drying to 40% moisture
content. Drying to 25% MC was the same for treated and
control hay and treatment caused a loss in the green color
of the dried product (Kennedy et al., 1954). Shepherd
(1959) found that the herbicide ethylene dipyridelium
dibromide did not affect subsequent plant growth. Dilute
solutions of fusicosin increased cuticular as well as
stomatal transpiration (Turner, 1969). Dipping in sodium
azide increased the initial drying rate only (Tullberg and
Angus, 1972). Tri-n-butyl phosphate doubled the drying rate
of grass and reduced dry matter losses (Harris and
May-Brown, 1976). Briphos compounds (orgnaic phosphates
16M, COGD and COZOD) also increased the drying rate of grass
and reduced dry matter losses (Harris, 1978).

The utilization of potassium carbonate to speed drying
has been used in the grape industry in making raisins. In
fact, Columella (60 AD) suggested the use of ashes and oil
to increase the drying rate of grapes. More recently
research has substantiated the use of KZCO3 to speed the
drying of grapes in making raisins (Dudman and Grncarevic,

1962; Grncarevic et al., 1968). That this treatment does

17

not remove sufficient wax to speed drying (Dudman and
Grncarevic 1962) so investigations have centered on finding
other possible reasons for the increased drying rates.
Increased drying rates may be due to a change from a
hydrophobic to a hydrophylic character of the grape surface
whereby the transport of water may occur in a liquid phase
across the cuticle in emulsion filled air spaces rather than
the slow vapor diffusion process (Dudman and Grncarevic,
1962; Chambers and Possingham, 1963). The effect of the
potassium carbonate was reversed by washing the plants. In
fact the drying rates slow to that of untreated plants when
washed, indicating that the waxes are not permenantly
altered (Grncarevic et a1. 1968). These researchers also
found an artificial wax covered membrane system showed a
similar reaction to drying when dipped in an oil and
potassium carbonate solution.

Potassuim carbonate (KZCO3) has been shown to increase
the drying rate of alfalfa leaves and stems (Tullberg and
Angus, 1972; Wieghart et al., 1980), affecting the stems
more than the leaves (Tullberg and Angus, 1978; Johnson,
1983). This may be of benefit to reduce overdrying of
leaves, which is a major problem in the drying of alfalfa.
To reach an average moisture content of 20% for baling the
leaves are generally dryer than 20%. The leaves then become

brittle and are easily shattered and lost. This is a

18

paticular problem as the alfalfa leaves contain more protien

than the stems.

Radiation

Increasing thermal radiation or the exposure time to
this radiation increases moisture removal rates from plants
(Person and Sorenson, 1962). These authors also found that
at equal radiation intensities drying rates are dependent
upon the wave length of radiation. Incident radiation is
affected by clouds, rain and perhaps wind (VanElderen et
al., 1972). Solar radiation increases the drying rate
because leaf temperature is raised which affects the vapor
pressure deficit (Hill et al., 1977). Ajibola et a1.
(1980) have studied the solar spectral absorptivities for
chopped, macerated and dewatered alfalfa in the field.
However, there has not been extensive research of the effect
of light as it relates to drying and its interrelation with

various methods used to speed drying.

Drying Analysis
An exponentially decaying curve is generally used to
model the drying of many agricultural products. This curve
is generally of the form: MR = exp(-kt)
where MR = (M-Me)/(Mo-Me)
t - time (hour)

k - drying constant
M - the moisture content

19

Me - equilibrium moisture content
Mo - initial moisture content for the interval

In 1969 Whitney, et al. used an exponential curve of the
form:
(M-Me)/(Mo-Me) = B*exp(-kt)

where k s C*exp(ST)
C and S were determined from experimental data. Menzies and
O'Callaghan (1971) used a similar equation and further noted
that forages dried at temperatures under 80 C% dried in two
or three distinct stages, with differing coefficients for
each stage. These researchers found the drying constants
were related to the temperature but not to the humidity of
the air. The first cutoff point for the drying period was
defined by

MC = 0.83 + 0.58 Mo

where MC - moisutre content

Mo - initial moisture

This relationship fit the data with a correlation
coefficient of 0.81. The other cutoff moisture contents
were not given in this paper. The equation to determine the
drying constant was:
k = Cl*exp(C2*t)
t - temperature, degrees C
C1,C2 - 0.031 and 0.024 for the first interval
- 0.0078 and 0.037 for the second
interval

- 0.0027 and 0.046 for the third
interval

20

Kemp et a1. (1972) used an equation similar to that of
Whitney (with B = exp(C2) and k = -C1*LE):
MR = exp(-C1*LE*t)exp(C2)
where: LE - latent evaporation
Cl - 0.1
C2 - 0.6
C1,C2 are coefficients determined from test data. A
regression on data generated from this equation, and the
actual test data gave a correlation coefficient of -0.96 for
early bloom alfalfa and -0.98 for full bloom alfalfa.
Substitution of k = 0.1(LE) into this equation gave
correlation coefficients of 0.9 (early bloom) and 0.87 (full
bloom).
Hill (1976) also used a similar equation of the form:
MR = e(-kt)*exp(0.8)

where: k - 0.0079(VPD) + 0.098
VPD - vapor pressure deficit

Temperatures from 19-31 C, and relative humidities from
50-93% were observed during testing. Hill warns that major
errors occured when there were significant radiation
effects, presumably due to the crop surface temperature
being substantially higher than the ambient temperatures.
He also found the greatest accuracy when the vapor pressure
deficit was near 30 millibars below saturation.

Hayhoe and Jackson (1974) used a similar equation to
predict the final moisture content after drying for n days:

I: * -*
Mn Mo exp ( a sumPE)

21

where M - moisture at the end of the nth

M2 - initial moisture at cutting
a - weighting factor
sumPE - the summation of the potential energy

for each day of drying (mm)

day

.Harris and Thaine (1975, Harris and May-Brown, 1976) used
the technique of plotting the relative water content versus

time, where the relative water content was described as:
ch = (Wt-Wd)/(Ws-Wd)*100

To summerize, most previous modeling of the drying of
alfalfa, and other hays, is based upon an exponentially
decaying curve. There have been various environmental
conditions used as the independent variable in the models
including ambient temperature, latent evaporation, vapor

pressure deficit and the potential energy.

CHAPTER 4

EQUIPMENT

Chamber and Air Handling

In order to monitor the drying of alfalfa in a
controlled environment equipment was needed that would
maintain desired temperature and vapor pressure conditions.
The equipment used consisted of a drying chamber, sample
holders, an Aminco Aire unit, weight sensors, and‘ a
microcomputer to control the environment and collect data.
Figure 1 gives a plan view of the equipment. All control
systems, including the microcomputer and the rewiring of the
Aminco Aire Unit were built and maintained by Richard Byler
and are discussed in Chapter 4 of his Dissertation (Byler,
1983).

The drying chamber, shown in Figure 2, was constructed

of plywood. The floor of the chamber was 1.6 meters long

with a plenum at each end. Each plenum was 0.3 meters deep

22

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which gave a one meter length in the area containing the
samples. The cross section of both the sampling area and
the plenums was 30 x 50 cm2. Each plenum stood on a base,
also made of plywood, at a height convient for changing and
viewing the samples. Since the sample chamber was not
supported by the base it was reinforced with angle iron.
Between each plenum and the sample chamber a metal
honey-comb was placed to create a uniform airflow by
creating a pressure drop. An air baffle was placed in the
intake plenum to disperse the air coming into the plenum
from the Aminco unit. The holes in the baffle were located
between 30-75 degrees off center, on both sides. There were
no holes that would allow airflow directly onto the samples.

The air entered and exited the plenums through the
plenum floor and was circulated by a fixed speed
squirral-cage fan. Airflow was measured with a hot wire
anemometer at various points around the sample holders. The
velocity varied from 0.1 m/s to 0.7 m/s with an average
airflow of 130 m /h. Henderson and Pabis (1962) found that
airflow rate variations in this range had an "insignificant"
effect on drying. The reason to maintain a constant airflow
was two-fold; to decrease variability between trials, and to
circulate the air through the Aminco unit to maintain proper
drying conditions.

The sample chamber top was constructed with an aluminum

26

frame which fit over the plywood floor and plenums. The
frame was covered with propafilm-C to allow light on the
sample, viewing, and to reduce heat buildup. The inlet
plenum was insulated with rigid foam 0.25 m thick to reduce
heat loss. The sample chamber was also lined with rigid
foam insulation except for 0.4 m of the chamber to allow
viewing of the samples. The plywood was covered with
several coats of polyeurthene to reduce moisture adsorption
by the wood. To change samples the entire film covered
aluminum frame was slid up and off the drying chamber.

The alfalfa stems were held vertically in the chamber
on two sample holders. Each sample holder was made in three
parts, an aluminum rod, a hard plastic cross piece and 4
binder clips as shown in Figure 3. The binder clips held

the alfalfa stems and fit over the cross piece. Each clip

 

 

Figure 3. Detail View of the Alfalfa Holders and Transducer Mount
(by D. Sprott)

27

held 7-8 stems. The cross piece had a hole in the center
offset from the line of binder clips which fit on the
aluminum rod protuding through a hole in the chamber floor.
The rod was held upright by the transducer outside the
chamber.

Each transducer was wired to the microcomputer so the
sample weight could be recorded at any time. The sensors
were mounted on a cement block to allow shock absorption The
cement block rested on two pieces of iron which were in turn
mounted 14 cm high on a cross piece on the chamber base.
The block did not Sufficiently damp out vibrations and
additional filtering was necessary. A strain gage amplifier
was used to do this. To allow adjustable placement of the
sensors two tracks were built in the cement block. The
sensors were then mounted on a base which could he slid
along the track (Figure 3). This arrangment allowed easy
positioning of the sensors directly under the holes in the
chamber floor. Additionally this allowed a method of
changing the number of sample holders.

An Aminco Aire Conditing unit, model J4-5460, was used
to control the environment. The unit was designed to
condition up to 8.5 cubic meters of air per minute in
cabinets of less than 1.13 cubic meter volume. The sample
chamber had a volume of 0.24 cubic meters and the fan forced

about 2.17 cubic meters of air per minute through the

28

system. This unit drew in air (in this case from the exit
plenum of the drying chamber) and cooled it to the water
temperature by spraying the air with a fine mist of water.
The water temperature was maintained at the desired level
with either a refrigeration unit or an electric heating unit
as needed.

The air was then heated to the desired temperature in
the next portion of the Aminco by an electric heater. The
heating and refrigeration units were controlled by the
microcomputer rather than the original controller. The air
was then moved into the drying (sample) chamber by the fan.
Two ducts were used to connect the Aminco to the sample
chamber. They were each 1 m long with a 0.127 m diameter.
The tube leading from the air conditioning unit to the
sample chamber was insulated with 0.03 m of foil-faced
fiberglass insulation. The duct exiting the sample chamber
was not insulated since the Aminco cooled the air to the
water temperature so retaining heat would serve no purpose.

Once steady state conditions were reached in the
equipment they were easily held there as the load produced
by the drying alfalfa was not great. There was some heat
loss to the environment from the sample chamber, the amount
of which varied dependent upon ambient and drying
temperatures. This was only a minor problem, however, and

with contol feedback using the drying chamber temperature

29

the test conditions were well maintained. The greatest load
on the system was due to the systematic cooling, saturating

and reheating of the circulating air in the Aminco.

Microccomputer and Control

The microcomputer was used to maintain constant drying
conditions and to record the sample mass, and the system
temperatures. Extensive revisions were performed by Byler
(1983) to allow microcomputer control of the Aminco Aire
unit.

The relationship of the water temperature, the dry bulb
temperature and the resultant relative humdidty was given
graphically in the manufacturers catalog. This chart was
used to determine the necessary water bath temperature to
give the desired relative humidity at any dry bulb
temperature. A formula was derived from this data by Byler
(1983):

DB = 77.3673-16.9764ln(RH)-0.082136*WT*ln(RH)
+73.6944/(RH)+1.377*wt
where DB = the dry bulb temperature,‘Celsius
RH = the relative humidity, percent
WT = the water bath temperature,'Celsius
The mean square error for this equation was reported as less
than 0.2.

The original bymetalic thermoregulators of the Aminco

Aire unit were replaced with integrated circuit precision

30

temperature sensors (LM 135) for better control. These
sensors were wired to the microcomputer to be used for
control and data output. For trials 1 to 16 a temperature
sensor placed near the exit of the Aminco unit controlled
the air temperature. There was some difficulty in
maintaining the desired chamber temperature and vapor
pressure with this equipment. After trial 16 a sensor near
the samples was used to help control the desired
temperature. This modification enhanced control since the
heat loss that occured before the drying air reached the
samples did not need to be estimated. Six analog
temperature sensors were used. The microcomputer converted
the analog signal to a digital signal. Two of these sensors
were placed in the Aminco Aire unit, one located in the
water bath (#2) and one near the electric heater (#4). The
other four sensors were in the drying chamber, #3 was near
the inlet and the other three (#‘s 5,6,7) were near the
drying samples, one between the samples and one behind each
sample. The numbers refer to the computer control number.
The microcomputer as has been mentioned, was used to
collect data and to control the sample environment. Figure
4 diagrams the information flow in the data acquisition and
process control equipment. A more detailed discussion of
the microcomputer may be found in the PhD Thesis by Byler

(1983). The data collection program recorded data on a

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cassette tape for storage as well as on a hardcopy output,
if desired.

The temperature information obtained was used to
control the air conditioning unit. National Semiconductor
precision temperature sensors LM335 (National Semiconductor,
1980) were used. The voltage output was linear and operated
in a range of -10 “C to + 100 °C with a nonlinearity over
that range of 0.3 “C (National Semiconducter, 1980, pp.
9-22). Actual nonlinearity in the ranges used was expected
to be 0.15 “C or better. All the thermocouples were
calibrated by placing them in the water of the Aminco,
varying the temperature with the microcomputer and recording
the thermometer reading and the readings from the
thermocouples as given to the microcomputer. A laboratory
mercury thermometer marked in 0.1 °C was used. Linear
regression curves were fit to this data. The thermocouples
used in the chamber to record the drying temperature were
also calibrated for a range of air temperatures from 10-50
‘C. The linearity of these calibrations was very good with a
correlation coefficient, r, greater than 0.9999 for all
sensors. Table 1 shows the temperature sensor coefficients
from the equation A*(digital number) + B. The conversion
factors for sensor 4 are not precise since this sensor was
used to control the air heater in the Aminco the temperature

was not recorded.

33

Table 1. Regression Coefficients for the Temperature Sensor
Calibration (from Byler, 1983)

 

* *
Sensor A B
Number
”
2 0.003932 -18.85
3 0.003994 -l9.00
4 0.322 -15.94
5 0.003921 -19.05
6 0.003921 -18.79
7 0.003986 -18.52

*In the equation: A*(computer number) + B

Table 2. Regression Coefficients to Obtain the Sample Mass
Used in Experiments 1-25 ‘

 

Trials Sensor A* 8* r
Number

0
1-9 ' 0 0.2215776 103.57 0.9963

1 0.2159596 129.23 0.9998
10-17 0 0.221799 190.251 0.9995

1 0.2136949 220.570 0.9998
18-25 0 0.0162765 61.409 0.9999

1 0.0157122 114.210 0.9999

 

¥ .

f

* In the equation: mass = A*(computer number) + B
r is the regression coefficient

34

There were two weight transducers used for the testing.
They were strain gage transducers, model number 4850 (two
pound capacity) built by GSE Incorporated. The rated
nonlinearity was 0.02% (0.2 grams) of full scale and 0.01%
(0.1 grams) typically. The sensors were numbered 0 and 1 in
the microcomputer. The two sensors were used in order that
two samples could be dried simultaneously. The operating
temperature range for these transducers was -17.78 to 93.33
%C with a temperature effect of 0.00044%/C on rated output.
The transducers were located outside the chamber to reduce
temperature and humidity effects. This was espically
important for tests at high humidities where the moisture
conditions may have affected the transducers.

The transducers were designed to be insensitive to
other than vertical mass loading. The rated error per inch
off-center loading at half capacity was 0.004% (0.04 gram)
of full scale. To calibrate these transducers known masses
(0 to 200 grams) were loaded on the sample holder and the
corresponding digital output noted (from the microcomputer).
A linear regression of the form A*(digital number) + B was
performed on the weight sensor calibrations. The
coefficients A and B are shown in Table 2. The correlation
coefficient, r, was greater than 0.996 in each case. The

coefficients changed after trials 9 and 17 because of system

36

changes. For trials 1 to 17 calibrations were performed
after each test and these coefficients were used in
subsequent analysis. The r value for any one of these
trials was greater than 0.9999. It was not necessary,
however, to enter different regression coefficients for each
trial. As Table 2 shows by grouping the trials in three
sets the resultant correlation coefficient was good enough
to allow average coefficients to be used for each set.

After trial 16 an excitation source for the weight
transducer was used so there was less noise in the data.
Also after trial 16 the weights were calibrated by taking a
final reading, with the samples in place, and a reading
without samples (0 9) since the linearity had been well
established. One further note- although the weight
transducers measured force and calculations were made using
units of mass all data was collected in the same place that
the calibrations were run so gravitational affects did not

influence data collection.
Software

Digital control software and data acquisition software
for trials 17-33 were written by Byler (1983) and the
programs may be found in the appendicies of that reference
with a discussion found in Chapter 4. This program output

an initial data set, a data set every 10 minutes for four

37

hours, and a data set every half hour thereafter. A data
set included the weights of the sample, the waterbath and
chamber temperatures and the date and time of the data set.
More frequent readings were taken at the beginning of a test
since alfalfa dries faster at a high moisture content. To
reduce system noise each weight reading was actually the
average value of six readings taken over a one minute
interval.

The program was stored on tape and had to be retrieved
from the tape after a system malfunction. Repeated reading
from the tape caused the tape to age and become distorted,
therefore, the program had to be reentered occasionally.
Because of these problems a data collection program was
stored in the microcomputer memory. It allowed the operator
to choose any time interval desired (minutes) for data
collection. Ten minute intervals were then used in trials
17-33. To calibrate the transducers a known weight was
placed on the sample holder and allowed to rest for 5
minutes after which a data set was recorded for that weight.
Calibrations after each test. included 0, 50 and 100 gram
weights. This method was used for trials 1-16.

After trial 16 a quicker method of calibration was used
as the transducer linearity had been well established. A
data set was recorded just before the sample was removed

from the chamber to be dried. The alfalfa was immediately

38

weighed and dried. The sample holders were replaced and
another data set taken now without any alfalfa. This method
gave a regression line (two points) and allowed minimum
disturbance of the transducers. The sample was weighed and
these two points were used to determine the regression line.
The original calibrations (to check linearity as well as
find the coefficients) used many weights, from 0 to 200

grams.

CHAPTER 5

DATA COLLECTION

Before the alfalfa samples were gathered for each test
the apparatus was checked to confirm that it was functioning'
at the desired temperature and vapor pressure.

Alfalfa was cut within 3 miles of the laboratory on the
Michigan State farms and transported to the laboratory in a
plastic bag to minimize drying during transport. A visual
estimate of the percent bloom was made when the sample was
cut. The alfalfa was placed in the chamber immediately so
no storage was necessary.

The top 22 cm of the plant was used in order to
decrease variability in the product. Fifty grams of alfalfa
were clipped to each holder. A 50 gram sample generally
required 20-30 stems. Once the samples were in the chamber
the data collection program was initiated.

The data collection program put the sample weight and

39

40

temperature data on tape and printed this information, if
desired. Appendix B gives the hourly moisture content data
sets for all trials. The program transfered the data to the
tape after 8 data sets had been taken and listed the track
and block where it was stored on the tape. When the trial
was completed the cassette tape was taken to another
microcomputer system to transfer the data to the university
mainframe computer (Cyber 750). This was done in two steps.
First the data was read from the tape and put on a floppy
disk (using the program TDCONTRL, Byler, 1983). For trials
1-16 the following procedure was then used. The data was
transfered to the mainframe using CDCFAST (Byler, 1983).
Once in the mainframe the moisture content for each data set
was calculated from the sample mass which was determined
from the regression equations obtained after each test.
Analysis was performed on the change in moisture content
over time.

After trial 16, system changes in both software and
hardware were made which necessitated minor data analysis
software changes. The new software calculated the moisture
content of the sample before the data was sent to the
mainframe (RPAK, Byler, 1983). Once these files were
transfered to the mainframe the programs were simplified
because the moisture content, on which all subsequent

analysis was based, no longer needed to be calculated.

41

The drying temperatures from the 4 chamber temperature
sensors were averaged for each test to check the equipment
control. In trials 17-33 control was very good, in the
eariler trials (before the system changes) the actual
temperatures were often very different from those desired.
The high temperatures were particularly difficult to
maintain when the control sensor was not near the samples.

In order to have an idea of the maturity of the sample
the leaf-stem ratio and internodal lengths were determined
from remaining alfalfa as soon as a test was initiated. For
the leaf-stem ratio, a 25 9 sample was used (the top 22 cm
of each stem only). The leaves were removed and weighed and
the remaining stems were weighed. The ratio was then
calculated. To measure internodal lengths, six 22 cm
alfalfa stems were used. To get a representative value per
trial the uppermost four internode lengths were averaged for
the six stems. The leaf-stem ratio, internodal length and
percent bloom and initial moisture contents for each trial
are listed in Table 3.

The trials generally lasted for 60 hours however this
time length was occasionally shortened due to equipment
malfunction. In order for the alfalfa attain a moisture
content below 30 percent in the drying chamber the alfalfa

was allowed to dry for 60 hours. Once the trial was

42

 

 

Table 3. Alfalfa Maturity and Time of Cutting for all
Trials
L/S INL BLM MC A,B
Trial cut/year ratio (cm) (%) (%db)
untreated '
1 3/82 1.4 3.5 pre 4,33 4,25
2 3/82 1.6 2.2 pre 2,35 2,77
3 3/82 1.4 3.0 10 3,33 3,50
4 3/82 1.5 2.0 10 3,37 3,42
5 3/82 2.1 3.3 10 2,53 2,51
5 3/82 2.3 2.1 10 2.84 3.07
7 3/82 1.9 2-2 50 2.94 2.98
3 4/83 1.4 3.3 pre 3,34 3,23
9 4/82 2.1 3.1 pre 3.57 3.69
10 4/82 1.7 2-9 pre 3.42 3.00
11 4/82 1.9 3.0 pre 3.23 3.22
12 4/82 2.0 2.1 pre 3.38 3.23
13 4/82 1.8 2.5 pre 3.12 3.01
14 4/82 1.7 1.4 pre 2.85 2.96
15 4/82 2.2 1.6 pre 2.55 2.69
16 4/82 1.8 2.2 pre 2.73 2.70
17 1/83 1.2 2.3 pre 4.76 5.10
18 1/83 1.0 2.6 pre 7.25 6.24
19 1/83 1.0 3.2 pre 5.82 6.18
20 1/83 0.9 3.8 pre 6.02 5.98
21 1/83 1.1 3.8 pre 6.02 5.98
22 1/83 1.4 4.1 pre 2.75 3.64
23 1/83 1.3 na pre 1.27 1.19
24 1/83 NA na 5 3.17 2.99
25 1/83 1.6 3.3 pre 3.08 3.32
treated
25 2/83 1.7 3.32 pre 3,55 3,72
27 2/83 0.9 na pre 4.08 4.34
28 3/83 NA na pre 2.50 3.19
29 3/83 NA na pre 4,29 4,33
30 3/83 NA na pre 4,43 3,13
31 3/83 NA na pre 3,50 3,10
32 3/83 NA na pre 3,07 3,04
33 3/83 NA na pre 3,03 3,42

 

INL - average of top 4 internodal lengths (6 stems/trial)
na - information not available

BLM - bloom

VP - vapor pressure

L/S ratio - leaf/stem ratio

MC - initial moisture content (dry basis)

43

completed the samples were removed from the chamber and
weighed. They were placed in paper bags and oven dried at
103 ”C for at least 24 hours. Weighing the oven dried
sample gave the final dry weights to be used in moisture
content calculations.

Trials 1-16 were performed in late summer and fall of
1982 while trials 17-33 were performed during the summer of
1983. In trials 26-33 both samples were sprayed with a
2.76% potassium carbonate solution. With this conditioning
treatment, two trials were performed at each of 4 drying
conditions. Samples in trials 1-25 were not treated, except
in three cases where sample B was sprayed with a solution to
speed drying. The potassium carbonate solution was used in
trials 2 and 16 while a commercially available drying

solution was used in trial 6.

CHAPTER 6

ANALYSIS

The calculated change in moisture content, with time,
was analyzed to determine variations in drying rate for
different environments. Three methods were used. The first
method calculated a drying constant which was used as a
measure of the drying rate. The second method of analysis
required the calculation of the mass transfer coefficient.
The third involved analysis of the coefficients of a best
fit exponential curve for each trial. With the analysis of
the drying constant and mass transfer coefficient each
drying curve was divided into three sections dependent upon
moisture content. A drying .constant and mass transfer
coefficient were determined for moistures greater than 2
(dry basis), between 2 and 1, and for a moisture content
less than 1. This was done to separate the drying process
into a fast drying period (before stomatal closure), a

44

45

medium drying period (closed stomata), and a slow drying
period (closed stomata, high resistance to water loss). It
was beyond the scope of this study to determine at what
moisture contents these periods might occur so the cutoff

points are somewhat arbitrary.

Drying Constant

The first procedure (drying constant analysis) has been
used and described by Priepke and Bruhn (1970). The drying
curve was assumed to fit an exponential model where the
drying constant was the slope of the natural log of the
moisture ratio as a function of time. The drying constant,
k, was determined as:

k = -(1/t)*ln((M-Me)/(Mo-Me))

length of time interval(h)
initial moisture content for
the interval (db)
M a final moisture content for
the interval (db)
Me a equilibrium moisture content (db)

where: t
Mo

This equation is often written in exponential form:
MR . e-kt
where: MR - the moisture ratio = (M-Me)/(Mo-Me)

A drying constant, k, was calculated for each data
collection interval and an average value determined for the
3 ranges previously mentioned. The drying constants for

each range were then compared using the SPSS statictical

package (Nie et al., 1975) with temperature and vapor

46

pressure as the independent variables.

In order to calculate the drying constant an
equilibrium moisture content (Me) had to be assumed. Values
for the Me of alfalfa dried under various environmental

conditions were obtained from Bakker-Arkema (1962).

Mass Transfer Coefficient
The second method of analysis was based on the
calculation of the mass transfer coefficient. This
coefficient was calculated using the equation (Holman,
1976):
m = HD(Cp- Ca)
7 where: m a diffuse mass flux
H a mass transfer coefficient
C = concentration of moisture in the
plant
C a concentration of moisture in the
a air
= initial moisture content for the

M1
time interval

M2 = final moisture content for the time
interval

t1 = initial time

t2 = final time

The laws of Dalton and Avogadro state that the molal
composition of a mixture is proportional to the distribution
of partial pressures. This gives the molal humidity,

generally labeled f, as the mass of water vapor in mols per

47

one mol of air:
f s Pv/Pa
Pv - vapor pressure
Pa a air pressure
a Pt - Pv (total pressure - vapor
pressure)
So the concentration of moisture in the air was determined
from the vapor pressure and the equation:
Ca: Pv/(101.3 - Pv)
where: Pv = vapor pressure
Table 4 shows the Ca values for each trial.

To determine the value of Cp the volume of alfalfa at
various moisture contents was needed. Samples of 50 grams
of alfalfa at moisture contents of 0.2 to 4.88 were immersed
in water to determine their volumes (Table 5). A linear
regression was used giving the equation:

Vol = 17.67*MC + 27.52
where: MC = moisture content
The r2 value was 0.83 for this regression. The
concentration of moisture in the plant could then be
determined using the equation:
Cp= MC*DM/Vol
where: MC = moisture content
DM - dry mass of the alfalfa
Vol = the volume of alfalfa at MC

The values of the drying constant, k, the mass transfer

coefficient, HD, and the correlation coefficient were

48

Table 4. Temperature Settings, Drying temperature, Vapor

Pressure and Concentration of Moisture in the Drying Air for
all Trials

 

 

 

 

T c wa DB

TRIAL (‘C) 9v a
(kPa) kPa water (‘C) ('C)

kPa air

1 26 1.08 0.011 5.0 35
2 26 1.08 0.011 5.0 35
3 30 1.91 0.019 15.0 35
4 25 1.87 0.019 15.0 27
5 26 1.88 0.019 15.0 31
6 29 1.92 0.019 15.0 40
7 23 1.26 0.013: 8.0 25
8 24 1.55 0.016 12.0 25
9 25 1.94 0.020 15.5 25
10 21 1.88 0.019 18.0 25
11 26 2.25 0.023 18.0 26
12 21 1.36 0.014 12.5 30
13 30 2.12 0.021 17.0 40
14 22 1.24 0.012 7.5 25
15 17 0.91 0.009 2.0 18
16 27 1.67 0.017 12.5 40
17 26 1.68 0.017 25.0 40
18 40 4.79 0.050 30.0 40
19 33 2.55 0.026 19.3 33
20 33 1.90 0.019 14.0 33
21 33 1.55 0.016 10.0 33
22 33 1.30 0.013 6.5 33
23 38 1.58 0.016 10.0 38
24 38 1.88 0.019 13.5 38
25 38 1.13 0.011 3.0 38
ggeatEd sampléfs 5.76 0.060 14.5 35
27 30 4.24 0.044 10.0 30
28 40 7.37 0.078 19.0 40
29 25 3.17 0.032 6.0 25
30 25 3.17 0.032 6.0 25
31 30 4.24 0.044 10.0 30
32 35 5.76 0.060 14.5 35
33 40 7.03 0.075 19.0 40

 

C0- concentration of moisture in the air
P

- vapor pressure,
WB - wet bulb tempera

DB - dry bulb temperature
t - temperature, dry bulb

kPa (kilo Pascals)
ture

49

Table 5. Volume of Water Displaced by Alfalfa at Different
Moisture Contents

 

 

 

Sample Moisture

Mass Content Volume
(9) (db) (ml)

# m
51.9 4.88 100
25.68 2.01 60
14.70 0.72 50
9.72 0.14 30
9.87 0.15 30
9.61 0.11 25
10.46 0.20 35

10.27 0.20 25

50

determined using the program CALCKHD (Appendix A). This
program used the moisture content and time for each data set
to calculate the drying rate (as described by k and HD) for
each time interval. The output for this program is seen in

Appendix C.

Exponential Curve Fitting

The third method of analysis was exponential curve
fitting. The statistical software package BMDP, program P3R
(nonlinear regression), (Jennrich, 1981) was used to fit an
.exponential curve to the data. This program, BMDP3R, is
given in Appendix A. A model utilizing two exponents was
used:

MC = P1*exp(P2*t) + P3*exp(P4*t) + P5

\

where: Px = coefficients (x=l,2,3,4)
P5 = equilibrium moisture content (from
Bakker-Arkema, 1962)
t = time

This equation was similar to the equation used to calculate

the drying constant where:

(-kt) (-kt)

140- M = (M0- Me)e 4’ Me = (P1 + P3)e + P5
This comparison assumes -k = P2 = P4 (the exponents).

Models utilizing 3 and 4 exponents were also used.
These models did not fit the data better than the 2

exponential model so no further analysis was done.

CHAPTER 7

RESULTS AND DISCUSSION

The drying rate of alfalfa was studied by analyzing the
change in mass, with time, of cut alfalfa plants for samples
dried under different temperatures and vapor pressures. All
mass loss was assumed to be due to loss of water from the
plant as the plant dried. As was mentioned in the
literature review, Greenhill (1959) reported a 7% dry matter
loss due to continued respiration during drying but
suggested that continued photosynthesis may offset this
loss. A graph of the change in moisture content, with time,
is given for each trial in Appendix D. These curves show
the exponential nature of the drying alfalfa plant.

Table 6 shows values for the drying constants, k, and
the mass transfer coefficients, HD, for trials with the same
vapor pressure conditions (different temperatures). The

values given are for transducer A from 3 trials at each of

51

Table 6. Drying Constants and Mass Transfer Coefficients
for Alfalfa Trials at Two Vapor Pressures and Different
Temperatures (Transducer A)

 

 

 

Vapor Pressure = 1.25 kPa Vapor Pressure 2 1.88 kPa
Trial 7 22 25 10 5 24
Temp 23 33 38 21 26 38
INL 2.2 4.1 3.3 2.9 3.3 na
L/S 1.9 1.4 1.6 1.7 2.1 na
Bloom 50 pre 10 pre 10 5
k1 0.089 0.137 0.201 0.041 0.053 0.190
k2 0.067 0.120 0.201 0.035 0.056 0.222
k3 0.033 0.049 0.044 na 0.041 0.072
HD1 0.515 1.020 1.203 0.339 0.271 1.241
HDZ 0.278 0.607 0.909 0.187 0.209 1.058
HD3 0.097 0.146 0.161 na 0.097 0.294

 

INL - internodal length (cm)
L/S - leaf/stem ratio
Bloom - percent
na - information not available k - drying constant
k1 - average k value for moisture contents greater than 2.0
k2 - average k value for 2.0 > moisture > 1.0
k3 - average k value for moisture < 1.0
mass transfer coefficient
value for moisture > 2.0
value for 2.0 > moisture > 1.0
value for moisutre < 1.0

HD -
HD1 - average H

HD2 - average HD
HD3 - average HD

53

two vapor pressures. Generally k and HD increase with
increasing temperatures, indicating faster drying at higher
temperatures. A linear regression, with temperature as the
independent variable and drying constant as the dependent
variable gave an R2 value greater than 0.82 for each of the
three moisture ranges. Regression with HD as the dependent

2 value less than 0.65 for all three

variable gave an R
moisture ranges. Table 6 also shows that there was a
decrease in the magnitude of k and HD at lower moisture
contents. This would be expected due to the greater
resistance of the plant to water loss at lower moisture
contents.

Table 7 shows the k and HD values for trials at
different vapor pressures and two temperatures. In this
table no pattern seems to exist between the drying constant
nor the mass transfer coefficient with increasing vapor
pressure. As before, however, there was a decrease in the
magnitude of k and HD with decreasing moisture contents.
These two results suggest that drying time was directly
related to the ambient temperature while not directly
affected by the vapor pressure in the range used in these
trials. It is possible that other factors, including
maturity, weathering and water stress during growing, may

have masked the effect of the vapor pressure on drying.

The change in moisture content, over time, was plotted

54

Table 7. Drying Constants and Mass Transfer Coefficients
for Alfalfa Trials at Two Temperatures and Different Vapor
Pressures (Transducer A)

 

 

Temperature . 26 C

 

 

 

 

Trial 1 l7 5 ll
VP 1.08 1.68 1.88 2.25
INL 3.5 2.3 3.3 3.0
L/S 1.4 1.2 2.1 1.9
Bloom pre pre 10 pre
k1 0.139 0.257 0.053 0.042
k2 0.052 0.164 0.056 0.050
k3 0.039 0.072 0.041 0.050
HD1 1.272 2.577 0.271 0.282
HD2 0.354 1.027 0.209 0.210
HD3 0.185 0.310 0.097 0.159
Temperature s 33 C

Trial 22 21 20 19
VP 1.30 1.55 1.90 2.55
INL 4.1 3.8 3.8 3.2
L/S 1.4 1.1 0.9 1.0
Bloom pre pre pre pre
k1 0.137 0.114 0.102 0.209
k2 0.120 0.078 0.051 0.026
k3 0.049 0.030 0.026 0.062
HDl 1.020 1.334 1.242 2.713
HDZ 0.607 0.544 0.358 0.961
HD3 0.146 0.155 0.137 0.372

 

INL - internodal length (cm)

L/S - leaf/stem ratio

Bloom - percent

k - drying constant

k1 - average k value for moisture contents greater than 2.0'
k2 - average k value for 2.0 > moisture > 1.0

k3 - average k value for moisture < 1.0

HD - mass transfer coefficient

HD1 - average H value for moisture > 2.0

HDZ - average HD value for 2.0 > moisture > 1.0
HD3 - average HD value for moisutre < 1.0

56

for the conditions of Table 6 in Figure 5. This figure
shows drying occurs more rapidly as temperatures increased
for a vapor pressure of 1.25 or 1.88 kPa. The change in
moisture over time at four vapor pressures for temperatures
of 26 and 33 °C are given in Figure 6 (refer to Table 7).
The drying rate due to changes in vapor pressure were not
consistant. At 26 “C the fastest drying was for 1.68 kPa.
The alfalfa dried with vapor pressures of 1.30 kPa and 2.55
kPa dried at a similar rate which was faster than the
alfalfa dried at 1.55 kPa or 1.90 kPa. The environment with
the greatest vapor pressure (2.25 kPa) had the slowest
drying at 26 “C but the fastest at 33 “C. This apparant

interaction deserves further study.

Crop Maturity

Information collected on the leaf-stem ratio and
percent bloom indicate that the drying rate may be affected
by the physical properties of the plant when it was cut.
The moisture content (of the mature alfalfa plant was a
product of many variables including the soil fertility and
acidity, available moisture during growing, wind and other
environmental conditions. In general at high percent bloom
the k values are lower than for trials using less mature
alfalfa. For example, trials 7 and 14 were performed under

similar conditions except that trial 14 was in pre-bloom and

fi

57

VHPOR PRESSURE : 1.25 kPa

 

4-00

- l

NTENT (DB)

  
 

 

 

 

 

  

 

 

 

 

  

   

C3 .
00
uf.’
(KN
:3
p.
.‘2
23

°0 .00

TIME IN HOURS

8
.45“)
g VAPOR PRESSURE = 1-88 1.1».
"' 0
~8 A 345:
z- 0
m"1 m 38 “C
p.
2:
C3
00
m9
(KN-1
:3
p.
8
252’

‘13-‘30 ~00 50-0 00 715.00

TIHE IN HOURS

Figure 5. Plot of the Change in Moisture Content of the Actual

Data Over Time for Different Temperatures at Two Vapor Pressures

   
       

 

 

    
 
  
 

 

 

g 58
a” TEHPERHTURE = 26%
C3
U - A 1.08 kPa
i-g 0 1-68 kPa
EV; m [-88 kPa
2E <Q> 22'1255 kPa
C3
(now
ufi-
age:
:3
p_.
2
8'4 - ~

r31.00 25.00 55.00 75.00

TIHE IN HOURS

8
505° TEHPERHTURE = 33°C
H A 1.30 kPa
P8 0 1055 kPa
E; m 1 ~90 kPa
E Q 2'55 kPa
CD
00
In?
tum“
:3
p.
8
as: "

c11.00 25.00 55.00 75.00

TIME IN HOURS

Figure 6. Plot of the Change in Moisture Content of the Actual
Data Over Time for Different Vapor Pressures at Two Temperatures

59

trial 7 was at 50% bloom. The K values for trial 7 are
lower than those for trial 14. This was not unexpected
since the older plant has a lower moisture content when cut
. and will not release water as easily as a plant at a higher
moisture. This effect was also observed in trials on
grasses, by Menzies and O'Callaghan (1971). Menzies and
O'Callaghan stated that generally the maturity of the grass
did not have much effect on the drying characteristics of
the grass. Wieghart (1979) observed, however, that older
alfalfa dried faster than the less mature alfalfa when cut.
This author suggested the faster drying was due to a greater
amount of weathering sustained by the older plant's cuticle,
and a subsequent decrease in cuticle effectiveness.

Higher leaf-stem ratios generally yield lower k values,
which would be expected as this is another test of the level
of maturity. The internodal lengths did not noticably
affect the drying constant. Table 6 shows that there may be
a correlation in these three parameters. As the percent
bloom increased the leaf/stem ratio also increased and the
internodal length decreased. Crop maturity was not a
variable of major concern in this study. However, due to
the possible affect of maturity on drying an attempt was

made to keep the variabilities discussed here to a minimum.

6O

Drying Constant

The mean drying constant, k, for 4 temperatures, 4
vapor pressures, and 3 moisture content levels are given in
Table 8. These values are separated into 2 groups, those
for transducer A and those values for samples dried on
transducer B. This was done to check that these two
transducers gave the same results.

There was a general pattern of increasing drying
constant magnitude (as shown by the means) with increasing
temperatures. As mentioned previously this relationship was
confirmed by a linear regression. The values of k at
temperatures from 31 to 35 ’8 are lower than the values in
the previous group (ZS-30°C). This was not expected and may
be due to trials being performed in different years. Trials
in the 31-35 °C temperature range were run early in 1983 (a
total of 4 trials) and the trials performed at 26-30 %C and
1.31-l.8 kPa were run in 1982 (a total of 8 trials). The
differences in the material, as well as the variability in
the '83 trials (less repetitions) may have Caused the lower
k values at higher temperatures.

The analysis of variance shows that the differences in
the transducers (or sides of the chamber) are negligable
(Table 8). This was particularly true for sample moisuures

greater than 1.0 (a=0.82). For sample moisture less than

61

Table 8. Mean Drying Constants for 3 Moisture Content
Intervals with Different Temperatures and Vapor Pressures,
and the Results of an analysis of variance on this data

 

 

 

 

 

Transducer A Transducer B

Temperature kl k2 k3 k1 k2 k3

17-25 C .083 .067 .042 .085 .071 .044
26-30 C .164 .112 .051 .148 .104 .059
31-35 C .140 .094 .042 .146 .085 .047
36-40 C .209 .192 .056 .243 .224 .065
Vapor Pressure

0.7-1.3 kPa .130 .103 .040 .132 .108 .046
l.31-1.8 kPa .162 .133 .053 .160 .123 .062
1.81-2.0 kPa .130 .080 .042 .093 .081 .046
2.01-4.5 kPa .207 .133 .062 .221 .154 .061
overall mean .141 .108 .048 .142 .111 .053

 

Signifiance level from the analysis of variance

 

k1 k2 k3

Temperature .021 .001 .031

Vapor Pressure .124 .106 .008

Transducer .954 .820 .140

Interactions ' kl k2 k3
temperature-vapor pressure .722 .270 .001
temperature-transducer ,92} 726. 720
vapor pressure-transducer .978 “911 '43
temperature-vapor pressure-transducer .992 '979 I823

R

k - drying constant

kl - average k value for moisture > 2.0

k2 - average k value for 2.0 > moisture > 1.0
R3 - average k value for moisture < 1.0

62

1.0 the possible difference in the transducers has a value
of a-0.l4 (86% confidence that the means of the two samples
are not equal). Of greater interest was the effect of
temperature, vapor pressure and their interactions. For
moisture contents greater than 2.0 the analysis shows a 98%
confidence that the means are not equal for the effect of
temperature and a 88% confidence that the vapor pressure
effect was significantly different. For moisture contents
from 1.0 to 2.0 the level of confidence increases to 99.9%
for temperature and 99.4% for vapor pressure. The
interaction was not significant. For moisture contents less
than 1.0 temperature and vapor pressure effects had a
significant interaction (99.9% confidence). This may be due
to the increased resistance to further water loss by the
plant at low moisture contents. When the plant is severely
stressed the internal and external environments,
particularly the difference in moisture, becomes a governing
factor in further water loss.

This analysis indicates that initial drying was less
dependent on environmental conditions which would be
expected when stomata are open. Then as stomata close and
plant resistance to water loss increases the environmental
conditions, singularly and combined, play a larger role in
drying.

Regression analysis was used to determine the closeness

63

of fit of the model based on k to the actual data. The
predicted moisture content was calculated using the average
drying constant for each trial. The predicted values were
compared to the actual values. The coefficients of
determination (r2) are given in Appendix C, with the k and
HD values. The fit of the theoretical curves were very
good. Of the 99 values (33 trials with 3 moisture intervals

per trial) there were 6 cases where r2 was less than 0.9

and 3 cases in which r2 was less than 0.8. For the
interval of moisture content greater than 2.0 the lowest
correlation value was r2 = 0.95. The interval with
moisture contents less than 1.0 gave the poorest fit
possibly due to the equipment noise having a significant

effect upon the data and causing inaccurate results.
Mass Transfer Coefficient

Table 9 gives the results of an analysis of variance on
the mass transfer coefficient with the mean HD values for
the 4 temperatures, 4 vapor pressures and 2 transducer
levels. The mean HD increases with increasing temperature
which was also shown in Table 7. As was the case with k an
increasing vapor pressure does not show an increase in the
mean mass transfer coefficient values. However HD does
increase with increasing vapor pressure, except in the

l.81-2.0 kPa range. Trials in all vapor pressure ranges

64

 

 

 

 

 

 

Table 9. Mean Mass Transfer Coefficient for Three Moisture
Content Intervals with Different Temperatures and Vapor
Pressures, and the Results of an analyais of variance on
this data
Transducer A Transducer B

Temperature HD1 HDZ HD3 HD1 HD2 HD3
17-25 C 0.524 0.304 0.125 0.533 0.314 0.117
26-30 C 1.121 0.528 0.188 1.176 0.526 0.195
31-35 C 1.577 0.617 0.203 1.628 0.563 0.224
36-40 C 1.854 1.092 1.007 2.402 1.437 0.769
Va or Pressure
0.7-1.3 kPa 0.836 0.467 0.134 0.875 0.499 0.140
0.31-1.8 kPa 1.177 0.659 0.191 1.263 0.628 0.236
1.81-2.0 kPa 0.732 0.388 0.162 0.702 0.389 0.175
2.01-4.5 kPa 2.308 0.924 1.045 2.738 1.203 04123
Overall mean 1.120 0.561 0.308 1.247 0.621 0.284
Signifiance level from the analysis of variance

HD1 HD2 HD3
Temperature .001 .001 .001
Vapor Pressure .001 .001 .001
Transducer .631 .555 .001
Interactions Hnl H02 HD3
Temperature-vapor pressure .001 .005 .001
Temperature-transducer .779 .292 .001
vapor pressure-transducer .821 .532 .001
temperature-vapor pressure-transducer .539 .290 .001

 

HD - mass transfer coefficient
- average value for moisture > 2
- average value for 2.0 > moisture > 1.0

1
'HD3 - average value for moisture < 1.0

65

were performed during both 1982 and 1983 so it was unclear
what might have caused this, other than natural sampling
variability.

Similar to the drying constant analysis the effect of
the trandsucer was not significant until the low moisture
content levels (MC<1.0) are reached. Unlike the analysis of
k, however, temperature and vapor pressure have a
significant (confidence 99.9%) affect on HD at all moisture
contents. The interaction of temperature and vapor pressure
was significant for all three moisture content groups also.
This result reflects the key point of the mass transfer
coefficient analysis. This analysis is based on the
moisture in the plant and in the air and consequently the
air vapor pressure interaction with drying temperature is
important.

In modeling the drying of alfalfa with the mass
transfer coefficient (non-exponential in nature) the drying
temperature and vapor pressure are important at all moisture
contents. This may be because this model does not assume
the natural exponential nature of drying alfalfa.

The correlation coefficients for the mass transfer
coefficinets are in Appendix C as previously mentioned. Of
the 99 regression coefficients there were 14 values with

2

values of r < 0.9 and 7 values less than 0.8. The fit was

better at the highest moisture content interval (greater

66

than 2.0) as with k. Again the poorest fit was seen at the
lowest moisture contents, probably due to system noise

causing inaccurate results.

Exponential Curve Fitting

The results of the exponential curve fitting were
inconclusive. Figure 7 shows the curves generated from
this analysis for trials 1 and 2. The curves fit the data
quite well but the problem of analysis lies in the lack of a
means of comparison of the equations. Table 10 lists the
calculated coefficients in order of increasing temperatures
and Table 11 lists these coefficients in order of increasing
vapor pressure. There are no definite trends among the
variables with the change in environmental conditions. In
fact trials with the same temperature and vapor pressure,
such as trials 1 and 2, had markedly different coefficients.
This method of analysis modeled alfalfa drying very well but
could not be used to compare trials. Because there was no
noticable patterns in the coefficients this analysis was not

performed on trials 10-33.

Chemical Treatment

The eight (8) trials performed with potassium carbonate

C
U.
3.75
I
p
Q
A
Y 2.‘
0
U
i
f
4 -¢
‘0‘-
Lo -

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V
Z
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Time (hours)
1 - transducer A

0’0000‘0000‘0000'00009000090000°0
. O
of 0
0' 0
0' 0
0' 0
0 0 o
0 ' 0
0 ' 0
O P 0
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Time Lhours)

Trial 2 — transducer A

0 - actual (observed) data point
P - predicted data point
* - observed and predicted data points coincide

Figure 7.
l and 2

67

0" AOL. -‘VI>0U ‘

0'0 avg-040mm 8

Trial

10"

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Time, (hours)
1 - transducer B

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IL 0:

Tims.(hours)

Trial 2 - transducernB

Example of the Two Exponential Curve Fit (BMDP) for Trials

68

Table 10. Coefficients of the Two Exponential Curve, Fit to
the Data, in Order of Increasing Temperature

Trial t VP P1 P2 P3 P4 P5

 

j

Transducer A

7 23 1.3 2.61 -0.07 0.06 -0.00 0.10
8 24 1.6 5.00 -0.04 -1. 82 -0.03 0.11
4 25 1.9 2.09 -0.10 0.89 -0.03 0.13
9 26 1.9 0.56 -l.83 2. 78 -0.04 0.12
2 26 1.0 2.42 -0.11 0. 20 -0.01 0.08
1 26 1.1 1.90 -0.26 1.87 -0.04 0.08
5 26 1.9 2.48 -0. 06 0.10 -0.00 0.12
6 29 1.9 0.48 -0. 84 2.11 -0.08 0.11
3 30 1.9 3.14 -0. 08 0.11 -0.00 0.09
ansducer

7 23 1.3 2.66 -0.07 0.12 -0.00 0.10
8 24 1.6 3.13 -0.05 0.04 -0.00 0. 09
4 25 1.9 0.44 -l.79 2.88 -0.06 0.13
9 25 1.9 0.58. -2.76 2. 95 -0.05 0.12
2 26 1.0 2.29 -0.82 0. 48 -0.06 0. 08
1 26 1.1 2.36 -0.17 1. 39 -0. 03 0. 08
5 26 1.9 2.45 -2.67 0.05 -0. 00 0.12
6 29 1.9 2.47 -0.29 0.42 -0. 06 0.09
3 30 1.9 3.18 -0.09 0.19 -0. 00 0.10

 

t - temperature, dry bulb
Vp - vapor pressure, kPa
Px - Coefficients in the equation:

Plexp(P2*time) + P3exp(P4*time) + P5

P5 - the equilibrium moisture content, fixed, from
Bakker-Arkema (1962)

Table 11.

69

Coefficients of the Two Exponential Curve, Fit to
the Data, in Order of Increasing Vapor Pressure

 

 

 

 

Trial VP t P1 P2 P3 P4 P5
Transducer A
2 1.0 26 2.42 -0.11 0.20 -0.01 0.08
1 1.1 26 1.90 -0.26 1.87 -0.04 0.08
7 1.3 23 2.61 -0.07 0.06 -0.00 0.10
8 1.6 24 5.00 -0.04 -1.82 -0.03 0.11
4 1.9 25 2.09 -0.10 0.89 -0.03 0.13
5 1.9 26 2.48 -0.06 0.10 -0.00 0.12
9 1.9 25 0.56 -1.83 2.79 -0.04 0.12
6 1.9 29 0.48 -0.84 2.11 -0.08 0.11
3 1.9 30 3.14 -0.08 0.11 -0.00 0.09
Transducer B
2 1.0 25 2.29 -0.82 0.48 -0.06 0.08
l 1.1 26 2.36 -0.17 1.39 -0.03 0.08
7 1.3 23 2.66 -0.07 0.12 -0.00 0.10
8 1.6 24 3.13 -0.05 0.04 -0.00 0.09
4 1.9 24 0.44 -1.79 2.88 -0.06 0.13
5 1.9 26 2.45 -0.06 0.05 -0.00 0.12
9 1.9. 25 0.58 -2.67 2.95 -0.05 0.12
6 1.9 29 2.47 -0.29 0.42 -0.05 0.09
3 1.9 30‘ 3.18 -0.09 0.19 -0.00 0.10

t - temperature, dry bulb

Vp - vapor pressure, kPa

Px - Coefficients in the equation:

Plexp(P2*time) + P3exp(P4*time) + P5
P5 - the equilibrium moisture content, fixed, from

Bakker-Arkema (1962)

70

solution sprayed on both alfalfa samples were compared using
the drying constant and mass transfer coefficient analysis
described previously. The mean values of k and HD, for the
three levels of moisture, are shown in Table 12. As with
the untreated samples the magnitude of both k and HD
decrease with decreasing moisture content of the plant or
with increasing drying temperatures. The magnitudes of the
average drying constant or mass transfer coefficient at each
level of moisture content are, in every case, greater for
the treated product than the untreated alfalfa, indicating
faster drying with the potassium carbonate solution. The
Chemically treated alfalfa was dried under conditions of
higher vapor pressures than the untreated alfalfa so a
direct comparison was not possible.

Linear regression was used to analyze each moisture
content interval for both the potassium carbonate treatment
and the control. Drying temperature was the independent
variable and the drying constant the dependent variable.
The linear regression fit the treated samples best with r2
greater than 0.84 for moisture contents above 1.0. The
coefficient of determination for the treated samples at
moisture contents below 1.0 and for all the control sample
moisture intervals was less than 0.41. In spite of this
poor fit the regression lines indicate the effect of the

potassium carbonate solution to speed drying. The slope of

71

Table 12. Mean Drying Constants and 'Mass Transfer
Coefficients for Three Moisture Content Intervals at Four
Environmental Conditions with Treated Alfalfa

 

 

Condition A B C D mean
k1 0.116 0.147 0.236 0.422 0.230
k2 0.108 0.118 0.241 0.399 0.216
k3 0.112 0.072 0.158 0.154 0.108
mean 0.112 0.112 0.212 0.325
HDl 0.922 1.234 1.699 2.923 1.694
H02 0.580 0.676 1.252 1.923 . 1.108
HD3 0.485 0.315 0.466 0.344 0.402
mean 0.662 0.742 1.139 1.730
Conditions
A - temperature = 25 C, vapor pressure - 3.17 kPa
B - temperature = 30 C, vapor pressure - 4.24 kPa
C - temperature = 35 C, vapor pressure = 5.76 kPa
D - temperature - 40 C, vapor pressure = 7.03-7.37 kPa

 

k - drying constant

kl - average k value for moisture > 2.0

k2 - average k value for 2.0 > moisture > 1.0
k3 - average k value for moisture < 1.0

- mass transfer coefficient

H

H31 - average value for moisture > 2

HD2 - average value for 2.0 > moisture > 1.0
HDB - average value for moisture < 1.0

72

the line for untreated. samples with moisture contents
greater than 2.0 was 0.007. The slope of the line for the
treated samples, with moisture content greater than 2.0 was
0.020. This is a 186% increase in the slope of this
regression line. The second moisture content interval
(1.0-2.0) also had an increased drying constant as indicated
by the increased slope for the treated samples. The slope
of the line for the treated samples was 0.020 and the slope
of the line determined by the untreated samples was 0.006
(233% increase). The final moisture content interval (less
than 1.0) was also affected by the chemical treatment, the
slope of the regression line increasing from 0.0006 to 0.006
due to chemical treatment. A graph of these regression
lines is given in Figure 8. The effect of the chemical
treatment is greater at higher drying temperatures as
indicated by the increased slope of these regression lines.
At drying temperatures above 35 ’C the effect of potassium
carbonate is significant. At 25 ’C the treated samples are
observed to have lower drying constants than the untreated
samples. This is not actually the case, it is due to the
linear model. Exponential models were not used in this
analysis as they did not increase the Coefficient of
determination.

There were two cases in which the sample on thansducer

B was sprayed with a potassium carbonate solution (trials 2

73

 

 

 

 

In Control sample, I - 1
0 Control sample, I - 2
o ‘ Control sample, I - 3
f O Treated sample, I - 1
:5q .0 Treated sample, I - 2
f, Treated sample, I-l
O
T
P-c:q
I:
c J
p.
03 .
2c: ‘ 1.
o9: _
Uo" '4 .
(.3
z -- -
ha
m— /
O
CDCD‘A”
.7
+ _+_ + A
O
9
I
925. 00 30 .00 35 .00 4b .00 45 .00

TEMPERHTURE C

I - 1: moisture contents greater than 2.0 (dry basis)

I - 2: moisture contents greater than 1.0 and less than 2.0 (dry
basis)

I - 3 moisture contents less than 1.0 (dry basis)

Figure 8. Linear Regression Curves for Drying Constant Based on

the Drying Temperature for Treated and Control Samples at Three
Moisture Content Intervals

74

and 16, trial 6 used a similar, commercially available
solution). These treatments significantly increased both
the k and HD values as shown in Table 13. The. increased
drying rate was higher at the higher moisture contents. The
treated samples achieved lower moisture contents much faster
than the untreated and continued to dry somewhat faster.
This may been seen graphically in Appendix D.

The curve fitting package, BMDP-P3R, was also utilized,
but in a slightly different manner. Two trials, at each of
4 drying conditions, with two samples per trial were
performed giving a total of 4 drying curves for each drying
condition (temperature and vapor pressure set). The P3R
package was used on these four curves to give a 2
exponential equation of best fit.

The BMDP 2 exponential equations were as shown:

At 25 C: 3.69exp(-0.12t) + 0.18exp(-4.06t) + 0.18
At 30 C: 2.40exp(-0.10t) + 0.98exp(-0.36t) + 0.30
At 35 C: 0.23exp(-0.00t) + 3.19exp(-0.22t) + 0.06
At 40 C: 2.75exp(-0.44t) + 0.25exp(-0.01t) - 0.07

A plot of these four equations is given in Figure 9 and
'indicates that as the temperature increases the initial
slope of the drying curves are steeper. The final moisture
content was lower at the higher temperatures, except for the
sample dried at 25°C. Note that these trials were performed
with a relative humidity of 35% rather than a constant vapor

pressure. The drying rate increased as the ambient

75

Table 13. Comparison of the Drying Constant and the Mass
Transfer Coefficient Values for Treated Alfalfa Dried
Simultanously with Control Alfalfa

 

 

Trial 2 Trial 16
A g A B
k1 0.131 0.577 0.213 0.399
k2 0.093 0.625 0.180 0.530
k3 0.025 0.042 0.073 0.070
HD1 0.705 3.091 1.095 2.059
HDZ 0.383 2.560 0.727 2.101
H03 0.114 0.133 0.209 0.191

 

k - drying constant

K1 - average k value for moisture contents greater than 2.0
K2 - average k value for 2.0 > moisture > 1.0

K3 - average k value for moisture < 1.0

H - mass transfer coefficient

H 1 - average HD value for moisture > 2.0

HD2 - average HD value for 2.0 > moisture > 1.0

H 3 - average HD value for moisutre < 1.0

76

 

_ 4.00

 
  
  

CD¢31CDCII
(JICOCWH73C7

go O'Ik(3|§

0- JBCUH3350

3.00

2.00

MOISTURE CONTENT (081

1,00

..
—~—

.00 15.00 20.00 35.00 40.00 50.00
TIME IN HOURS

Figure 9. Plot of the Two Exponential Equations of Best Fit Generated
from Potassium Carbonate Treatment Data

 

 

cp.00

temperature increased.

77

CHAPTER 8

SUMMARY

Three methods were used to analyze and compare the
drying of alfalfa under controlled conditions.

A drying chamber was built and' used with equipment
developed by Byler (1983) to monitor the drying of alfalfa
under controlled conditions. The drying chamber held two
samples which could be simultaniously monitored by a
microprocessor which was connected to the weight transducers
which held the samples.

A data collection procedure was established and analyis
techniques were tried. Data was transfered to the mainframe
computer where average drying constant and mass transfer
coefficients were determined for each trial. Curve fitting

was also used on some trials.

78

79

Both methods of analysis used, determination of a
drying constant, k, and a mass transfer coefficient, HD, fit
the data well, particularly at moisture contents greater
than 1.0 (dry basis). The drying constant increased with an
increase in ambient temperature. The drying temperature was
the driving force in the drying constant analysis, and both
the drying temperature and the vapor pressure were important
factors in the mass transter coefficient analysis.

Potassium carbonate increased the drying rate of cut
alfalfa. This effect was greater at ambient temperatures
above 35 ’8 where this solution would be particularly useful
to speed drying. The increased drying rate was observed at
all temperatures but might not be economically feasible for

ambient temperatures below 30'C.

B I BLI OGRAPHY

BIBLIOGRAPHY

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Bakker-Arkema F. W., Hall C. W. and Benne E. J.:
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Exp Sta Quat Bul 44:492-496, 1962.

Barre H.J.: Vapor Pressures in studying moisture transfer
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19(6):247-249,1938.

Barrington G. P. and Bruhn H. D.: Effect of mechanical
forage-harvesting devices on field curing rates
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Bolton J. L.: Alfalfa; Botony, Cultivation and Utilization,
London, England, Leonard Hill TBooksT Limited,
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Byers G.L. and Routly D.G.: Alfalfa drying - overcoming
natural barriers. Agricultural Engineering,
Sept., 1966.

Byler R.K.: Parameter estimation methodology in selected
moisture desorption models. (Unpublished
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Chambers T.C. and Possingham J.V.: Studies of the fine
structure of the way layer of sultana grapes.
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M.CC.XLV (1745). pp. 520-521 (Chapter XVI - of
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60 A.D.

80

81

Dudman W.F. and Grncarevic M.: Determination of the surface
waxy substances of grapes. J Sci Food Agric
13:221-224, 1962

vanElderen E., vanHoven S.P.J.H. and Kroeze G.H.: Short
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Fairbanks 6.8. and Thierstein C.E.: Performance of hay
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Greenhill W.L.: The respiration drift of harvested pasture
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Grncarevic M., Radler F. and Possingham J.V.: The dipping
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demonstrated with an artificial cuticle. Am J
Enol 19:27-29, 1968.

Hadley N.F.: Surface waxes and integumentary permeability.
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Hall D.M. and Jones R.L.: Physiological significance of
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Hamilton J.W.: Transpiration control by native clover
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Hammer P.C. and Day C.L.: Thin layer hay drying.
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Harris C.E.: The effect of organic phosphates on the drying
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Harris C.E. and May-Brown R.: The effect of tri-n-butly
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Harris C.E. and Thaine R.: The effect of thermal and
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82

Harris C.E., Thaine R. and Marjatta Sarisaco H.I.:
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Harris C.E. and Tullberg J.N.: Pathways of water loss from
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Hayhoe H. N. and Jackson L. P.: Weather effects on Hay
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Hearle W.L. and Paterson H.: Thhe design and operation of
systems with special reference to drying in the
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Hill J.D., Ross I.J. and Barfield B.J.: Use of vapor
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alfalfa hay. Transactions of the ASAE
20(2):372-374, 1977.

Holloway P.J.: Chemistry 'of leaf waxes in relation to
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J Sci Food Agric 20:124-128, 1969.

Jennrich R.: Nonlinear REgression, P3R, Abstract. BMDP
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Jones T.N. and Palmer L.O.: Field curing of hay as
influenced by plant physiological reactions.
Agricultural Engineering 199-200, 1932.

Kaldy M.S., Hanna M.R. and Smoliak 8.: Influence of drying
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Kemp J. G., Misener G.C. and Roach W.S.: Development of
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Kennedy W.K., Hesse W.H. and Johnson C.M.: The effect of
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83

Kepner R.A., Goss J.R. and Meyer J.H.: Evaluation of hay
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84

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85

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86

Zelitch 1: Control of leaf stomata - their role in
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APPENDI X A

Table A.1

86a

CALCKHD, Program to Calculate the Drying Constant and Mass

Transfer Coefficient

_A

 

 

100:
110:
120:
130=C
143:C
150=C
160=C
170:
130:
190:

200210

210:
220:1
230:
240:2
250:
253=C
273:
280=C
293:3
300:
313=C
320=C
330:0
340=C
350:

362:13

37C:
350:
393=C
400=C
410:0
023=C
430=C
440=C
05C=C
460:
47;:
052:
090:
5C0:
518:
520:
530:

593:110

55::
56C:

573:120

58::
590:
620=C
613=C
620=C
530:
542:
650=C
563:
$70=C
680=C
590:
700:
710=C
723:

 

 

 

PROGRAM CALCRnccIxcuT.OUTpUT.TApE1.TARET.TAFEI=:~RUT.T4=EE.TA=59>

connou T4420)

DIRENSIOR AHC4400108MC4400)

THIS PROGRAM CALCULATEs THE DRYING CONSTART. K. I THE "‘ss TRANSFER

COEFFICIENT. H00 400 THEIR RESPECTIVE R2 VALUES FOR THREE OROuPsts
or ROISTURE CONTENTS: START To 2 RC. 2 T0 1 RC AND 1 T0 :00 or TRIAL

REUINO I

00 10 I=I.4OO
ANCIIO=BHCCI):T(1)=J.0
CONTINUE

PRINT 1

FORRAT (zen UHAT
{:10 13.2) NAME
=0RRAT 416.110)
PRINT 3

IS THE DATA FILE NAME?)

INPUT CONSTANTS

FORMAT (IZQH ENTER ClofnolDHOEDFgNUH77
READ 'QCAoEHOAOHqBDflgNUM

TAPE 1 - DATA TAPE. HITH TIVE IND HOTSTURE CONTENTS FDR THO SAMPLES
AT EACH DATA COLLECTION POINT.

READ (1013) ((T(J)9A”C(J)03HC(J)0J=10NUF))
FORHAT fCF120701X0F50301195503))

PRINT 00 T41)0T(2)9T4NUH)

DO 130 IPASS=102

RUN THROUGH ANALYSIS FOR SIDE A0 THEN SIDE E
IT - CCUNTER F03 NUHBER OF VALUES SUHHED
”H - COUNTER FOR PC RANGE (5-202-101-81
SUHK - VALUES TO BE AVERAGED TO DETERMINE K
SUHHD = VALUES TC 8E AVERAGED TO DETERMINE HD

Q
o

IT
an 1

SUHK = SUHHD = 303

30 2C8 V = ZONU"

IF (IP4550E002) GOTD 113
ZHC : AHCCH)

THC : APCCH-l)

SOTO 120

CONTINUE

ZHC = BHCIH)

Yflc : BMCIH-1)

CONTINUE

IT = IT 0 1

V0 = I

CALCULATE THE SUM OF THE DRYING CONSTANTS FOD 4N INTERVtL

SUPK O (C'ALOO((ZHC'EH7/(YHC-EH))7/(T(“)-T(H-1)))
(THC-ZHC)I(T(H)-T(”-I))

SUHK :
AHR :

TC C‘LCULATE THE “ASS TRANSFER COEFFICIENT FIRST CALCULATE THE
MOISTURE RATIO. AHRO AND THE CONCENTRATION OF HOISTUPE 11 THE PLANT

IZHC'ADH)/t(17.67-ZHC)027052)
SUHHD 0 (AHP/(CP-CA))

3P =
SUHPD =
IF CUTSFF POINTS HAVE BEEN REACHED

CHECK TO SEE

87

Table A.1 (continued)

A

730=C
740:
750:
166:
770:300
780=C
790=c
800=C
813:
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8 0:
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850=C
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870:
880:
890:
900:
910:310
920:320
93::
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950:
960=
970:
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1000:000
1c10=c
1:20:C
1030:C
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1:5C=
1:60:
1:70:
1580:
1990:010
1100:
1110:
1120:
1130:
1140:200
1150:C
1160::
1170:
1180:
1190:
1200:
1210:
1226=C
1230:C
1240:C
1250:
126C=610
1270:
1293:
1290:C
1300=C
1310=C
1320:C
1:30:20

IF ((ZHCALT020)AAVDACHH0E901D) SOTO 300

IF ((ZHC0LT01010ANDA(HH0E002II SOTO 400

SOTO 200 I
CONTINUE

CALCULATE AVERAGE VALUES (KOHD) FOR MC > 20:

AKI = SUHK/IT
AHDI 3 SUHHC/IT

suaROUTIHE RSORO CALCULATES THE FIT OF THE THEORETICAL CURVE FRoH
TH! AVERAGE R AND «O VALUES

IF IIPASSoEOaI) CALL RSORDCAHCoAkloAHDIVRZAIRZEONOVHM03OUOCA0E“)
IF (I'ASSAE002) CALL RSORD(BHCOAKI0AHDIORZAORZEONOOHH09DH0CA0E”)
IF (IPASSoEOol) URITE (70310) HAPE
IF CIPASS05002) IRITE (70329!
FORMATCIH 0A801019H VALUES FOR SIDE A:)
FORMATTIH .19H VALUES FOR SIDE 8:)
HRITE (70330) AKIORZAoAHOIORZBOIT
FORMATCIH 0CX02TH FOR "C05T02: K: 0‘50399‘05"923 0
9F6030/031X06H H0: 0F50303X05H82: 0F60303IO9HNUH pTS: 0I3)
SUNK 3 SUHHD = 00:
IT 3 0
MM : 2
SOTO 233
CONTINUE

CALCULATE AVERAGE VALUES (KoHD) FOR 2 ) "C > I

AKZ : SUHK/ZT

ANOZ 3 SLFHO/IT

IF (IpASS0E001’ CALL RSORDCAHCOAKZOAHDZOPZAORZE0N09”"9‘0"0C‘05“)
IF (IPASSoEOoZ) CALL RSORO(BHCOAKZOAH020R2A0RZEONORHUORDH0CA0E")
URITE (70919) AKZORZAOAHDZORZBOIT

FORHAT (IH OEN02TH FOR IoOoLToNCALT020: K: 0F50309105H°23 9
0F6030/032105F ND: 0750303105552: 9‘60303X09HNUH FTS: 013)
SUHK=SUMHD = 300

HM : 3

1T : S

CONTINUE

CALCULATE AVERAGE VALUES (KOHD) FOR MC < loC
AK} : SUHK/IT
AND! 3 SUHHD/IT
IF (IPASSAEOAI) CALL RSGRDCAPCOAKSOAHDIORZAOPZCONOOPHOADFOC405“)
IF (IPA550E002) CALL RSGRD¢8VC0AK3OAHDSORZAORCSONUAHHABDPOCAOEP)
“H : 5

UNITE CUTPUT ON TA:E 7

URITE (70615) AK30°ZAAAPD309239IT

FORMAT(IH 0EX925“ EUR :000LT0UC0LT0I033 “3 O‘C0PO3X05H92: 0
¢F60301932105F "D: 0F50303X05592: oF6-303‘09HUU” cTS: 0130/)
IF (I‘ASSAEcol) IRITE (302C) AKIO‘KZ0‘53OAHDIVAHDZIAH93

URITE K AND HO VALUES FOR SIDE A ON TAPE 8
HRITE K AND HO VALUES FOR SIDE 8 ON TAPE 9

FORMAT (6(F60401K71

88

Table A.1 (continued)

13‘03 IF (1“SSOEGOZI 1917: A902?) AKIQAKZQAK3AAFDIQAHDZOAHDB
1350:100 COATINLE

1360:' END

1370: SUBROUTINE RSORD(AHCoAKoAHDoREGRKoREORHoNOo'HoADHoCAoE“)
1380: COHHON TAAOO)

1390: OIHEASION AHCTAOJ)oAHAAOO).AHHO(AGC)

1000:C

1~10=C THIS SUBROUTINE CALCULATES A REGRESSION AND RETUPNS F-R or
1420=C THE ACTUAL DATA COMPARED TO THE THEORETICAL DATA
1030=C

IOQO: IF (”MOEGO1, ISTART : 2

1.50: IF (HH.E0.S) ISTART : 2

1060: “AH(1) : AMHDIl) : ANCC1)

1Q10: ANCISTART-l) : ANHOAISTART-1) : AHCTTSTART-l)

1420: 00 100 J:ISTART9VC

1A90:C

1500=C CALCULATE TUE THEORETICAL CURVES. BASED ON AVERAGE K9 HO VALUES
1E10=C

1520: AHtJO = C(APCJ-11-EH)OEIP(-AKO(TCJ)-TIJ-1))))¢E”
1530: CP = (AHHD(J-1)OADH)/(CIT-GTOAFHD(J-I)) 0 27.52)
1540: AHHDIJ) : A“HDCJ'1) - «AND-(TAJT-TCJ-l)T-(c9-CA))
1550: 100 CONTIUUE

1560: L : 0

1570: SUHA = suHK = suHH : 3.9

1580: ASUHK = ASLHH : SUHAZ : SUMKZ : SUHHZ : 0.:

159;: no 110 N : ISTARTq~O

xeoo=c

1&10=C SUM THE VALUES ‘09 THE REGRESSION EQUATIONS

1620=C

1630: L = L 0 1

1640: SUNA : SUMA o AMEtN)

1650: SUMK : SLIK o AHTLJ

1660: SUFH : SUHH . AMHDAN)

1&70= ASUHK = ASUMK o (AHCCNIOAFCN))

1590: ASUHH = ASUHH o (AHCAN)~AHHD(H))

1&90: SU~A2 = SUHA2 o (=HC(~)-o2)

1750: SUHKZ = SU“K2 . (A‘CN)0'2)

1710: SUFHZ : SUHHZ o (:HHDLN)--2)

172C=110 CONTINUE

1730: R1K : ASUMK - CSJMAOSUFKIL)

1740: R1H : ASUHH - (SJHA-SUNH/L)

1750: RZK : (A$UHA2-ASUHAao2)IL)0(SUHK2-(SUHKo.2)/L))ooc.5
1760: RZH : c(SUHAz-CSUHA..2)/L)oASUMHZ-ASUHHc-2)IL))c¢f.5
1TTc=C

1780=C CALCULATE RcR

1790=C

1500: REORK : (R1KIR2K)--2

1610: REGRH : (R1H/R2H)--2

1E2C: ISTART : N0 e 1

1é3c= RETUR~

1540: END

 

#

89

Table A.2 BMDP3R, Program to Calculate a Two Exponential Best Fit
Curve using BMDP (BioMedical Data Package)

18.:/ RCcLVH TITLE 15 '2 ixpouimTch - Fl‘A'.
1‘2:/ TLFUT UNIT : 55 .

i‘2= VsfilAfiLES £9: 2.

1‘5: ‘CDVLT IS '(F1;.’olxo:5.')'.
2 ;- CALE : 32;.

:1‘=/ Vt=iaEL; fl:V?S LR: T:M:,M‘:STUD§,
221: L5: = TI"EV”DISTJ°EA

23' I ”EE‘iSS CS‘ENEEKT IS "OISTUF .

25 : ‘L-T’bE: : 10

23?: g-,-I‘u"~*~'f~.'TiF?S ADE ?.

2" INSESENUEWT IS TIVE.

2’ = CC“VF°G£NCE = T. :1.

2: = ITL:LTIS’S = S‘ .

2=‘: F:LVZRS - é .

3';- :zt‘lT : f." 0

31'=/ FLF1”ETEF IKZTIAL = 3.39 -‘.19 2. ., -'.-_, 2.
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APPENDI X C

100

 

A A

IRIAL 1

J‘LUES FOP SIDE ‘3
FOR MC.GT.2:

FCP 1.:OLTOHCOLT020:

rep

VALUES F09 SIDE 8:
‘OR "C.GT.2:

TRIAL C
VALUES Ff

TRIAL 7

VALUES 5C9 SICE A:
FCF MC.GT.2:

ViLUiS “SF

TQIAL “

VALUES F0:

VALUES “C9 SZCS 9:

GOJOLTOHCOLTOIOS:

(3 II

FOR 1.0.LTOFCOLT020:

3.3.L7.HC.LT.1.Z:

IXIXIX

D II V.) II

I.J.LT.“C.LT.2.

90:0LT0MC0L701..°

1

IX

FER 1.3.LT.MC.LT.2.:

:39 C.E.LT.MC.LT.1.C:

(I) II 0 II ‘3 II

XXIX

IOSOLTOqCOLTOZO:

:OLTOHCOLTOIOS:

IXIXIX
'JIIOIIIJII

10-0LT0MC0L70207

:IZOLTONCOLTOIO:

[XIXIX

"7 II 1') II ‘7 II

400

:CR 10:0LT0MC0LT020:

:OLTCUC 0LT

u u
p
O I
u H
\n b
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II
o
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.173

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= .399
D: 055:
= :55
D: 0279
= .247
C: 0197
..EQ
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.962
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.990
.941
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MUM PTS:

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NU" DTS:
NU“ PTS:
MUM PTS:
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NU" PTS:
NU“ PTQ:
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EU“ 075:
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NU“ PTS:
nu" DTS:
nu" pTS:
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nuu FTS:
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FOR 1.00LT.HC.LT.20:
FOR 9.0.LT.HC.LT.1.D:
TRI‘L E
V‘LUES FOR SIDE A:
FOR FC.GT.2:
FOR 1.3.LT.HC.LT.2.:
FUR C.G.LT.MC.LT.103:
VALUES FOR SIDE 5:
FOR PC.GT.2:
3R 1.3.LT.FC.LT.2.:
“OR 9.3.LT.“C.LT.1.::
TQIAL E
VALUES F09 SIDE ‘3
F39 "C.GT.2:
FDR

1.3.LT.NC.LT.2.:

FCR 3.3.LT.”C.LT.1.3:

VALUES cog SIDE 8:
F09 PC.GT.2:
FOR 1.3.LT.MC.LT.2.:

“CR 2.2.LT."C.LT.1.3

TRIAL 7
VALUES FOR SIDE A:
F0D HC.GT.22

F09 1.30LT0MC.LT.2.:

=09 3.?.LT.NC.LT.1.T

VALUES “DR SICE B:
F0° HC.GT.2:
FOR 1.30LT0MC.LT.2.:

90R C.J.LT.HC.LT. .:

TRIAL E

VALUES F09 SIDE A:

FOR MC.GT.2:

F0Q 1.30LT0MC.LT.2.:

IXIX
IDIIOII

IXIXIX

IXIXIX

(7 II I) II D II

IXIXIX

IXIX
'IXUIIIJII

U II II
II

UIIOIII'JII

OIIIJIIOII

,XTXTX

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C) II [J II
II II

D II
II

1.337

.393

.355

.276

R‘:

O---

.23
.343
.092

189

.356
.322
.151

.271
1.493

.224

.913
.941
.136

II
II

o
N
‘1
(D

.377
.951

.365
.274
.339

.393

.341
.27?
.347

R2:
R2:
R2:

92:

D2:
92:
P2:
R2:
P2:

92:

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10330

.999
.961
.939

.937
.989
.999
.991
.975

.953

.996
.994
.998
.998
.970

.935

.998
0994
.995

NUM

NU“

I‘JU‘I

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nun

r: u"

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wuv

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P75:

P75:

P75:

P75:

. P75:

P75:

PTS:

975:
P75:

P75:

pTS:
P75:

P75:

PTS:

27

26

IO
Id

111

1?

17

119

'0
'n

I‘d
3

45

102

HD- .‘.8 92: .996 NUu 975:
909 3.3.LT."C.LT.1.:: K: .936 92: .959
“93 .ICE 92: .968 NU" 975:
VALUES FOR SIDE 3:
F09 MC.GT.2: = .C53 92: 0999
HD: .‘QQ 9:: .995 NUH P75:
F09 1.3.LT."C.LT.2.: = .053 92: .998
H0: .225 92: .999 NUM PTS:
FOR 9.0.L7.HC.LT.1.C: 3 .3“5 92: .987
H3: .123 92: .97C NU“ F75:
TRIAL 9
VALUES FOR 9105 A:
F09 HC.GT.2: K: .C79 92: .979
"C: .567 92: .964 NU” PTS:
:09 1.3.L7oMC.LT.2.: K: .995 92: 0996
H0: .217 92: .995 ”U" P75:
F09 9.9.LT."C.LT.1.:: F: .337 92: 0968
“D: .133 92: .959 NU" pTS:
VALUES 90R SIDE 8:
F09 "5.97.2: K: .37: 9;: .979
“D: .574 92: .963 NU? PTS:
FCD 1.3.L7.MC.LT.2.: K: .995 92: .999
“D: .223 92: .995 NU“ 975:
(CR A.:.LT.MC.LT.1..: K: .393 93: .956
HE: .143 92: .974 Nd“ 97$:
T9IJL 1:
VALUES F99 SIC: A:
‘09 ”C.GT.2: “3 .9 l 9:: .99
H = .339 92: .977 NU” PTS:
:09 3.3.LT.MC.LT.1.:: K: .835 92: .999
“D: .197 9:: .91: NU” DTS:
VALUES F39 5135 b:
:09 ”C.GT.2: K: .504 9:: .999
H9: .33: 92: 099.9 N‘J'" :75:
“C? :.$.LT.MC.LT.1.:: K: .:35 :2: 996
“9: .156 92: .99: 5U“ 975:
TRIAL 11
VALUES F99 SIZE A:
909 HC.GT.2: K: .242 92: .993
”9: .292 92: .964 NUM FTS:
9C9 1.5.LT.”C.LT.2.: K: .39: 9:: .991
“3: .219 92: .999 NU” 975:
:09 9.3.LT."C.LT.1.:: K= .35; 9;: .979
“9: .159 9:: .95: NU” 975:
VALUES FUR SIDE b: -
F09 MC.ST.Z: K: .CSb 9:: .997
“9: .35 92: .971 NU” 975:
F09 I.J.LT."C.LT.2.: K: .351 9.: .953
H): .231 92: .993 NU” 975:
F09 3.3.LT.MC.LT.1.:: = .959 9;: .997
H0: .13: 92: .995 ”um 97$:

TRIAL 12
VALUES F09 SIDE A:

FOP MC.GT.2:

coR 1.3.LT.MC.LT.2.:

FOR 0.3.LT.NC.LT.1.3:

VALUES FOR SIDE 8:
F0q MC.GT.2:

FOR IOOOLTOMCOLTOZO:

COR 3.9.LT.MC.LT.1.3:

TRIAL 13
VALLES FCR 3105 :2
F09 MC.GT.2:

‘0: 1.9.LT.HC.LT.2.:

FOR :.3.LT.MC.LT.1.::

'-

VALUES can 51.
FO° vc

O I"

. O.
In

8
GT

‘OR 1.3.LT.”C.LT.2.:

”C” :.:.LT.”C.LT.1.r

CC: 1.:.LT.”C.LT.2.

r09 :.E.LT.MC.LT.1.::

.
.

r *0

t’T.&.

=C= I.J.LT.~C.LT.2.:

CC: :.3.LT.MC.LT.1.

TRI&L l?
VALUCS FPS SIDE A3
FOP MC.GT.2:

{OF 1.:.LT.MC.LT.2.:

top T.C.LT."C.LT.1.

VALUES :?R SIDE b:
F0; MC.?T.C:

UHUNC)H

IXIXIX

IXIXTX
DIIUIIDH
II II

1 I I X [ I
U H L) H 0 II
M II

T‘IXTX
II
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D ll ('3 ll (3

'1 II (I) H
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103

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.35

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.799
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.123

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I~J f” I“ V) I) l-_‘
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TRIAL

VALUES

VALUES rCR SIDE

TPILL

VALUiS

anu:s

TOILL l:
anuES

TFIAL

VALUiS FC9 SIDE
FCC MC.GT.Z:

F39 SIDE A:
FC° MC.GT.2:

oLToMCoLTolo

‘09 1.3.L7.“C.LT.2.

:IJOLTOVCOLTOIO

ON)
HP
r3 ‘,
~40.

FCp 1.C.LT.P‘C.LT.q

.OLTOMCOLTO.

:09 loSoLToMCoLTo

T.:.LT."C.LT.1.

F0: 1030LT.”C0LT020

2.3.LT.“C.LT.1.

COP lotoLTo”CoLT02.'

:OLTOMCOLTO

pop 1.8.LT.MC.LT.2.

IOGOLTOMCOLTozo:

COCOLTO”COLT01.T:

FOR I.JOLYIVC0LT020:

A
U

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[KIXIX
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x IA

'1") ll ('1 H "1 ||

111

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105

"C: 0961 92: 0989 NU“ FTS:
“OP :0C0LT0MC0LT010:: K: 0252 92: .992
“5: 3 2 92: 0959 NU“ P75:

VALUES FOR SIDE 8:

F09 MC05 02: V: .299 D2: 0979
“3: 3.774 92: .943 NU” 975:
C09 1030LT0“C0LT020: K: 0115 DE: 099‘
“3: .851 92: .986 NU“ $73:
:02 3.2.LT.NC.LT.1.Z: K: 0:59 :2: 0999
“D: .413 92: .998 MUM P73:
TRI‘L 29
VALUES ‘C9 SIDE A:
F09 MC.GT.2: K: .13; ‘2: .998
“C: 10292 9:: 0°99 NU” CTS:
:09 1.30LT0VC0LT020: K: 0351 9:: .973
”D: .359 CC: .957 NU“ PTS:
:00 :030LT0VC0LT01093 K: 0:26 02: 0959
“D: .137 9:: .949 NU” =75:
VALUES ‘99 $193 8:
CC: “C.GT.-: k: .135 =2: .995
“C: 1.239 ‘2: .959 NU" 975:
‘9‘ 1.5.LT.WC.LT.2.: *= .939 :2: .979
”3: .273 92: .956 NU" F753
:09 :.:0LT0MC0LT010:: '3 0:29 92: 0999
“3: 0153 :2: 099: NUu 9T5:
T°I¢L 21
V:LU;S F?“ 5:35 -:
“GP ”C.GT.Z: K: 119 9:: 099‘ ‘
“E: 103:9 :2: 0995 My“ 9T5:
=3: l.§.LT.MC.LT.2.: K: .17: :13 .953
PC: 54“ =2: .97: nu” 9T3:
529 :.;0LT0MC0LT010T: I: 0: . :2: 0992
“C: .1=5 :2: .957 NU" :75:
VALUES rm: SICE F:
:C: "C05702: K: 0119 C3: 0997
“3: 1.31: 9:: .997 ”U” =75:
‘0: 1.3.LT.“C.LT.2.3 K: .398 9:: .97:
"C: .479 92: .957 ”U" ‘79:
F3: :.:.LT.”C.LT.1.73 *= 0534 9;: .995
P9: .171 3:: .997 NU“ FTS:
TnléL 22
VALUTS FCF 9:5: £3
F09 VCOGT02: K: 0137 0:: 0995
' “D: 10:29 =1: 0995 NJ” :75:
“C9 10:0L70PC0LT020: K: 01;. 92: 0992
PD: .6;7 9:: .972 NU“ 975:
:0: :.:0LT0“C0LT0109: 9: 0:99 2:: 0991
*2: 0196 Ct: 0975 ”U" P75:
VALUES :99 $195 B:
‘C: ”C.ST.2: K: .15“ 9:: .997
‘5: 1.135 9;: .997 TU“ P75:
:99 10:0LT0VC0LT020: K: 0123 c:: 0996
“C: .539 :9: .9‘5'7 NU" 57::
=09 :0:0LT0MC0kT0107: "3 0:55 9;: 0955

1"

{L‘

.J
\I)

0

Oh

I

1‘. 3

:01

.4

[.0
~10
U-

T°IAL 25
VALUES rep SIDE A:
son MC.GT.2:

COG 30:0LT0HC0LT01033

VALUES FOR SIDE E:
‘09 MC.CT.2:

‘OR 5.5.LT0HC.LT01.3:
TQIAL 24
VALUES F0? SIDE A:

to: MC.GT.Z:

“CF 1.3.LT.”C.LT.2.:

F0p C.G.L70”C.LT010::
VALUES =CR SIDE 6:

FCC MC.GT.2:

:0a 1.5.LT.NC.LT.2.:

FC: T0C0LT.”C0LT010::
TF13L :5
VALUES FCP SIDE A:

F09 HCOGT.::

:05 1.50LTO“COLT.2.:

‘09 7.:.LTO”COLTO .3:
VALUZS rTR SIDE b:

7C; “C.GT.Z:

=09 1030LT0MC0LT020:

COP :0'30LT0VC.LT010::
TRIAL 2:

F09 1030LT0“C.LT.2.:

‘09 :0:0LT0"C.LT010-:

VALLES FOR SIDE b!
FOR NC.GT.::

106

TXII
O" k! II
II

I' K I I I K
CI II D II V) II
II II II

T 7 I. X 1‘ X
D II (1 II
II II

rJ II
II

X

1'

X I X
(3 II D II (3 II
II II

1

[1»[ xl’x
0 II (3 II 0 II
II

T. ’ T X I X
V) II D II '3 II
II II II

.19E
1.251
.225

1.146

.19;
10241
.22;
1.359
.372

.294

\D

..- n. In m I“ (l

(I I) 0‘

r...-.

. . 0'0. H
VJ 0-0 m P.)
p;

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1.213

.231
.929
0:43
.151

05.0

0 0 0 940 N

N'J\O\IIUIO'

91".. 1h 00 h)
.5

btbtu

.255
2.?79
0258
1.Q35
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92:

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0. 00 .. 00 00 .0

0000.10

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00 00 00 00 00 ..

F.‘I‘)III\) II”)

U nn

0 U .‘U 0 D
I‘II'UI’I‘I I-I I‘I
00 00 00 00 00 .0

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0000

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.997
.993
.999
.999
.599

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.=¢?

.995

NU“

NUM

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NUM

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(A

107

 

FOR 10°0LTO"COLT02.: K: .245 R2: .999
HO: 1.357 PE: .996 NU” ”TS:
FOR :OGOLTOMC0LTolo9: 3 .113 92: .963
”D: .641 R2: .955 NU“ DTS:
TRIAL 27
VALUES FOR SIDE A:
FOR HC.GT.2: K: .15; 92: .995
HO: 1.4a} DE: .955 NU” FTS:
F09 1.0.LT."C.LT.2.: = .095 93: .996
‘0: .635 P2: .991 NU” PTS:
FCD ‘.C.LT.“C.LT.1.3: = 0392 92: .991
P0. .215 92: .934 NU“ FTS:
VALUES COR SIDE 8:
F0: HC.GT.2: K: .153 93: .997
H3: 1.55 92: .999 NU“ PTS:
COR 1.3.LT.HC.LT.2.: K: .17“ D2: .99.
H3: 0552 DE: .957 NU“ PTS:
FOP 2.3.LT.MC.LT.1.‘: “3 .34“ oz: .995
“O: .222 ”I: .992 NUM 9T3:
TQIQL 25
VALUES Ff: SIDE A:
‘09 "C.GT.2: K: .‘37 :2: .999
“3: 2.251 :2: .995 ”U” 9T5:
‘CP l.3.LT.“C.LT.2.: 3 .39: 92: 1.353
"D: 1.799 92: .986 NU” FTS:
‘09 3.3.LT.MC.LT.1.:: “3 .154 9:: .9Qé
”3: 2..3° 9?: .757 NU“ PTS:
VALUES “CR SIDE E:
FOO HC.GT.2: «3 .515 3;: 1.?f’
HO: .ft: 92: .997 ”U” 075:
FOR 1.3.LTO~COLT.20: K: .4“: DC: .998
“D: 2...: 92: .973 ”U" 9T5:
CC? :OOOLTOMC05T0102: ‘3 .1“: =2: .912
”D: .361 :2: 0563 “2L7"! 5T5:
T=I5L 29
V:Lb§¢ ‘99 SILi :°
:09 "COCTOZ: ‘3 .121 :2: .999
“9: 1.959 9:: .999 ”U“ 9T5:
PC: 1.3.LT.MC.LT.2.: K: .111 9:: .99E
*8: .296 =2: .99: ”U" 9T5:
FOR ZOCOLT0VCOLToloc: K: .1“: =:: .993
“D: .677 9:: .999 NU“ CTS:
VALUES rCR SID: 8:
F0: “C.GTOZ: K: .13. :2: .995
“D: 10165 92: .991 Ht" 3T5:
:09 1.2.LT.“C.LT.2.: K: .117 92: .995
P3: .53: DC: .993 NU" aTS:
Coa 2.3-L70MC0LTolof: '3 0119 :3: 0995
“:3 .552 3:: .995 ”UV FTS:
T915. 3:
VALUFS F3: 9ILE L:
FOR “C.GT.Z: '3 .137 at: .997
“9: ."03 ‘2: .993 NU“ FTS:
FOP :OSOLTOMCOLTozo: “3 0:?7 9:: 1.2::

'0

.‘o
(d

0* .;
' I [h ‘0

.4

108

HD- .‘76 92: .996 NU“ pTS:
pOR 3.0.LT.MC.LT.1.3: K: .‘95 92: .991
F3: .373 92: .99“ NU“ =75:

VALUES “OR 5103 8:

FOR HC.GT.2: K: .196 92: .999
HQ: .713 ”2: .997 NU“ P75:
FOR 1.9.L7.HC.LT.2.: K: .105 9:: .999
H0: .516 :2: .996 NU“ DTS:
COR 3.C.LT.MC.LT.1.:: K: .295 :2: .995
”0: .397 92: .998 NUM PTS:
TRIAL 31
VALUES FOR SIDE A:
FOR ”C.GT.2:' K: .137 :2: .999
HO: 1.31; :2: .996 NU“ FTS:
COP 1.5.LT.‘C.LT.2.: K: .13: 2:: .999
9D: .569 :3: .999 NU” PTS:
FCD :OOOLTOMCOLT010:: K: .885 22: .980
”D: .393 02: .973 ”U“ PTS:
VALUES 93R SIDE E:
:00 MC.GT.:I “3 .137 92: .998
“C: .974 92: .996 NU“ PTS:
90? 1.:.LT.“C.L7.2.: K: .141 DE: .999
“3: .797 3:: 1.3C5 VUV 97$:
FUR 9.3.LT.MC.LT.1.:: K: .116 :2: .953
P9: .“7'7 3:: .952 NU” PTS:
T915L 3:
VALUZS FCD SIC: u:
CCD MC.F‘T.:: “3 .199 7:: .999
“D: .979 92: 1.5;: “U“ F75:
COR 1.3.LT."C.LT.2.: K: .171 :2: 1.C3€
HE: .955 9:: .999 1'” F73:
SEQ :.3.LT."C.LT.1.:: K: .121 0:: .977
“D: ""' :2: 19° YU” 975:
VALLZS {CR SICE 5:
F9: ”C.GT.:: K: .239 :2: .995
“3: 1.559 9:: .999 ”U" 97::
:0: 10$.LT.MC.LT.2.: K: .295 9:: 1.:5;
”3: 1.4;1 :2: .995 ”U” 973:
‘09 LoQoLT.”C.LT.l.;: ‘3 .lég 9:: .9:9
”3: 2.436 :2: .765 NU“ F75:
TRIAL 33
VALUES F99 5105 A:
‘09 ”C.GT.2: K: .356 :2: 1.393
”03 2.571 =3: .999 NUH PTS:
FOR 1.3.LT.VC.LT.2.: “3 .41- oz: 1.2:
“C: 2.1:9 9:: .999 NUM 97$:
to: 3.3.LToMC.LT.1.C: K: .272 93: .967
HD: .691 92: .915 NU“ P75:
VALU’S F39 5105 9:
F09 MC.GT.Z: 3 .37“ 92: .999
”93 2.597 92: .994 NU“ FTS:
FCG 1.3.LT.MC.LT.2.: K: .399 OS: 1.3::
“3: 1.79“ 93: .997 “U” F733
‘0‘ :.:.LT.”C.LT.1.:: “3 .211 C:: .965

f) k0

‘7‘

'Id

(I

APPENDI X D

109

 

 

 

 

 

 

 

0 SIMPLE n
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c0.00 2.00 5'0. 00 t91.002.01515.0 00
TIHE INO HOURS TINE INo HOURS

Figure D. Plot of the Actual Change in Moisture over Time for all
Trials

 

110

 

 

 

8 .
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TIME IN HOURS
Figure D. (continued)

0 SRHPLE n

 

 

 

A SRHPLE B
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TIME IN HOURS

111

o SRHPLE n

 

 

 

 

 

 

 

 

A SRHPLE B
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TIHE IN0 HOURS TIME IN0 HOURS
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Figure D. (continued)

112

o SAMPLE n

 

 

 

 

 

 

 

 

A SFIMPLE B
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TIME IN0 HOURS TIME IN0 HOURS
8 8
Ha? “‘5‘
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g TRIRL 15 g TRIRL 1s
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TIME IN0 HOURS TIME2 6IN0 HOURS

Figure D. (continued)

113

8.00

TRIRL 17

4.00

MOISTURE CONTENT (OB)
2.00

 

2&. 00 60.00
00TIME IN HOURS

co2.00

6.0
2‘:

TRIHL 19

4.00

1

2.00

l

MOISTURE CONTENT [DB]

 

 

cp.00

.00 M& 60.00
TIME IN0 HOURS

Figure D. (continued)

0 SHMPLE n
A SHHPLE a

8.00

  
   

TRIRL 18

4.00

MO I STUIEEOOCONTENT (OB)

cp.00

00 2 .00 60.00
TIME IN HOURS

8-00
A
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TRIRL 20

4.00
J

2.00

l

MOISTURE CONTENT (OB)

 

 

.00 wk 60. 00
TIME IN0 HOURS

cp.00

114
o SRMPLE Fl

 

 

 

 

 

A SIMPLE B
8 8
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$.00 2&.00 6'0.00 $.00 26.00 60.00
TIME IN HOURS TIME IN HOURS
8 8
..«3‘ ..45‘
CD (D
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2'. 2‘.
w' 0.1"
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TIME IN HOURS TIME IN HOURS

Figure D. (continued)

115

I

o SRMPLE n

A SRMPLE B

oo.o

60.0

TRIRL 26

2% .00
TIME IN HOUR

 

30.00

 

840 ooé
“no. hzmpzou mmapwuoa:

     

.00

TIME IN HOURS

      

  

TRIRL 25

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8.. Sé 8&0
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TIME IN HOURS

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I
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6‘0 .00

TRIHL 27
TIME IN HOURS

21; .00

 

 

“0.00

oo.v co. .N on.
”may hzwhzou mantho:

(continued)

F1gure D.

116

o SRMPLE n

 

 

 

 

 

 

A SRMPLE B
8 ' 8
-6] -6
ID (D
S TRIFIL 29 e TRIRL 30
541 E;
v- I—
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we. we:
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r— +-
52 2
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‘0 .00 26 .00 6'0 .00 ‘0'.00 2'6 .00 60 .00
TIME IN HOURS TIME IN HOURS
8 8
..«3‘ A“?
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E TRIRL 31 S TRIHL 32
0—8 "g I
54‘ E;-
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TPME IN HOURS TIME IN HOURS

Figure D. (continued)

117

0 SRMPLE n
A SRMPLE a

6-00

I

gel

TRIRL 33

l

ENT (08
4.00

 

‘
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MOISTURE CONT
2.00

c0:00

 

 

on 25.00 6b.00
TIME IN HOURS

Figure D. (continued)

MICHIGAN STATE UNIV. LIBRARIES
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