HYDROTHERMAL CHARACTERISTICS

AND QUALITY OF ALFALFA CUBES

DURING TRANSPORT

A Thesis Submitted to the College of

Graduate Studies and Research

in Partial Fulfillrnent of the Requirements

for the Degree of Doctor of Phi losophy

in the Department of Agricultural and Bioresource Engineering

University of Saskatchewan

Saskatoon

Mohammad Hadi Khoshtaghaza

C ~ L C 1997

O Copyright Mohammad Hadi Khoshtaghaza, 1997. Al1 rights reserved.

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UNIVERSITY OF SASKATCHEWAN

College of Graduate Studies and Research

Submitted m partial fiilfillment

of the requirements for the

DEGREE OF DOCTOR OF PHEOSOPBY

Department of Agiculhu;ll and Bioresource Engineering College of Engineering

University of Saskatchewan

Examinhg Committee:

eecta/hw&&bWDean's Designate, Chair Coiiege of Graduate Studies and Research

Dr. D.I. N o m

Dr. S. Sokhansanj

Dr. G.J. Schoenau

Dr. B. Gossen

Dr. E.M. Barber

Dr. R. Ford

External Examiner:

Chair of Advisory Committee, Dept. of Agriculturai and Bioresource Engioeerhg

Supervisor, Dept. of Agric. and Bioresource Engineering

Dept . of Mechanical Engineering

Agriculture and Agri-Food Canada

Dept. of Agricultural and Bioresource Engineering

Dept. of Agricultural and Bioresource Engineering

Dr. G.S.V. Raghavan Dept. of Agric. & Biosystems Eng. McGa University, McDonald Campus. SA-Bellevue, Que. H9X 3V9

Hydrothermal Characteristics and Quality of

Alfalfa Cubes During Transport

Alf ia cubes are transporteci over long distances for export. The environmental

conditions that the cubes may be exposeci to m transit codd vary h m below f i k g to

m excess of 40°C anci relative humidity. up to 100%, eqeciaüy when cubes are exported to

h d regions of the Pacific Rim Under humid .Condition, cubes absorb moisture and

becorne prone to spoilage at high temperature. The objectives of thb research were to

determine tirne-temperature-humidity combinations for safe storage of cubes and to

develop a mode1 for estimahg the hyckothermal dyaamics of cubes diiring transport.

Samples of commercial alfia cubes wae exposeci to combinations of temperatures

fîom 9" to 39°C a d relative humidities (RH) h m 60% to 86% for 66 and 90 &ys in

closed chambers. Cube moisture content, color, density, hardness, durabiliry, and time of

appearance of molds were measured. Dynamic equations representing quaüty change with

respect to thne and storage conditions were developed. The Nbes stored klow 71 % RH

did not develop mold, but aii of the cubes lost some degree of their greenness.

Discoloration was severe at higher temperatures and humidities. Density, hardness, and

durabdity of cubes declined sgdicantly at 80% relative humiday.

Data nom mstrumented contaherized alfhifà cube shipments nom Canada to Taiwan

were analyzed. Temperatures and relative humidities were monirored during transit, and

the moisture contents and durabilities of alfia cubes wae measured on samp1es taken at

the time of loading and doadmg. The calculateci spoilage potemial agreed wÏth the

mcidence of mold recorded at the time of doadmg.

Heat balance equations based on buk thermal diffiisivities and natural convection

were developed. Several boundary conditions represdg the dynamics of cube

surrounnings during shipments were mvestigated. It was shown thaî for prediction of the

cube temperature, temperatures both the head Wace above the cubes and the container

ceiling were required.

The moisture transfa withm the cube pile m the contanier was modeled as a closed

system, Le. assuming no moisture transfer between hide a d outnde of the container.

The calculateci humidity ratio in the headspace was lowa than the measured humidity

ratio. It was concluded that the source of extra moisture inside the container was outside

mojsture penetrating mto the container-

PERMISSION TO USE

In presenting this thesis in partial fulfillment of the requirements for a Postgraduate

degree from the University of Saskatchewan, 1 agree that the Libraries of this University

may make it freely available for inspection. 1 further agree that permission for copying of

this thesis in any manner, in whole or in part, for scholarly purposes may be granted by

the professor or professors who supervised my thesis work or, in their absence, by the

Head of the Department or the Dean of the College in which my thesis work was done. It

is understood that any copying or publication or use of this thesis or parts thereof for

financiai gain shail not be allowed without my written permission. It is aIso understood

that due recognition shd l be given to me and to the University of Saskatchenm in any

scholarly use which may be made of any material in my thesis.

Requests for permission to copy or to make other use of material in this thesis in

whole or part should be addressed to:

Head of the Department of Agriculturai and Bioresource Engineering

University of Saskatchewan

57 Campus Drive, Saskatoon, Saskatchewan S7X 5A9

ABSTRACT

Alfalfa cubes are transponed over long distances for export. The ambient conditions

that the cubes may be exposed to in transit could Vary from below freezing to in excess of

40°C and relative hurnidity up to 10096, especially when cubes are exported to hurnid

regions of the Pacific Rim. Under hurnid condition, cubes absorb moisture and become

prone to spoilage at high temperature. The objectives of this research were to determine

time-temperature-humidity combinations for safe storage of cubes and to develop a mode1

for estimating the hydrothermal dynamics of cubes during transport.

Sarnples of commercial alfalfa cubes were exposed to combinations of temperatures

from 9" to 39°C and relative humidities (RH) from 60% to 86% for 66 and 90 days in

closed chambers. Cube moisture content, colour, densi ty, hardness, durabili ty, and time of

appearance of molds were measured. Dynamic equations representing quality change with

respect to time and storage conditions were developed. The cubes stored below 7 1% RH

did not develop mold, but ail of the cubes lost some degree of their greenness.

Discoloration was severe at higher temperatures and humidities. Density, hardness, and

durability of cubes declined significantly at 80% relative hurnidity.

Data frorn instrumented containerized alfalfa cube shipments from Canada to

Taiwan were analyzed. Temperatures and relative humidities were monitored during

transit, and the moisture contents and durabilities of dfalfa cubes were measured on

sarnples taken at the time of loading and unloading. The calculated spoilage potential

agreed with the incidence of mold recorded at the time of unloading.

Heat balance equations based on bulk thermal diffusivities and natura! convection

were developed. Several boundary conditions representing the dynamics of cube

surroundings during shipments were investigated. It was shown that for prediction of the

cube temperature, temperatures both the head space above the cubes and the container

ceiling were required.

The moisture transfer within the cube pile in the container was modeled as a closed

system, i.e. assuming no moisture transfer between inside and outside of the container.

The calculated humidity ratio in the headspace was lower than the measured humidity

ratio- It was concluded that the source of extra moisture inside the container was outside

moisture penetrating into the container.

ACKNOWLEDGEMENTS

May al1 praise be to God

1 would like to express my gratitude and sincere appreciation to my acadernic

advisor, Professor Shahab Sokhansanj, for his continued support, guidance, patience and

encouragement throughout al1 stages of this study.

My sincere thanks and gratitude to my advisory cornmittee members, Professor G.J.

Schoenau of Mechanical Engineering, Dr. B. Gossen of Agriculture and Agri-Food

Canada, Professor D.I. Norum, Professor E.M. Barber, and Professor R. Ford of

Agricultural and Bioresource Engineering for their guidance and support.

Special thanks go to the following individu&: Dr. En-Zen Jan of Agriculture and

Agn-Food Canada for his professional efforts on collecting data on alfalfa cube shipment,

Mr. D. Pulkinen of KAPT-AL Services Ltd., Tisdale, for supplying the alfalfa cube

samples used in my experiments, Mr. W. Morley and Mr. L. Roth of Agncultural and

Bioresource Engineering for assistance in preparing and setting up the experiment. Dr.

L.G. Tabil and Mr. W. Crerar of Agricultural and Bioresource Engineering for their

advise and assistance during this research.

My deepest acknowledgments are extended to the Ministry of Culture and Higher

Education of the Islamic Republic of Iran for granting me a Ph.D. Scholarship.

This thesis is made possible with the financial support by the KAPT-AL Services

Ltd., the Canadian Dehydrators Association, and the Natural Sciences and Engineering

Research CounciI of Canada.

Last but not least, my deepest appreciation goes to my wife Maryam, my son Mehdi

(14), and my daughter Marzieh (10). Without their love, support, and endless patience. 1

could not have completed this work.

Dedicated to rny motlr er

and

in mernory of my father

TABLE OF CONTENTS

APPENDIX B: EXPERIMENTAL DATA ON ALFALFA CUBES SHI~MENT---------------------------------------------------------------------------- 119

B.1 Background-------------------------------------------------------------------- 120 B.2 Material and Mehods-------------------------------------------------------- 120

B .2.1 Cube containers and instrumentation ------------------------------- 130 B 2.2 Loading, transportation, and unloading ---------------------------- 121 B.2.3 Cube inspection and testing 123

LIST OF TABLES

Table 1 . 1 :

Table 1.2:

Table 1.3:

Table 1.4:

Table 2.1 :

Table 3.1:

Table 3.2:

Table 3.3:

Table 3.4:

Table 3.5:

Table 3.6:

Table 3.7:

Table 3.8:

Table 3.9:

Table 3.10: Nutritional value (%, dry basis) after 90 days of storage.----------------------- 52

Table 3.1 1 : Days in transit and spoilage index (SI) at stages of shipment. ----------------- 55

Table 4.1 :

Table 4.2:

Table 4.3:

Table 4.4:

Table 4.5:

Table 4.6:

TabIe 4.7:

Table 4.8:

Table 4.9:

Table 5.1:

Table 6.1 :

Table 6.2:

Table A 1 :

Table A 2

Table A3:

storage. --------------------------------------------------------------------------------- 98

Colour, moisture , hardness, and density variations over time during

storage at 16.2"C. ------------ .................................... ------------------ 1 09

Colour, moisture , hardness, and density variations over time during

storage at 24.OOC. .................................................................. 110

Colour, moisture , hardness, and density variations over time during

storage at 3 1.4"C. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1 1 1

storage. --------------------------------------------------------------------------- 117

Table A12: Temperature and RH of test charnbers stored at 40°C for 90 days of

LIST OF FIGURES

Figure 1.1: Regular size alfalfa cube (30 x 30 mm x 40 mm long).------------------------- 3

Figure 1.2: Flowchart of a typicd alfalfa cubing operation in Canada (Sokhansanj

and Wood, 199 l)-------------------------------------------------------------------- 4

Figure 1.3: Temperature of grapefmit in the experimental shipping container from

Florida to Europe.(Hinds, 1970). Thermostat was set at 14°C. --------------- 10

Figure 1.4: Temperature regimes in experimental shipments of citms fruit in general

cargo sealed containers (Orner and S hushan, 1978). ........................... 1 1

Figure 2.1 : Air-tight cylindncai controlled-humidity charnber containing alfalfa

cubes and sulfuric açid solution.------ ............................................. 19

Figure 2.2: Control environment room and test chambers in the control environment

room.--------------------------------------*-*------------------------------------------ 2 1

Figure 2.3: Measurement of temperature and humidity of the air space inside a test

-- chamber. Alfalia cubes are seen through the transparent cover. -------------- 73

Figure 2.4: Colour coordinate values of the Hunterlab spectrophotometer (L*a*b). ---- 25

Figure 3.1 : Cube moisture content during 66 days storage (initial rnoisture lO.3%,

w.b.),----------------------------------------------------------------------------------- 3 1

Figure 3.3: Final moisture content of cubes for given storage temperature and

relative humidity. ................................................................... 32

Figure 3.4: Cube green colour ('a' value) during 66 days storage. ------------------------- 35

Figure 3.5: Cube green colour ('a' value) during 90 days storage. ......................... 36

Figure 3.6: Experimental data and mode1 (Eg. 3.4) fitted to the data green colour

ratio (a/ai) at 3 1 OC storage. -------------- ------- ------------------------------------ 38

Figure 3.7: Cube surface conditions at the end of 66 days of storage (T=24' and

3 l0C).--------------------*------------------------------------------------------------- 4 1

Figure 3.8: Cube surface conditions at the end of 90 days of storage (T=gO and 39°C). 42

Figure 3.9: Alfalfa cube density during 66 days storage (initial density 0.8 L hIg/m3). --44

Figure 3.10: Alfalfa cube density during 90 days storage (initiai density 0.73 ~ ~ / m 3 ~ ) . -45

Figure 3.1 1 : Experirnental data and model (Eq. 3.7) fitted to experimental data of

density ratio @/pi) of alfalfa cubes at 24°C storage. ---------------------------- 46

Figure 3.12: Alfalfa cube hardness during 66 days of storage (initial hardness 0.34 k ! ) . 4 7

Figure 3.13: Experirnental data and model (Eq. 3.8) fitted to experimental data of

hardness ratio (H/Hi) at 3 1 OC .------------------------------------------------------ 49

Figure 3.14: Typical spoiiage index versus transit time for actual shipments during the

Figure 4.1 :

Figure 4.2:

Figure 4.3:

Figure 4.4:

Figure 4.5:

Figure 4.6:

Figure 4.7:

summer of 1993 and the winter of 1994. -- ---- ------------ ---------------------- 54

Heat transfer inside alfalfa cube container. q, is radiation heat transfer

between container walI and cube surface. qh is the convective heat transfer

between the headspace and cube surface. Tc, Ta, Ts (input data), and Tp are

ceiling, headspace, surface and pile temperature, respectively. Drawing is

net to scale. ----------------------------------------------------------------------- 58

Exarnple problem for a serni-infinite solution of heat equations. This

problern was used to compare the numerical solution with the analytical

solution- ------------------------------------------------------------------------------- 65

Recorded and computed temperatures at 60 cm below the surface of the

cube with constant surface temperature condition for the January 1994

shipment.----------------------------------------------------------------------------- 69

Recorded and computed temperatures at 60 cm below the surface of cube

with convection boundary condition for the January 1994 shipment.-------- 69

Recorded and computed surface temperature of cube with convection

boundary condition for the January 1994 shipment. ---------------------------- 70

Recorded and computed temperatures at 60 cm below the surface of cube

with convection and radiation boundary condition for the January 1994

shipment.------------------------------------------------------------------------------ 70

Recorded and computed surface temperatures of cube with convection

and radiation boundary condition for the January 1994 shipment. ----------- 7 1

Figure 4.8: Recorded and cornputed temperatures at 60 cm below the surface of cri bc

with constant surface temperature condition for the July 1993 shipment. --- 75

Figure 4.9: Recorded and computed temperatures at 60 cm below the surface of cube

with convection boundary condition for the July 1993 shipment. ------------ 75

Figure 4.10: Recorded and computed surface temperatures of cube with the convec tive

boundary condition for the July 1993 shipment. -------------------------------- 76

Figure 4.1 1: Recorded and computed temperatures at 60 cm beIow the surface of cube

with convection and radiation boundary condition for the July 1993

skpment.------------------------------------------------*----------------------------- 76

Figure 4.12: Recorded and computed surface temperature of cube with the combined

Figure 5.1 :

Figure 5.2:

Figure 5.3:

Figure 5.4:

Figure 5.5:

Figure 5.6:

convection and radiation boundary condition for the July 1993 shipment.-- 77

Finite difference nodes for the numerical solution of rnoisture transfer

within bulk cubes and between cubes and the headspace. Drawing is not

to scale.-------------------------------------------------------------------------------- 85

Hurnidity ratio inside the cube container calculated from the recorded

temperature and RH and the simulated humidity caiculated from Eq. 5.16

(computed at sealed condition) for the May 1993 shipment. ------------------ 87

Hurnidity ratio inside the cube container calculated from the recorded

temperature and RH and the simulated humidity calculated from Eq. 5.16

(computed at seaied condition) for the July 1993 shipment. ------------------ 87

Humidity ratio inside the cube container calculated from the recorded

temperature and RH and the simulated hurnidity calculated from Eq. 5.16

(computed at seded condition) for the October 1993 shipment.-------------- 88

Hurnidity ratio inside the cube container caiculated from the recorded

temperature and RH and the simulated hurnidity calcuiated from Eq. 5.16

(computed at seaied condition) for the January 1994 shipment. -------------- 88

Humidity ratio inside the cube container calculated from the recorded

temperature and RH and the simulated humidity calculated from Eq. 5.16

(computed at seded condition) for the June 1994 shipment, ------------------ 89

xiv

Figure B 1 :

Figure B2:

Figure B3:

Figure B4:

Figure B5:

for the June 1994 shipment. -- ------------ ------ -------- --..------- ------------------ 94

Location of sensors in the container. ---- -------- ------ ------- ------- ------- - - I ,- "3

ReIative locations of cube containers onboard ship for trips 1, 3,4,

and 5 . -------------------------------------------------------------------------------- 132

Air temperature and relative humidity inside the container, May 1993. --- 137

Typical cube profiles within container: a) at loading, b) during transit. c )

upon amval. ........................................................................ 130

Typical humidity ratio versus time of the shipment during summer and

winter- ------------------------------------------------------------------------------- 133

LIST OF SYMBOLS

moisture concentration

specific heat

number of mold free days

moisture diffusion

diffusion coeffkient of water vapor in air

seed viability

heat transfer coefficient

mass transfer coefficient

cube hardness

themal conductivity

moisture adsorption rate constant

colour coorinates

moisture content

moisture content

equilibrium moisture content

moisture flow

convective heat transfer

radiation heat transfer

saturation vapor pressure

water vapor pressure

radiai distance from center

radius

relative humidity

spoilage index

temperature

ceiling temperature

fraction

w . ~ - ' . K "

TP u

t

t h

ts

W

X

Greek

E

El

a

A

CL

v

8-

0

P

pile temperature

intergranular air velocity

storage time

time in hours

time in seconds

width of container

linear dimensions

emissivity

porosity

thermal difisivity

change in a parameter

viscosity

kinematic viscosity

temperature ratio

standard deviation

density

Subscripts

a air

b bulk

f finai

i initial

s surface

fraction

fraction

CHAPTER ONE

INTRODUCTION

This chapter briefly reviews the production of alfalfa cubes in Canada and overseas

shipment. A brief literature review on shelf life of alfalfa cubes and related products is

presented. A detailed review of literature on the quality of alfaifa cubes in storage and in

transit will be presented in other chapters. This chapter outlines the objectives of this

research.

1.1 Background

Alfalfa is one of the most important forage plants in North Arnerica, because of its

digestibility and available protein as an animal feed. According to Hoveland (1980),

dehydrated aifalfa contains on average 19% crude protein, 26% crude fiber and 10.5

MJkg digestible energy.

Zn addition to conventional silage and hay making, alfalfa is also processed into

cubes, pellets, and compressed bales to facilitate its handling and transportation

(Sokhansanj and Wood, 1991). Because of the larger size particles in the alfalfa cubes as

compared to smalt particles in alfalfa pellets, the demand for alfalfa cubes as a nutritious

ruminant feed source is increasing. Larger particles facilitate by-pass protein digestion for

a better feed conversion in dairy cattle (Christensen, 1990).

To produce cubes and pellets, fresh alfalfa is cut and pre-wilted in the field before

being chopped and brought to the plant site. At the plant, the chopped pre-wilted alfalfa is

dried to about 8% moisture content (w.b.) in thermal dryers. Ln dry climates, the chopped

alfalfa might be sufficiently dry to by-pass the thermal dryer.

To make cubes, the dried chops are rnixed with a small quantity of water to activate

the binding characteristics of alfalfa (Sokhansanj et al., 1992). Using a cubing mill, the

moistened alfalfa chops, at about 12% rnoisture content (w-b.) are pressed into cubes.

After their formation, the cubes, at about 50°C and about 12% moisture content (w.b.).

are cooled and dried. The finai moisture content of the cubes is about 10% (Hunter and

Sokhansanj, 1991). Figure 1.1 shows a photograph of a typical 30 mmx 30 mm x 40 mm

long alfalfa cube. Larger or smaller cubes are also produced. The cubes are stored under

cover prior to being loaded into shipping containers. The steps in the production of alfalfa

cubes from field to shipping are shown in Figure 1.2.

Containerized shipment of cubes is an effective method of transponing cubes from

Canada to destinations overseas. Alfaifa pellets are usuaily handled and shipped in bulk

and thus the physical damage to these pellets is severe. Containerized cubes undergo less

physical darnage since, once cubes are loaded into the container, they are not removed

until the container reaches its final destination.

Cube shipping containers are not climate controlled intemally. The walls are not

insulated and mechanical ventilation is not used. Most shipping containers are supposed

to be seded, with no direct inter-connection between the intemal environment and the

outside once the container door is closed. Cubes in the container, however undergo

temperature and hurnidity changes that are brought about by outside conditions. These

vaxying conditions include diurnal variations in temperatures in Canada, varying ambient

conditions while at sea and extremely humid and hot temperatures in most locations in

the Pacific Rim countries of destination. The temperature may reach as high as 40°C and

direct Sun exposure of the container may raise the container wall temperature to as high as

60°C.

Biochernical reactions and the activity of microorganisms are influenced by t hc

relative humidity and temperature of the surrounding air. The ambient conditions promote

discoforation and mold growth and affect density, hardness and durability of alfalfa cubes

during storage and transport. Moldy and discolored alfalfa cubes are severely devalued in

domestic and export markets. Cubes with low durability are susceptible to breaking into

smaller pieces that cause low feed quality and difficulties in handling.

ALFALFA CUBE

Figure 1.1: Regular size alfalfa cube (30 mmx 30 mm x 40 mm long).

Alfalfa in Field

O Small Dehydrator

i

Bales Dry Chop Green Chop

( 2 0 - 3 0 s m.c.) (60-80s m.c. )

Transport 1 Transport Transport to Plant to Plant CO Plant

Weight & Weight & Weight & Moisture Moisture Moisture

O Large

Check

Tub Grinder or Chopper

1 1 Dehydrator

O Temporary S torage

I

Check

9 Metering Bin

Check

Mixer

Cuber

Cooler

S to rage

O Bulk Load and Ship

Figure 1.2: Fiowchart of a typical alfalfa cubing operation in Canada (Sokhansanj and Wood, 199 1 ).

1.2 Previous Research on Storage and Transportation of Forage Products

1.2.1 Storage

Research has been conducted on the equilibrium moisture content of loose hay,

particularly of alfalfa (Zink, 1935; Dexter et al., 1947). Table 1.1 is a summary of the

combined data from these researchers showing that, on average, the equilibrium moisture

content of the second-cut hay is lower than that of the first-cut. Second-cut hay has more

stems than the first-cut, and thus a lesser hygroscopicity. First-cut hay is more leafy and

contains more protein. Proteins not only absorb moisture readily, but also degrade easily.

Table 1.1 shows a sudden increase in moisture content of alfalfa when the relative

humidity increases from 60% to 70%. Leaves have much more affinity for water than

stems. in al1 cases, relative humidities higher than 70% resulted in an abrupt increase in

moisture content. Dexter et al. (1947) aiso tested the moisture-absorbing characteristics of

freshly-cut alfalfa. They observed that undried, fresh-cut alfalfa becomes moldy very

quickiy, and this results in substantial dry matter loss.

HeadIy (1969) published a set of moisture content and relative humidity of alfalfa

pellets (6 mm diameter) data. Table 1.2 shows that at 210C and RH of 76%, pellets are at

14.3% moisture content (w.b.), which is a safe moisture content for storage. When the

RH increased to 80% at the same temperature, the moisture content of the pellets

increased to 16.3%. The equilibrium moisture content of pellets was higher at cooler

temperatures for example, at 1 1°C and 75% RH, the pellet moisture content was 17%.

Headly (1969) also investigated the moisture uptake by pellets in a humid environment

and the resulting increase in the volume of the pellets. Table 1.3 shows the percent

expansion of 6 mm diameter pellets initially at 7% ta 9% moisture content (wb.). The

storage temperature for this pellet was 1 lac. The original graph presented by Headly

(1969) indicated that, when stored at 58% and 75% RH, pellets approached the maximum

expansion after two weeks. The percent expansion at RH of 93% after two weeks was

within 90% of the asymptote value.

Headly (1969) investigated the effect of storing pellets at high humidity and the

Table 1.1 : Equilibrium moisture content (%) for alfalfa hay stored at 23°C and various relative humidity levels (Zink, 1935; Dexter et al. 1947).

Treaunent Relative hurnidity, 9% 20 40 50 60 70 80 90

First cut (dried) 10.2 11.7 11.0 12.0 16.0 17.8 20.7" Second cut (dried) 5.7 8.5 9 .O 10.6 15.0 14.9 18.4" Fresh cut (undried) 7.4 9.8 10.6 12.8 15.7 18.6* 36.8+ Fresh cut (dned) 6.4 9.4 - 13.3 - 19.0* 23.5+ Stems (dried) - - 9.4 - 12.5 15.8 21.7* Leaves (dried) - - 10.4 - 13.8 17.5 24.8"

'*' Moldy '+' Very rnoldy '-' not available

Table 1.2: Equilibrium moisture content (9%) and relative humidity of alfaifa peliets stored at three temperatures (Headly, 1969).

11°C 31°C 32°C RH, % m.c., % RH, % m.c., % RH, 5% rn-c., 70 25 .O 6.9 23 6.8 22 5.9 40.0 7.5 35 7.1 33 8.8 58.0 14.1 55 12.0 5 1 12.0 75 .O 17.3 76 14.3 76 14.2 82.0 20.4 80 16.3 - - 93 .O 25.7 - - - -

'm.c.' moisture content, w-b.

Table 1.3: Percent volume expansion of the alfalfa pellets stored at 11 OC environment (Headly , 1969).

Relative Humidity S torage Period (%) 1 week 3 weeks 58 5% 6%

development of mold on the pellets. He observed that storing 7% to 9% moisture content

(w-b.) pellets in RH greater than 75% and at a temperature of 110C resulted in moldiness.

Regardless of the temperature, when pellets were stored at relative humidities less than

75%. the probability of mold growth was nil. In the same study, Headly (1969) performed

a limited number of durabiIity tests on the pellets stored at different environments. The

data showed that storing at RH between 55% to 75% RH produced the most durable

pellets. Pellets stored outside of this range, either at higher or Iower relative humidities.

broke more easily.

One may compare the equilibrium moisture content of Ioose alfalfa hay (Table 1 .1)

with that of dense compacted alfalfa hay (Table 1.2). It seems that first-cut loose hay is

more hygroscopic than processed compacted hay (pellets). However, pellets absorb more

moisture than second-cut loose hay when exposed to the same environment.

Chaplin and Tetlow (197 1) studied the storage stability of cubes (or wafers). both

whole and ground of alfalfa and several grasses. The tests were conducted in the

laboratory at 2 1 OC in a controlled humidity chamber. The cube sarnples were brought to

equilibnum with relative humidities ranging from 50% to 90%. Some samples were

treated with propionic acids and some were prepared with binders (calcium

lignosulphonate). Mold development was found to be slower on pure protein than on pure

starch, and much slower on pure fiber substrates. Compared to the starchy grains, it

appeared that mold growth on forages was slower. The use of a binding agent had littIe

effect on the mold growth. Propionic acid sprayed on samples improved the storage time.

Table 1.4 lists a surnrnary of the results of Chaplin and Tetlow's experiments on alfaIfa.

The molds identified on most sarnples were from the Aspergillus glaucrcs group. Also A.

ruber, A. repens, and A. chevalier were identified on a few samples.

Chaplin and Tetlow (1971) also studied the moisture uptake of cubes in bulk by

exposing them to a change in relative humidity. First a bulk quantity of wafers was

exposed to 90% humidity (1 loC) for 6 days followed by a drop in humidity to 45% for

the following six days. Their findings showed that the top layer gradually absorbed

moisture as the humidity penetrated within the pile. Following a drop in relative

hurnidity, the top tended to return to a Iower moisture content. However, it appears that

Table 1.4: Number of days before the first appearance of mycelium and spores on alfalfa pellets for various treatments at 21°C and a range of relative hurnidity (Chaplin and Tetlow, 197 1).

Treatments Presence of ReIative humidity, %

Pellet only Spores + 350 83 20 - Mycelium 410 222 69 - -

Pellet + Binding agent Spores + 210 75 20 - M ycelium 410 141 45 - -

Pellet + Fungus inhibitor Spores - - - + 141 (propionic acid) Mycelium - - - + 102

'+' more than 440 days '-' not recorded

the rnoisture of the pile increased gradually.

Fasina and Sokhansanj (1992) investigated hygroscopic moisture absorption by

alfdfa cubes and pellets. The cubes and pellets were exposed to ambient conditions

ranging from 70% to 90% relative hurnidity and 10°C to 40°C. The solid pieces absorbed

up to 12 percentages points in moisture during a 48 to 72 h period. The corresponding

increases in solids volume were 15% to 20%. The increased volume and moisture content

of the cubes and pellets made them more susceptible to breakage and disintegration.

1.2.2 Transit conditions

Transcontinental and overseas shipment of agricultural products utilizes several

combinations of transport modes. The principal modes for fresh produce transportation

are tmck, train, ship and airplane. The main feature that sets fresh produce transportation

apart from other cornrnodities and manufactured products is its perishability, requiri ng i n

transit cooling or heating facilities (Ryall and Lipton, 1979; Ryall and Pentzer, 1982:

Peleg, 1 985).

Most of the previous tests on transport containers have been conducted on

refrigerated or environmentally controlled containers for fresh and perishable material.

These studies have involved the development of accurate methods for measuring heat

leakage under standardized test environments. checking vehicles of different constniction

and insulation for air leakage and insulation efficiency in control chambers, and over road

tests with commercial loads of perishables.

Hinds (1970) studied more than LOO shipments of fresh produce from the United

States to Europe. An experimental container with dual refrigeration and air circulation

system was used for an export shipment of grapefruit from Fionda to Europe. hitialiy

grapefruit at 29°C were cooled to the desired 14°C in 5 days. After loading the product in

containers, it was shipped by road or rail to Norfolk, Virginia and then by sea to France.

The time in transit was 19 days. Figure 1.3 shows the temperatures of the grapefruit and

the outside air during transport. The data showed that once the product was cooled to

14°C it maintained its temperature throughout transit when refrigerated.

In marine transport, refiigerated or ventilated containers of perishable material are

usually carried on the deck, while dry cargo is stowed below the deck (Peleg, 1985).

Orner and Shushan (1978) studied on large-scale experimental shipments of Israeli citnis

to European ports in sealed containers. In these experiments, they recorded temperature

regimes dunng shipments over 14 days (Fig 1.4). The containers were filleci on

November 24, 1977, then transported by truck and loaded on board ship in Haifa port

(Nov. 27), unloaded at the Adriatic port of Triest (Dec. 3), and loaded on rail cars (Dec.

3, arriving in Munich (Dec. 7). Curves A and B (Fig 1.4) are the outside air ambient

temperatures and sea water temperature, respectively. Curves C and D are records of

rnean temperature of nonprecooled fruit in containers on and below deck, respectively.

Curves E and F are mean precooled temperature in the container below and on the deck.

respectively. Because of high ambient temperatures in Israel, the fruit temperatures

increased significantly in al1 containers until the ship's departure from Haifa port. As the

containers approached the European winter climate, the fruit temperature dropped

gradually . Fruit temperature in the containers placed below deck was influenced by the sea

water temperature, while the temperature of the containers on deck was primarily ;i

function of ambient air temperature. Because the containers were practically sealed, Orner

and Shushan (1978) found that there was no air movement from inside out and heat

transfer mechanism was primarily conduction inside the container and convection

O 5 10 15 2 0 D A Y S in TRANSIT

Figure 1.3: Temperature of grapefruit in the expenmental shipping container from Florida to Europe.(Hinds, 1970). Thermostat was set at 14°C.

A ambient temperarure B water temperature C no precooling on deck D no precooling below deck E precooling below deck F precooling on deck ( 1 ) containers loading (2) deparnire from Haifa (3) discharging in Triest (4) loading on railcards in Trie

Figure 1.4: Temperature regimes in experirnenral shipments of citrus fruit in general cargo sealed containers (Orner and Shushan, 1978).

outside. Their test data showed that the quality of the citrus on the deck was better, due to

better air circulation on deck (more air currents around the containers).

The reviewed Iiterature includes research data prirnarily applicable to the

management of on-farm hay storage. The data do not include extreme conditions that

normally occur during the overseas shipment of alfalfa cubes. Therefore available data are

not sufficiently complete enough to allow the development of mathematical models

essential for prediction of cube quality during transport.

1.3 Objectives

The overall objective of this research is to determine the major physical factors that

controI the quality of alfalfa cubes during shipment. The following specific objectives are

set:

1) Conduct laboratory experiments to quantify the effect of relative hurnidity and

temperature on cube moisture content, mold, colour, durability, hardness, and density.

2 ) Combine the functions developed in ( 1) with the thermal data recorded for shipping

containers to calculate cube quality and compare the calcuiated values with the field

data,

3) Develop a mathematical mode1 of the transient thermal and moisture transfers in

shipping containers and conduct a theoretical analysis of cube quality during transit.

CHAPTER TWO

QUALITY OF CUBES IN CONTROLLED

ErnRONMENTS

Alfalfa cubes, particularIy during shipment, are exposed to wide variations in

temperature and RH. Predictive models are needed to integrate the Ioss of quality as a

function of tirne and arnbient conditions. Adequate experimental data on cube spoilage as

a function of temperature and RH are not available to develop the predictive quality

models. This chapter describes two series of experiments conducted to monitor changes

in the quality of cubes stored under controlled environrnents as a function of tirne. The

results will be discussed in Chapter three.

2.1 Background

A number of factors, such as initial cube temperature and moisture content, air

temperature and RH affect the storage stability of alfalfa cubes. These factors affect not

only the physical characteristics (e.g mold growth, colour, density, hardness and

durability), but also the quaiity of available nutritional constituents, such as

carbohydrates, proteins and vitarnins (Collins et al., 1987; Buckrnaster et al., 1989).

2.1.1 Physical attributes

Fasina and Sokhansanj (1993) determined the equilibrium moisture content of

alfalfa pellets and cubes for initial moisture contents ranging from 5% to 70% and

temperatures ranging from 10" to 40°C. When the RH exceeded 75%, the moisture

content of the pellets increased to more than 15%. They found that alfalfa cubes stored at

22°C and more than 75% RH became moldy. Snow et al. (1944) investigated the

relationship between the moisture content of dried grass (species unknown) and the RH

of the surrounding air during storage. MoId developed quickly at 75400% RH and slowly

under 75% RH, and the rate of mold growth was higher at 22°C than at 155°C. They also

studied the number of rnold-free days in other feedstuffs and found that the main factors

controlling mold growth were the RH, storage duration and temperature, and the chernical

composition of stored material.

Colour is a primary consideration in quality evaluation of feed products. Bright

green leaf colour, while it has no direct correlation with nutritional value, is usually

associated with high-protein and high-carotene contents (Walton, 1983). Alfalfa cube

colour is a quick subjective assay used by brokers and sellers alike (Black et al., 1990).

For this reason preservation of green colour in forage during processing and storage is

important on marketing. Greenness is directly reiated to the chlorophyll concentration.

Discoloration due to chlorophyll oxidation or chlorophyll loss causes degradation of the

green pigment.

Durability is a physical characteristic to represent the ability of the cube to

withstand the external impact and frictional forces during transportation to distant

markets. Durabiiity tests are often used to evaluate handling of alfdfa cube (Fasina and

Sokhansanj, 1992). Chaplin and Tetlow (1971) studied the durability of several dried

forage wafers stored at 21°C and 67-90% RH. They found that the durability of wafers,

including alfalfa, decreased slightly during 9 months of storage. Fasina and Sokhansanj

(1992) found that the durability of alfalfa cubes and pellets increased initially as moisture

content increased by 4 percentage points and then decreased with further increase in

moisture content. They found that during storage at high RH, cube density varied between

0.6 and 0.9 ~ g / r n ~ without showing any specific trend with the increasing moisture. The

density of alfalfa cubes decreased when cubes were exposed to a high RH environment.

Tabil and Sokhansanj (1995) investigated the effect of moisture absorption on

durability, hardness and volume expansion of pellets. They reported that the durabiIity of

pellets did not change from initial moisture content (6.5 to 7.5%) up to 10% moisture.

Durability decreased when moisture content exceeded 10%.

Patil et al. (1996) expressed cube hardness in terms of firmness, which effects

chewability and palatability of forage cubes. They measured hardness of cubes using three

compression tests and three moisture contents (7%, 12.6%, and 15.4%, w-b.). There was a

significant reduction in hardness of regular cubes when cube moisture was increased from

11.3% to 15.4%. TabiI and Sokhansanj (1995) found that alfalfa pellet hardness was

affected by moisture uptake. Hardness of low quality dehy alfalfa pellets did not

significantly decrease when the moisture content of the pellets increased from 6.5% to

896, but above a moisture content of 8% the hardness decreased. They found that the use

of a binder improved the hardness of pellets made from low quality alfalfa chops.

2.1.2 Nutritional attributes

Walton (1983) divided the chernical constituents of forages into two major

categories:

i) those existing in the cefl contents (e-g protein, sugar, pigments, and starch).

and.

ii) those which make up the structural components of the ce11 wall (e-g. cellulose,

hemicellulose, and lignin).

Chernical constituents contained in cells and those in the structural parts are

influenced by temperature and RH. Cnide protein (CP), beta-carotene, acid-detergent

fiber (ADF), and neutrai-detergent fiber (NDF) have been widely used to evaluate the

nutritional characteristics of alfalfa cubes (Racz 1994).

Beta-carotene content is used as a quality indicator in alfalfa cubes and pellets

(Baufernfeind, 198 1; Fasina, 1994 ). The rate of carotene degradation in dehydrated

alfalfa pellets was studied at various storage temperatures (Pulkinen, 1975). The rate of

carotene loss increased linearly to about 39% per month when storage temperature was

increased from O" to 22°C. Bruhn and Oliver (1978) reported that carotene content of hay

decreased from 58 to 18 mg/g during 18 weeks of outside storage in California. These

losses were due to exposure to sun curing.

Rotz and Abrams (1987) deterrnined quality changes of baled alfalfa hay durinz

storage. They found that NDF and lignin contents changed little after 30 days. Protein

content increased slightly dunng the first month of storage, and then subsequently

decreased. Collins et ai. (1987) studied initial moisture, storage method, and quality

losses in baled alfalfa during storage. They reported ADF concentration to increase

immediately after baling as initiai moisture content decreased, because of significant

effects on leaf loss during baling.

2.2 Quality Models

The quality of agricultural products has been modeled mathematically in two wqs:

(1) step change, and (2) gradua1 change, depending upon the type of quality

characteristics. Some quality factors such as visible mold c m be detected by the naked

eye after a period of storage. Le.: the change in quality has a "stepped" nature. Other

quality factors such as change in colour can be detected continuously.

2.2.1 Step change model

Muir and Sinha (1986) developed the following quality model for predicting safe

storage tirnes for canola when it is stored at a constant temperature (Tg) and moisture

content (M):

where d is storage tirne (days) before germination capacity drops by five percentage

points, Tg is seed temperature in OC, M is seed moisture content in % w.b., and a. b, c are

constants.

To account for dynarnic storage conditions, Sokhansanj et al. (1991) used a spoilage

index (SI) to predict the total loss of seed germination:

where At is the time interval at which temperature and moisture are constant and d is the

number of days at these constant temperature and moisture content before spoilage

occurs. The value of d is computed for each time interval from Eq. 2.1. When the sum of

(At/d)i is equd to or exceeds unity (SI 2 l), seed germination is reduced by a

predetermined value.

2.2.2 Gradua1 change model

Schreiber et al. (198 1) proposed an exponential model to predict the quality changes

of wheat during drying:

where Q is the quaIity characteristic (germination ratio G/G,, relative volume of bread

VN,, relative gluten F/F,), K is a constant, n is the order of the reaction and t is time.

Schreiber et al. (198 1) concluded that the value of 'n' was small and could be assumed to

be zero:

The constant K was found by taking the logarithm of K and expressing it as a linear

function of temperature (T) and moisture content (M):

where a, b, and K, are constants. PatiI and Sokhansanj (1994) modeled the change in

greenness of alfalfa leaf and stem sirnilar to the kinetic model given by Schreiber et al.

(198 1).

2.3 Experimental

Equations 2.1-2.5 show that quaiity parameters can be estimated using storage tests

at constant temperature and relative humidity. Field data (see Appendix B) showed that

temperatures in transit ranged from 8" to 40°C and relative hurnidity from 60% to 90%.

Cubes remained in transit up to 70 days. Experiments were designed to cover these ranges

of temperatures, relative humidities and times in transit.

Dehydrated dfalfa cubes were exposed to different arnbient conditions in two series

of expenments. In the first experiment, temperatures were set at 16", 24" and 3 1°C and

RH at 60% to 80% for 66 days. Since alfalfa cubes might remain in storage for longer

time and at temperature less than 16OC or above 31°C, a second experiment was

conducted at temperatures SO, 24", and 40°C and at relative humidities 6 1 % to 86% for

90 days. The repetition of 24°C test was used to check the repeatability of the

experiments. A complete replication of a test condition (temperature and RH) was not

done due to inadequate sarnpIe and time. Each temperature and relative humidity

expenment was conducted on two sets of identical samples. The test equipment and the

experimentai set up are outlined in the following sections.

2.3.1 Test chambers

Air-tight cylindrical chambers shown in Figure 2.1 were used to hold the cubes.

These charnbers were obtained frorn the laboratory of the Agricultural and Bioresource

Engineering. Each charnber was made of a section of PVC pipe. Acrylic plates were cut

and fitted with a rubber seal to provide the top and bottom for the test chamber. The

inside diarneter of the chamber was 19 cm and the height was 20.5 cm. Two round

shelves made of Polyethylene plates (6 mm thickness) were affixed to a rod and

suspended from the top cover. It was estimated that at least 42 cubes were needed in order

to measure durability and the other quality factors during each experiment. In preliminary

tests, about 42 cubes were placed in the test chamber. It was noted that the conditions

within the charnber changed drarnatically as the cubes absorbed moisture from the

environment. Therefore, it was decided to test the durability only at the end of the

experiment. The number of cubes were then reduced to 25 and it was found that the

h o l e for T & R H \ rneasurement

acrgl ic p l a t e

c u b e tray

c u b e

s u l f u r i c a c i d d i s h

Figure 2.1: Air-tight cylindrical controlled-hurnidity chamber containing alfalfa cubes and sulfuric acid solution.

hurnidity of the test chamber did not change significantly.

2.3.2 Control of relative humidity and temperature

A glass dish was placed at the bottom of the test chamber. About 500 ml of diluted

sulfuric acid was placed in the dish to create the desired RH. Table 2.1 lists the

concentrations of sulfuric acid required to provide a given equilibrium relative humidity

(ERH) levels at a fixed temperature (Hall, 1980).

Three controlled environment roorns (Conviron Plant Growth Chamber PGRIS.

ControlIed Environment Ltd., Winnipeg, MB) at the Phytotron facilities of the College of

Agriculture, University of Saskatchewan were used for each series of experiments. Each

room was set at 70% RH (+3%) and a temperature of 16"- 24"- or 32°C (MS°C) in the

first and go, 24". or 40°C in the second experiment. After a day of temperature

stabilization, eight test chambers were placed in each room (Fig. 2.2). Each pair of these

eight chambers had the same level of sulfuric acid concentration.

Temperature and RH within the charnbers were recorded periodically using a

Vaisala HM 34 humidity and temperature sensor (Vaisala Inc., Woburn, MA). A hole was

provided on the top of each chamber for the insertion of the humidity and temperature

sensor (Fig. 2.3). The hole was plugged with a rubber cork between readings. The

precision of the Vaisala for reiative humidity was specified by the manufacturer as &2%

over a range O to 90%- and the precision for temperature was +0.3"C. These calibrations

were checked against a General Eastern Hygro M2E2 (General Eastern Inc., Watertown.

MA) chilled rnirror dew point/hurnidity device at 90% and 55% RH. The unit showed a

negative error of 1.7% at 90% and a positive error of 0.7% at 55%. These deviations were

within the general accuracy specified or known for relative hurnidity sensors.

2.3.3 Sample preparation

The alfalfa cubes used for this study were obtained from a commercial producer in

Alberta (Tir01 International, Tiley, AB). The alfalfa cubes had been manufactured in Iate

summer 1994 from field harvested chops and dried in a rotary drum dryer. The cubes

were 24 mm x 25 mm (k1 mm) in cross section and 25 to 40 mm long. Cubes in the first

experiment had a moisture content of 10.3% and 12.7% moisture content in the second

Table 2.1: Concentrations (% on mass bases) of sulfuric acid at various temperature and RH (Hall, 1980).

Temperature, RH, % O C 60 65 70 75 80 85 90 8 38.3 34.9 3 1.5 28.1 24.7 21.3 17.9 16 38.4 35.0 31.7 28.3 24.9 21.5 18.1 24 38.6 35.2 31.8 28.4 25.0 21.6 18.2 32 38.7 35.3 31.9 28.6 25.2 21.8 18.4 40 38.9 35.5 32.1 28.7 25.3 21.9 18.5

Figure 2.2: Test chambers in the controlled environment room

Figure 2.3: Measurement of temperature and humidity of the air space inside a test chamber. Aifaifa cubes are seen through the transparent cover.

set of experiments. The cube samples were kept in a freezer at -20°C until required.

Before a test, the cubes were tempered to room temperature by placing them in the

laboratory overnight in plastic bags to prevent moisture loss or gain.

2.4 Quality Determinations

Colour, moisture content, density, hardness, durability and mass of the cubes were

measured prior to the experiments. Cubes on the top tray were observed daily through the

clear cover to detect the first incidence of mold on the sample. Cubes were removed from

the chamber six and seven times during the entire experiment. The number of cubes

removed each time were three in the first test (66 days) and 2 in the second test (90 days).

The cubes were tested for colour, moisture, density, and hardness (see below). After

completion of quality measurernents these cubes were discarded (were not returned to the

chambers).

The measurements were based on duplicate analysis for colour and with three

replicates for moisture and density. Hardness and density tests were in three replicates in

the first experiments and duplicates in the second experiments. Adequate sarnples were

not available for three replicate in the second set of experiments. Due to a limited number

of cubes in a container, the test for durability was perforrned, using seven cubes, before

and after a test. The m a s of the acid solution in each test charnber was checked and water

was added to bring it to its initial value.

2.4.1 Moisture content rneasurement

initial moisture content was deterrnined with three replicates by breaking each cube

into smaller pieces and drying them in a convection oven at 103OC for 24 h (ASAE.

1994a). Moisture content during storage was calculated based on changes in mass during

the entire test period.

2.4.2 Colour measurement

A Hunterlab colonmeter (spectrophotometer) was used to measure the colour of the

cubes. About 5 g of sample was ground and placed in a petri dish. The colour of the

sample was scanned in the visible range (400 to 700 nm) and expressed in L*a*b

coordinates (Fig. 2.4). 'L' designates darkness to brightness (O to 100), 'a' designates

greenness to redness (- 1 O0 to 100) and 'b' designates blueness to yellowness (- 100 to

100).

2.4.3 Mold identification

The type of mold appearing on a cube was identified by Dr. B. Gossen (Saskatoon

Research Centre, AAFC, Saskatoon). The cubes were exarnined using a dissecting

microscope and the diversity of fungi growing on the cubes was assessed based on the

size, shape and colour of the sporuIating structures. In addition, a number of single-spore

isolates of the fungi were made ont0 PDA (Potato Dextrose Agar) for subsequent

identification.

2.4-4 Density, hardness, and durability measurements

The density of the cubes was calculated from the mass of a cube and its volume.

The perpendicular dimensions of a cube were measured by a digital caliper to 0.01 mm

precision and the volume was calculated by multiplying the cross sectionai area by the

length of the cube. The mass was measured on a digital scale to 0.01g precision.

Hardness was measured by a compression test on an Instron Universal Testing

Machine (Mode1 10 1 1, Instron Corporation, Canton, MA). An 8 mm diameter cylindrical

probe with flat end was forced into the die side of the cube. The cross head speed kvas

maintained constant at 20 d r n i n . The rupture force in kN was taken as the hardness of

a cube. The method is a modification of ASAE Standard S368.2 (ASAE, 1994b).

Durability measurement was based on AS AE Standard S269.4 (AS AE, 1994~) .

Seven cubes were tumbled in a wire mesh cage for 3 min at 40 r/min. The pieces that

remained in the cage were weighed. Durability was expressed as the ratio of mass of

cubes left in the cage over the initial mass of cubes.

2.4.5 Chernical composition

Protein, beta-carotene, neutral-detergent fiber (NDF) , and acid-detergent fiber

(ADF) were measured for the second series of experiments. Standard laboratory

Figure 2 -4 Color coordinate values of the Hunterlab spectrophotometer (L*a*b).

procedures were used to determine the crude protein and beta-carotene content of the

cube samples (AOAC. 1984). The percentage of cmde protein was determined by

analyzing the cube nitrogen (N). Crude protein was calculated by multiplying nitrogen

content by 6.25. The nitrogen determination was by Kjeldahl method (Hoveland 1980).

ADF and NDF were deterrnined by the rnethods of Georing and Van Soest (AOAC,

1984). Al1 of the chernical analyses were done by the staff of the Saskatchewan Plains

Innovative Laboratory Services in Saskatoon.

CHAPTER THREE

EXPERIMENTAL REXULTS

Chapter 2 described experiments for collecting data on the physical quality of

aifalfa cubes at constant temperatures and relative humidities. The results are presented

and discussed in this chapter. The data are used in developing mathematical

representations of qudity factors with respect to storage temperature, reIative humidity

and time. FinalIy the spoilage mode1 is tested against real conditions from alfalfa cube

s hipments.

3.1 Experimental Data

The raw data for colour, moisture, hardness, density, and molding cubes for both

experiments is listed in Appendix A, Tables A.1-A.6. The first digit of the chamber

number in Tables A.1-A.6 indicates similar humidity conditions in the test charnber and

the second digit indicates the duplicate. For example. test chamber designated as 1.1 and

1.2 are duplicated test at the temperature of 16.3"C and the RH level of 76%.

Tables A.7-A.8 list temperatures and relative hurnidities in each chamber over the

same period. Tables 3.1 and 3.2 show the average and standard deviation of temperature

and RH inside the chambers. It was difficult to maintain constant RH during the entire

experiment, especially durhg the initial stages when cubes were placed in the test

charnber. The cubes absorbed or desorbed moisture depending upon the RH and thus

increased or decreased the RH of the headspace in the test charnber. The RH in the

headspace becarne stable in about 25 days when the storage temperature was low and RH

was high. The variations in RH and temperature of the air space were as much as 24-88

and M.4"C, respectively.

Table 3.1: Average relative humidity (96) of test chambers for 60 days of srorage. n = 13.

Test RH at 16.3OC Test RH at 24.1 OC Test RH at 3 1.3"C charnber avg. sdv. chamber avg. sdv. charnber avg. sdv.

1 . 1 76 3 5.1 75 4 9.1 80 3

RH = relative humidity avg. = average sdv. = standard deviation n = number of data

Table 3.2: Average relative humidity (96) of test chambers for 90 days of storage, n = 2 1.

Test RH at 83°C Test RH at 24.1°C Test RH at 39.1°C chamber avg. sdv. chamber avg. sdv. chamber avg. sdv. 13.1 76 4 17.1 80 3 21.1 86 2

RH = relative humidity avg. = average sdv. = standard deviation n = number of data

3.2 Mode1 Development

A number of models expressing quality factors as function of variables were

presented in Chapter 2. Most of these models are exponential in tenns of time. The

reaction terms were expressed as a linear function of temperature and moisture (relative

hurnidity). These could also be expressed including an interaction terms of temperature

and relative humidity which has not been used by previous researchers (Schreiber, 198 1 ;

Muir and Sinha, 1986). Nevertheless it was decided to test the significance of not

including the interaction terms in the model. SAS program was used for this purpose and

it was found that at a = 0.05, the interaction terms was not significant.

3.2.1 Moisture content

Data on the moisture contents of the cubes are summarized in Figs 3.1 and 3.2.

Typically, the moisture content increased from an initial value of 10.3% to anywhere

from 12.5% for test charnber 8.2 (24.0°C, 60% RH) to 22.5% for test chamber 5.2

(24.0°C, 76% RH) or decreased from an initial value of 12.7% to 10.7% for test chamber

20.2 (24.3"C, 63% RH). Cubes absorbed more moisture from the humid environment and

at low temperature as compared to high temperature. Also, the moisture absorption

increased as RH increased from 60% to 85%. Al1 of the cubes in the first test (Fit. 3.1 )

showed an increase in moisture content with time. Cubes in 2"* test showed a decrease at

60% RH (Fig. 3.2).

Figure 3.3 is a plot of RH vs. final moisture content at the end of tests (66 and 90

days). The data do not show an isotherm typical of equilibrium moisture content-relative

hurnidity for agicultural products. We may deduce that the moisture penetration into the

cube was not complete for these sarnples, though Figures 3.1 and 3.3 show that the

moisture absorption by the cubes approached an asymptote value.

The following exponential model was fitted to the moisture absorption data (Figures

3.1 and 3.2):

O 1 O 20 30 40 50 60 70 80 90 1 O0

Storage time, d Figure 3.1 Cube iiioisture content diiring 66 days of siorüge (initiiil moistiire 10.3% w.b.).

50 55 60 65 70 75 80 85 90

Relative humidity, %

Figure 3.3: Final moisture content of cubes for given storage temperature and relative humidity.

where m, mi, and rnf are instantaneous, initial, and final moisture content in % ci-b.. k,,, is

moisture absorption rate constant in h-', and t h is time in h.

km and rnr were estimated using the non-linear estimation procedure (NLIN) in SAS

package (SAS, 1986). The estimations are listed in Table 3.3. The coefficient of

determination (R') of the estimated parameters (km and mf) for each temperature and RH

combination was greater than 0.98, while the standard error of estimate of fit was less

than 2.6. The estirnated km and rnf varied with temperature and RH. The linear estimate of

rnf was obtained with temperature and RH as:

where T is OC and RH is in %. The constant km was found to be dependent on

temperature. It was expressed by:

Table 3.3: Values of km and mf obtained from non-linear regression analysis.

Temperature, RH, km, mf, Standard Error of

OC % h-' %, d.b. Estimate

Equations 3.2 and 3.3 shouId be used with caution and only within the tested ranges of 9"

to 39°C temperature and 61% to 85% RH.

3.2.2 Colour degradation.

Figures 3.4 and 3.5 sumrnarized green colour variations during 66 and 90 days of

storage, respectively. The data points in these Figures are the average of green colour of

the cubes sarnple of two test chambers with the same level of RH. The green colour

('a' vaiue) increased (less greenness) as temperature and RH increased. At 24°C the 'a'

value increased from -1.6 to 0.5 by increasing RH from 60% to 75% after 66 days of

storage (Table A.2). Also at the end of storage the 'a' value changed from -0.7 (test

chamber 1. i ) to 1.1 (test chamber 10.1) by increasing temperature from 16.2" to 3 1.4"C.

(RH= 75%). A sirnilar result was obtained after 90 days of storage in which the 'a' value

increased from -1.5 to 0.8 by increasing the RH from 63% to 80% at 24.3"C (TabIe AS) .

The 'a' value increased from -1.8 to 2.3 by increasing temperature from 8.8" to

39. 1°C(Tables A.4 and A.6), indicating a lesser green colour.

The colour coordinates 'L' (Iightness) and 'b' (yellowness) were almost constant over

time, but at higher temperature and RH, 'L' and 'b' values decreased at the end of the

storage ('L' decreased from 54.6 to 41.7; 'b' decreased from 27.3 to 16.8 at 39.1 OC and

86% RH).

Cube coiour degradation, especially green colour ('a'), was more severe at higher

temperatures and relative hurnidities than at cool storage conditions. Since the

experiments were carried out in a dark chamber, the colour changes were affected by the

temperature and RH only. Degradation was observed both on the surface and inside of the

cubes.

The kinetic reaction model (Eq. 2.5) was used to model the change in green colour

index:

O 1 O 20 30 40 50 60 70 80 90 1 O0

Storage time, d

Figure 3.4 Cube green color ('il' value) diiring 66 düys of storagc.

O 1 O 20 30 40 50 60 70 80 90 1 O0

Storage tirne, d

Figiirc 3.5 Cube grcen color ('ii' viiluc) cluriiig 90 days of storiige.

where a is the instantaneous and ai is the initial colour coordinate 'a'. kz is a constant, and

t is time in days. Table 3.4 gives k2 value at several temperatures and relative humidities.

Equarion 3.5 was found to describe k2 as a function of temperature (T, OC) and RH (470).

Standard errors of estimated parameters are included in the equation:

where a;! = -0.129 (s.e.=û.0297), b2 = 0.00241 (s.e.=û.OOO3 l), and cl = 0.00165

(s.e.=0.00044). Equation 3.5 can be used for a temperature range of 9°C to 39°C and

relative humidities of 61 to 85%. Figure 3.6 shows a typical plot of the exponential colour

model at 3 1°C- The colour loss was faster at high RH (8 1 %) than at low RH (62%).

3.2-3 Mold free days

Tables 3.5 and 3.6 list the number of days that the cube surface was mold free,

based on visual inspection. Cube moisture contents at the end of storage or at the time of

mold appearance are included in the tables. Mold growth occurred above 17.1 % moisture

content at low temperature (16°C) and above 11% moisture content at high temperature

(39"). These moisture content levels were attained at 71% and 63% RH. Mold developed

faster on cubes stored at 39°C and 86% RH. Mold was not detected visually on cubes

stored at 8.8"C for aii RH values.

Figures 3.7 and 3.8 display pictures of cube surface conditions after 66 days for the

first expenment at 24" and 3 1°C and 90 days for the second experiment at the level of 9"

and 39°C. The green colour detenoration of moldy cubes was higher than mold free

sarnples (Tables A.1-A.6). A blue-green colored species of Aspergillus was the

predominant hingus growing on the cubes under al1 the temperature and humidity

treatments. The Aspergillus species was identified as A. penicillioides Speg. by Dr. K.

Seifert of W C , Ottawa Research Centre. One other species, Eurotiwn Amstelodami

Margin, was also isolated from these sarnples.

The following model was fitted to the number of mold free days (d) as a function of

temperature and RH:

Table 3.4: The reaction constant kz for colour index model.

Temperature, OC RH, % kl R " Std. Error of Estimate 8.8 76 0.0 12 0.98 0.888

O 10 20 3 0 40 5 O 6 0 70

Storage time, d

Figure 3.6: Experirnental data and model (Eg. 3.4) fitted to the data representing green colour ratio (dai) at 3 1 OC storage.

Table 3.5 Mold free days and moisture content before and after 66

days of storage or when mold started.

Temperature OC

- --

Number of mold free days

8 8 17 16

49* 49*

no mold no mold

18 19 34 34

no mold no mold no mold no mold

41 41

no mo1d no moId no moId no mold no mold no mold

Final m.c. % (w.b.)

initia1 moisture content = 10.3%, w.b. * = slightly molded

Table 3.6 Mold free days and moisture content before and after

90 days of storage, or when mold started.

Nurnber of mold free days

7 7 15 15

no mold no mold no mold no mold

17 15 3 1 32

no mold no mold no mold no mold

Temperature OC

39.1

24.1

8.8

no mold no mold no mold no mold no rnold no rnold no rnold no mold

R H % 86 86 8 1 8 1 74 74 63 63 80 82 77 77 7 1 71 64 63 76 76 72 72 66 66 61 61

Final m.c. 9% (w-b.)

21.5 21.7 19.6 19.7 14.5 14.4 10.7 11.0 21.2 20.5 18.2 18.4 14.6 14.7 10.6 10.8

initial moisture content = 13.796, w.b.

Figure 3.7: Cube surface conditions at the end of 66 days of storage (T=24" and 3 1°C).

Figure 3.8: Cube surface conditions at the end of 90 days of storage (T=gO and 39°C).

where a = 4.84 (s.e.= 0.43), b = 0.01 (se.= 0.01)' and c = 0.04 (s.e.= 0.01). Equation 3.6

is valid for tested ranges of 16"-39°C in temperature and 70% to 85% in RH up to 90

days.

3.2.4 Density, hardness, and durability

Density of the cubes showed a slight reduction during storage (Figures 3.9 and

3.10). Both heat and hurnidity contributed to the Ioss of density, though the effect of

humidity was higher than the effect of temperature. For exarnple, density decreased from

an initial value of 0.81 ~ g / r n ' to 0.54 ~ ~ / r n ) at 16°C and 76% RH during the 66 days of

storage; the density values at the same temperature but 58% RH decreased from 0.8 I

~ ~ / r n ' to 0.76 ~ g / m ' (Figure 3.8). Density of cubes stored at 39°C decreased from 0.73

~ g / r n ' to 0.47 ~ ~ / r n ' at the RH of 86% at the end of storage (Figure 3.9). At the lower

temperature (8.8"C) and RH (61%), the cube density was almost constant near 0.73

~ ~ / r n ' .

Density ratio was correlated with the tirne of storage 't' (days) using the

polynornial:

where p and pi are instantaneous and initial density in ~ g / r n ~ , t is storage time in days.

and a, b and c are constants. The constants describing the density ratio at different

conditions are given in Table 3.7. The typical variation in density ratio with storage time

is shown in Figure 3.1 1.

Figure 3.12 shows the values of hardness of the cubes measured during and after

storage for the 66 day tests. The variation of hardness was as much as H.27kN. Although

there was a definite decrease in hardness at high RH condition, there was no reduction in

hardness for samples stored at lower RH. For instance at 24°C and 75% RH the hardness

O 1 O 20 30 40 50 60 70 80 90 1 O0

Storage time, d

Figure 3.9 Alfiilfa cube density duriiig 66 days of siorage (initial density 0.8 1 ~ ~ / i i i " ) .

O 1 O 20 30 40 50 60 70 80 90 1 O0

Storage tirne, d

Figure 3.10 Alfiilfil cube density tliiring 90 dnys of siorage (initial density 0.73 M ~ I I ~ ~ ) .

Table 3.7: The constants of density ratio(p/pi) equation.

Temperature, OC RH,% a b c R~ 8.8 76 -2 . lx l0 -~ 1 . 3 ~ 1 0 - ~ 9 . 7 ~ 1 0 - 1 0.90

0.5 1 I

O 10 2 0 3 O 4 0 5 0 6 O 7 0

Storage time, days

Figure 3.1 1: Experimental data and mode1 (Eq. 3.7) fitted to experimental data of density ratio (p/pi)of alfdfa cubes at 24°C storage.

O 1 O 20 30 40 50 60 70 80 90 100

Storage time, d

Figiirc 3.12 Alfiilfa cube Iinrdiiess during 66 diiys of siorage (initial tiiirdness 0.34 kN).

values decreased frorn initial value of about 0.34 Id4 to less than 0.04 kN in 66 days

(Figure 3.12). The hardness of the samples stored at the same temperature but at 60% RH

increased from 0.21 at the 3rd day to 0.25 at the 66th day of storage. For most of the

cubes there was an initial reduction in hardness. Hardness remained constant at 8.S°C and

6 1 % RH dunng the 90 days test.

The 90 day tests show large fluctuation in the data and the following logarithmic

mode1 was fitted to the hardness ratio of the 66 day tests only:

where H and Hi are instantaneous and initial hardness in kN, t is storage time in days, and

k3 and are coefficients. The coefficients were found by linear regression and the results

are listed in Table 3.8. The coefficients k3 and ki were found to be dependent upon

temperature and RH:

where a3 = -0.142 (s.e.= 0.0 I3), b3 = 0.000029 (s.e.= 0.0002), c3 = 0.0028 (s.e.= 0.0002).

= 4.132 (s.e.= 0.131), b j = 0.0041 (s.e.= 0.002), and c4 = 0.031 (s.e.= 0.002). Figure

3.13 shows typical hardness ratio at 3 1 "C.

Table 3.9 shows cube durability initially and at the end of the 66 days test and 90

days test. At 60% RH. the durability of the cube samples was almost at its initial value.

For example, at 24OC and 66 days of storage. durability initially of 89.7% decreased to

87.6% at 60% RH and to 56.5% at 76% RH. Durability of cubes at 39°C during 90 days

storage decreased from 88.4% to 87.4% at 63% RH and frorn 88.4% to 6.8% at 86% RH.

Cube durability declined significantly when stored at high RH. Therefore humidity has a

larger effect on durability than temperature has. When cubes were exposed to high RH

(70435%). they absorbed moisture and expanded which caused a reduction in durabil ity.

Table 3.8: The coefficients of hardness ratio(Wi)during 66 days storage.

Temperature, OC RH,% kr k3 R' 16.3 76 0.073 1 -709 0.92

7 1 0.063 1.8 14 0.94 66 0.039 2.03 1 0.69 58 0.020 2.298 0.7 1

24.1 75 0.073 1.686 0.84 72 0.067 1.826 0.7 1 66 0.047 1 -922 0.73 60 0.024 2.206 0.59

31.3 8 1 0.078 1.57 1 0.97 76 0.070 1 -652 0.94 69 0.057 1 -800 0.8 1 62 0.03 3 2.098 0.8 1

O 10 20 30 40 50 60 70

Storage tirne, days

Figure 3.13: Expenmental data and mode1 (Eq. 3.8) fitted to experimental data of hardness ratio (Hmi) at 3 1 OC.

Table 3.9: Durability of alfalfa cubes after 66 and 90 days of storage.

after 66 days after 90 days

T, OC RH, % DUf, % T, OC RH, % DUr, %

80 49.3 86 6.8

77 55.5 76 6 1.6 7 1 43.6 72 82.5

16.3 72 76.3 8 -8 72 87.3 66 77.2 66 88.4 65 82.0 66 85.8 59 88.2 6 1 89.1 58 85.9 61 89.6

Note: initial durability was 89.7% and 88.4% for the first and the second experiment, respectively .

hardness, and density.

3.3 Repeatabiiity of test at 24OC

At 24"C, the relative humidity obtained within the test chambers in the first

experiment (66 days) was about 5% lower than that in the second experirnent (90 days).

The initial moisture content of the cube samples in the second experiment was higher

(12.7%) than the initiai moisture content of cubes in the first experiment (10.3%). As

Tables A2 and A5 show the test charnbers 5.1 and 17.1 had the sarne concentration of

sulfuric acid but RH values were 75% and 80%, respectively.

At the same level of RH, cubes in the first experiment had fewer mold free days

than cubes in the second experiment. Tables 3.5 and 3.6 in the first experiment, mold was

detected after 18 days at 75% RH, but in the second experiment, mold was detected after

31 days at 77% RH. The reason for this difference might be several: (i) initial infestation

of the first set of the cubes was higher than the second set; (ii) the test chambers in the

second set were opened for cube removal more often than in the first. It was noticed that

by removing the cover plate, the RH within the charnber dropped. Green color, moisture

and density were almost the same after 60 days in both expenments.

3.4 Nutritional Tests

Nutritional characteristics of the second experiment of samples were measured.

Table 3.10 shows the nutritional value of the cube samples at 0, 35 and 90 days storage.

The acid-detergent fiber (ADF) content of the samples was measured initially and at the

end of storage. Beta-carotene content of the cube samples decreased significantly during

storage, and reduction was more rapid at high temperature. For instance, at 39°C and 86%

RH, beta-carotene decreased frorn 68.3 mgkg to 14.3 mgkg after 90 days. The

corresponding values at the sarne temperature but at 63% RH were from 68.3 mglkg to

13.8 mg/kg.

The protein content increased 2% to 3% at high RH storage conditions (75 to 85%

RH). It seems nitrogen (Nx6.25= protein) increased as the rnoisture content in the sample

Table 3.10: Nutritional value (96, dry b a i s ) after 90 days of storage.

--

T RH Protein, % B-carot., mgkg N.D.F, % A.D.F.,%

initial protein = 23.7% initial N.D.F. = 38.8% initial p-carot. = 68.3 mgkg initial A.D.F. = 29.3% shaded = molded sampIe d = days of storage

increased. This is probably the result of increased microbial activity at higher moisture

content, resulting in greater breakdown of carbon sources, but retention of protein in the

microbial biomass. Neutral-detergent fiber (NDF) and acid-detergent fîber (ADF)

increased slightly after 35 days and 90 days of storage, especially for the rnoldy samples.

NDF of moIdy cube sarnples at 38.7"C and 86% RH, increased from 38.8% to 57.6%

after 90 days storage. ADF of the sarne sarnples increased from 29.3% to 33.9% at the

end of storage. The digestible and soluble fractions of alfalfa cube compositions were

used by mold microorganism, therefore the concentration of NDF and ADF increased as

the other constituents were lost.

3.5 Quality of Alfalfa Cubes Shipment

To study the environmental factors affecting the quaiity of alfalfa cubes during

overseas shipments, the condition of cubes loaded in containers was monitored during

several shipments from a plant in Alberta to Taiwan in 1993 and 1994. This project was

initiated by the Canadian Dehydrators Association and was headed by Dr. En-Zen Jan of

Agriculture and Agri-Food Canada in collaboration with the University of Saskatchewan

(Dr. Sokfiansanj). Tir01 International Inc. of Alberta supplied cubes and containers and

shipping arrangements. The author (M. H. Khoshtaghaza) analyzed the raw data and

contributed to the preparation of a technical report (Sokhansanj et al. 1996). The technical

report is presented in Appendix B.

It was demonstrated that temperature variations within a container and humidity

accumulation were dependent upon the portion of the transit route. To test the hypothesis

that transit duration in Canada, on the ocean and in Taiwan had a signifiant effect on

cubes quality, the following quality analysis was conducted.

At each time interval, cube surface temperature and the RH of the space above the

cube were used to calculate the potential for mold growth. Visible mold growth was

modeled with a step change in quality (Eq. 2.2). Theoretically a cube spoils (mold is

visible on the cube surface) when the spoilage index is equal or exceeds unity (SI 2 1).

The spoilage index calculated in this work considered only the RH and temperature

of the air and did not take into account condensate on the cube surface. Figure 3.14 shows

/ Canada Ocean

Sumrner 1993

Wmter 1994

Visible mold

Taiwan i

Figure 3.14: Typical spoilage index versus transit time for actual shipments during the s u m e r of 1993 and the winter of 1994.

a plot of spoilage index versus days for two shipments, one in Surnrner 93 and one during

Winter 94. The spoilage index increased gradually but became rapid when conditions for

mold growth became favorable. The rate of mold growth was much higher during the

warm season than during the cold season. According to Figure 3.14, the greatest increase

in potential mold growth occurred in Taiwan where temperature and RH conditions were

favorable to moId growth and the spoilage index was already high.

Table 3.1 1 lists days in transit and spoilage index for each shipment. In a11 cases

where the model showed potential conditions for mold growth, mold occurrence was

inevitable as it was observed on the cubes. In one case, the potential mode1 showed mold

but surface mold was not reported. This could be attributed to the uncertainty in a proper

inspection or inherent uncertainties in the model.

Table 3.11: Days in transit and spoilage index (SI) at stages of shipment.

Container Season Visual Loading Canada Ocean Taiwan

Number Inspection SI* Days SI Days SI Days SI

1.1 Sp 93 good - - - - - - - 1.2 Sp 93 good O 8 0.13 18 0.62 6 0.76

1.3 Sp93 good O 8 0.01 18 0.02 7 0.03

1.4 Sp 93 good O 8 0.12 18 0.47 7 0.55 - - - - - - -- -- -

2.1 Su 93 moldy - - - - - - - 2.2 Su 93 moldy - - - - - - - 2.3 Su 93 moldy O 7 0.20 20 LOO* 13 1-20"

2-4 Su 93 moldy - - - - - - -

3.1 Fa 93 good O 8 0.28 18 0.66 10 0.76

3.2 Fa 93 good - - - - - - -

3-3 Fa93 good - - - - - - -

3.4 Fa93 good O 8 0.18 18 0.44 10 0.52

4.1 Wi 94 good O 6 0.08 19 0.49 3 0.55

4.4 Wi 94 good O 6 0.05 19 0.26 3 0.29

5.1 Su 94 good O 8 0.15 12 0.52 26 0.84

5.2 Su94 good O 8 0.10 12 0.24 34 I.68*

5.3 Su 94 moldy O 8 0.14 12 0.30 - -

5.4 Su 94 moidy O 8 0.11 12 0.31 42 2.49*

'Sp' spring 'SU' summer 'Fa' fa11 'Wi' winter '*' SI2 1 mold growth has occurred '-' data not available

CHAPTER FOUR

THERMAL CHARACTRISTICS OF CONTAINERIZED

ALFALFA CUBES

4.1 Introduction

Containerïzed bulk cubes undergo extreme temperature variations during transport

from Canada to overseas destinations. These temperature variations were recorded during

actual transport. Appendix B presents these data. In this chapter the thermal environment

and heat transfer within the cube container will be presented rnathematically. The mode1

will be used to investigate the methods of preventing cube shipments from large

variations in temperature.

4.2 Mode1 Development

There have been severai studies conducted to predict the temperature distribution of

cereai grains in storage bins. The models developed were either based on conduction or

convection heat transfer equation or a combination of both. Yaciuk et al. (1975) showed

that the effects of natural convection on temperature distribution in stored grain is

minimal and the thermal environment in a free standing grain bin c m be modeled with

the conduction equation done. Similarly, Smith and Sokhansanj (1989) reported that the

temperature distribution in a bin containing srnall cereal grain like wheat is mainly

dorninated by conduction. However, for large particles like potatoes the effect of

convection was important. The average temperature of the stored material is not very

much affected by respiration and the slow moisture transfer occumng during storage.

Beukema et al. (1983) also developed a temperature distribution mode1 for a porous

medium in a closed container with natural convection and conduction. They reported that

natural convection accelerated the cooling of the material and the predicted average

temperatures were lower than with pure conduction. With natural convection, the location

of maximum temperature shifted from the center of container upwards. A two-

dimensional time-dependent model had been developed for grain by Nguyen (1987). The

model used both convection and conduction heat transfer. Using of Stream Iines,

isothems, velocity vectors and grain moisture content, he was able to show that the

circulation of air in the grain bulk was a result of strong convection flows in the

headspace. He concluded that the effect of naturai convection on the temperature

distribution is significant for small grains stored in the containers with headspace.

Lo et al. (1975) used a decoupled heat and mass transfer model to study the

temperature change within a closed cylindrical storage bin due to climatic temperature

variations. They found that the grain moisture content changed due to changes in

temperature. Lo et al. (1975) tested the grain storage conditions by comparing

temperature and moisture content with "safe critena" as were suggested by Burges and

Burrel (1964). They concluded that the risk of damage to grain was highly dependent

upon the initial temperature and moisture content of the grain. The darnage could be

reduced by having good insulation of the storage structure provided the initiai conditions

of grain were favorable. Their analysis showed that the grain was most susceptible to

damage in locations near the surface of the grain where the standard deviation of

temperature was the largest.

In this study, shipping container with cubes are exposed to direct sunshine dunng

transit. The Sun heats up the roof resulting in an increased radiation between surface of

cube and the ceiling of the container. The data in Appendix B shows that for the majority

of the cube shipments, the headspace temperature was greater than the temperature of the

cubes and the ceiling temperature in many cases increased to more than 40°C during the

day . Figure 4.1 shows a schematic diagram of alfalfa cubes and the prevailing

temperature and heat fluxes within the container. The typical container dimensions are

2.3 m high, 2.3 m wide, and 12.2 m long. A mobile conveyor is used to load cubes into

the container to fil1 the container not more than 75% (1.7 m high) of the container

Figure 4.1: Heat transfer inside aifalfa cube container. q, is radiation heat transfer between container ceiling and cube surface. qh is the convective heat transfer between the headspace and cube surface. Tc, Ta, Ts (input data), and Tp are ceiling, headspace, surface and pile temperature, respectively. Drawing is not to scale.

volume.

The container with its contents is a three dimensional system and as such has to be

modeled and analyzed. In this study a one dimensional case will be developed. Since the

container is 12.2 m by length and 2.3 m in wide, the edge effect on the two ends of the

container c m be neglected. The container is 2.3 wide and thus neglecting the edge effect

on these two sides of container could effect the temperature and moisture profiles within

the cube pile. Zn this present anaiysis however, we assume that temperature and moisture

gradient are only in vertical direction. This simplification could also be supported by the

fact that the largest temperature gradients are between the roof and the cube surface. It is

also expected that heat and mass flow will take place primarily between the top cube and

few centimeters into the cube pile. To wnte the goveming equations the following

assumptions are made for the boundary conditions:

(a) The mechanism of heat transfer from and to the cubes is by radiation (q,) and

convection (qh) between headspace and the top boundary of cubes.

(b) The rate of moisture transfer within the cube pile and between air and cubes is

slower than the rate of heat transfer. The thermal diffusivity (a = Wpc) for the

bulk cube is about 4x10-~ m2/s whereas the rnoisture diffusivity of cubes is

8x10"~ m% (Fasina. 1994).

A heat balance through a small volume of cube within the pile is written as:

where p b and cb are density and heat capacity of bulk cube. respectively. Equation 4.1 can

be written for a point as:

where

where p, and c, are density and specific heat of air, u is intergranular air velocity and kb is

buik thermal conductivity. The air movement in this analysis is due to natural convection

dT only. upac,T represents the bulk flow of heat caused by air movement. The term kb-

dx

represents the conduction heat transfer. Substitution Eqs. 4.3 into 4.2 results in:

Equation 4.4 descnbes the heat flow within the cube pile. At the boundary, three

situations can be specified:

1) Surface temperature of the cube is known:

2) The heat transfer is by convection oniy:

3) The heat transfer is by a combination of convection and radiation. The potential for

radiation is the ceiling (roof) temperature and the cube surface temperature.

aT - k, axI = MT, - T, ) + ea[(T, + 273).' - (T, + 273)']

where Ts is the cube surface temperature in OC, T, is inside the container air temperature

in OC, Tc is the ceiling temperature in OC , E is the net emissivity or absorptivity of the

alfalfa cube and the ceiling surfaces, (3 is the Stefan-Boltzman constant, 5.670 x 1 0 - ~

W . m-' . K4.

4.3 Numerical Solution

The governing heat transfer Eq. 4.4 is recast into a finite difference equation by

employing fonvard difference approximations for time derivatives and central difference

approximations for the spatial derivative (FTCS) (Riggs, 1988):

where i is space and n is time indices. The constants D and C are defined as:

kb where a, = - P A is bulk thermal diffusivity and y = - is the ratio of thermal

f'bCb PbCb

capacity of air to cube. The following stability criteria control the size of At and Ax:

Re, = 7 4

where Re, is cailed the ce11 Reynold's number (Riggs 1988).

The initiai condition for this probIem was T(x, O)= Ti. The two boundary conditions

were the one at x = O which was one of the conditions of 4.5, 4.6 or 4.7 and the second

one which was the common boundary condition at x = H:

H is the depth of the bulk in the container. The fully backward one-side finite-difference

approach is used for boundary condition 4.13 (Riggs, 1988):

For the convection boundary condition, Eq. 4.6 at the top of the cube, the fonvard

two point one-side finite difference approach is applied:

For the convection and radiation boundary condition, Eq. 4.7, the following

numerical expansion is used:

The method of bisection was used to calculate the boundary surface ternperature.

The finite difference equations were coded in FORTRAN. To estimate u and E. the

sum of squares of residuals (the difference between calculated temperature and

experimental temperature), SSR, was calculated. The optimum u and E were the values

when SSR was at its minimum:

A

where t is time, Tp and T, are measurernent and calculated temperatures at 60 cm below

the surface (Fig. 4.1).

4.3.1 Evaluation of constants

For numerical calculations, the following constant values were needed to be

specified: specific heat of the air in bulk cube c,=1007 J . kg-' . K-' (incropera and Dewitt.

1990). cube bulk density pb= 475 kg/m3 (Sokhansanj et al.. 1993). The following

equations were used to calculate k b and cb:

where f = 0.45 is the cube porosity (Sokhansanj et al., 1993), cc= 1490 J . kg-' . K-1 is the

cube specific heat (Fasina, 1994), ka= 0.41 W. m-' . K-' is the air thermal conductivity

(Incropera and Dewitt, 1990), and k, = 0.41 W. m-' . K" is the cube thermal conductivity

(Khoshtaghaza et al., 1995).

4.3.2 Convective heat transfer coefficient

The convective heat transfer coefficient was estimated h m empirical correlations

developed for rectangular enclosures (Incropera and Dewitt, 1990). When the cube

surface temperature (T,) was higher than the ceiling temperature (T,), the heat transfer

coefficient was calculated from the correlation proposed by Globe and Dropkin (1959) as

given by Incropera and Dewitt (1990):

where RaL is the Rayleigh number, w is the width of the container in m, g is gravitational

acceleration 9.8 1 m/s2, P is the expansion coefficient of air (lm), and a and v are the air

thermal diffusivity in m2/s and kinematic viscosity in m2/s, respectively. Pr is the prandl

number (v la) . Al1 of the properties were evaluated at the average temperature,

T = (Tc + T,) 1 2 . The following correlations in the range of 200K 5 TI 350K were

developed frorn the data in Incropera and Dewitt (1990) for dry air:

When Tc > Ts, the heat transfer fro~n the ceiling to the cube surface is exclusively by

k a conduction (NuL = 1) and thus the convection coefficient can be estimated from h = - W

(hcropera and Dewitt, 1990).

4.3.3 Evaluation of the numerical solution

The numerical solution was checked against an analytical solution available for a

simple geometry. The heat equation for transient condition in a semi-infinite solid at

constant surface temperature boundary condition is given by (Incropera and Dewitt,

1990):

where erf is the Gaussian error function. T, and T, are the initial and surface temperature

in OC, and t is time in S. The following problem was solved by the analytical method (Eq.

4.27) and the numericd method (Eq. 4.8) with a constant surface temperature boundary

condition. This problem is taken from Incropera and Dewitt (1990 page 262):

What minimum burial depth (X,) would you recomrnended to avoid

freezing of the water main (Fig. 4.2) under conditions for which soil,

initiaily at a uniform temperature of 20°C. is subjected to a constant

surface temperature of - 15°C for 60 days?

Table 4.1 shows the temperature of the soi1 at different depths using Eqs. 4.27 and 4.8

and the sum of squares of the difference between these two equations. X, was found to

be at 68 cm for the analytical solution and 71 cm for the numerical solution. Table 4.1

shows that the numerical method proposed in this chapter caiculated the soil temperature

to within 0S0C of the analytical solution.

Soil

Ti = 20°C

I Water main

Figure 4.2: Example problem for a semi-infinite solution of heat equations. This problem was used to compare the numencal solution with the analytical solution.

Table 4.1: Soil temperature, O C , at different depth by analytical (Eq. 5.27) and numerical methods (Eq. 5.8).

Methods Depth of soil, cm O 10 20 40 68 71

Analytical - 15 - 12.6 - 10.3 -5.7 O 0.7

SSR O 0.30 0.03 O. 12 O. 12 0.43

SSR = Sum of square of residuals

4.4 Results and Discussion

Equation 4.4 was solved for three boundary conditions using the experimental data

obtained from the tests outlined in Appendix B as input. The data were selected from the

containers located on deck-starboard side during sumrner and winter (Containers

MOLU2086380 and GSTU763369 1 in Table B. 1 ). Recorded temperatures on the surface

of the cubes and those at 60 cm deep in the pile were compared to the temperature values

cornputed for these locations. Table 4.2 lists the a b and u values estimated for the case

where the surface temperature was specified for January 1994 data. ab = 4.08 x 10'%nm'/s

was calculated directly from known values of kb, p b and cb. The resulting value was

multiplied by factors 10, 20, or 30 and likewise the vales of u were set as 2 x IO-^. 2 x 10-~, 2 x IO-', or 2 x 10-~ rnls. The sum of square of residuals (SSR) were cornputed

for each combination of these parameter. Table 4.2 shows that the minimum SSR was at

ab = 8.16 x 10.' m2/s and u = 2 x 1 0 - ~ m/s.

Similar procedures were used to calculate temperatures at the surface and

temperature at 60 cm below surface for the case of convective boundary condition. Table

4.3 lists the SSR values for a b and u. For these tests the heat transfer coefficient (h) was

calculated from Eq 4.22. The least SSR value of 38 10 was obtained when a b = 4.08 x 10"

rn2/s, u = 2 x IO-^ mis.

Table 4.4 gives the optimum values of ab, u, and E when the combined convection

and radiation boundary condition was applied. The least SSR values listed in the Irist

colurnn of Table 4.4 indicates the values of ab= 4.08 x IO-' rn2/s , u = 2 x 1 0 - ~ m/s. and

E = 0.5 are optimum values.

Table 4.5 summarizes the values for optimum conditions for the January 1994

shipment. The boundary condition lx=, = Ts (constant surface temperature) resulted in

the lowest SSR. The convection boundary condition did not yield minimum SSR

indicating that the heat transfer to cube was not by convection only. A combined radiation

and convection model provided a better solution than a convection model alone. For al1

three conditions u= 2 x 1ov6 d s was the optimum air speed. Figures 4.3 to 4.7 plot

Table 4.2: Parameter estimation for given surface temperature as boundary condition (Eq. 4.5) for January 1994 shipment.

ab, m2/s u, m/s 60 cm SSR

4.08~ 10-~ 2~ 1om5 10439

4 . 0 8 ~ 1 o ' ~ 2x 1 Oa 456 1

4.08x10-~ 2x 1 O“ 36212

4 . 0 8 ~ 1 O-' 2x 1 44306

4 . 0 8 ~ 1 O-' 2x 1 1957

8.16x10-' 2x 1 o - ~ 1591

1 .22x 1 o4 2x 1 o 6 1657

CQ, = bulk cube thermal diffusivity u = intergranular air velocity SSR = Sum of square of residuals

Table 4.3: Parameter estimation for convective boundary condition (Eq. 4.6) for the January 1994 shipment.

ab, m2/s u, m/s Surface SSR- 60 cm SSR

ab = bulk cube thermal diffusivity u = intergranular air velocity SSR = Surn of square of residuals

Table 4.4: Parameter estimation for convective and radiative boundary condition (Eq. 4.7) for the January 1994 shipment.

m, mL/s u, d s E Surface SSR 60 cm SSR

ab = bulk cube thermal diffusivity u = intergranular air velocity E = emissivity SSR = Sum of square of residuals

Table 4.5: Optimum values for the boundary conditions for the January 1994 shipment.

Boundary conditions ab, m2/s u, mis E 60 cm SSR 60 cm ATD, OC

a) Convection+ 4.08~ IO-^ 2x 1 O" 0.5 2213 1.3

Radiaton

b) Convection 4 .08~10-~ 2x10-~ - 2333 2.0

c) Constant Surface 8.16x10-' 2x10-~ - 1591 2.4

Temperature

ab = bulk cube thermal diffusivity u = intergranuhr air velocity E = emissivity SSR = Sum of square of residuals ATD = Average temperature difference

/ - Recorded 60 cm - Cornputrd 60 cm /

Min SSR= 1591

u= 2x10-~ d s -

Figure 4.3: Recorded and computed temperatures at 60 cm below the surface of the cube with constant surface temperature condition for the January 1994 shipment.

1 - Recorded 60 cm - Cornputcd 60 cm / 20

Min SSR=3810

u = 2x10-~ ds

ab = 4.08~1 O" rn2/s

Figure 4.4: Recorded and computed temperatures at 60 cm below the surface of cube with convection boundary condition for the January 1994 shipment.

I- Recorded air - Recorded surface. - Computed surface /

Min SSR=3810 u= 2 ~ 1 0 ~ ds

IB = 4.08x10-' m2/s

O 5 10 15 20 25 30

Trans porta tion pe riod, d

Figure 4.5: Recorded and computed surface tempenture of cube with convection boundary condition for the January 1994 shipment.

1 - Recorded 60 cm - Comp uted 60 cm ]

1 Min SSR=2213 1

Figure 4.6: Recorded and computed temperatures at 60 cm below the surface of cube with convection and radiation boundary condition for the January 1993 shipment.

- - -

Recorded air - Recorded surface - Computed surface 35 I

I

Figure 4.7: Recorded and computed surface temperatures of cube with convection and radiation boundary condition for the January 1994 shipment.

recorded and computed temperatures of the surface and 60 cm into the cube pile for each

boundary condition. In most of the cases, the average difference between the measured

and calculated temperatures was less than 2°C for each location in the cube pile.

Tables 4.3 and 4.4 show that the SSR of the surface temperature was almost

constant. On the other hand the SSR for temperature distribution 60 cm below the surface

changed substantially, from one set of parameter estimates (ab, u, E) to another. As such.

it would be more appropriate to estimate the parameters using the temperature

distribution 60 c m below the surface. Ideally, the recorded air temperature should be

close to the recorded surface temperature. But Figures 4.5 and 4.7 show an average

difference of 2S°C between recorded air and surface temperature. Since the recorded

surface temperature did not fluctuate like the recorded air temperature, it is likeIy the

sensor measuring the surface temperature was covered with cubes. Also the measured

surface temperature may not have reflected the actual condition, because the cubes

changed profile during transit.

Because of the difficulty and uncertainty in surface temperature measurement, the

surface temperature boundary condition is not recomrnended for simulation. Thus, for

solving Eq. 4.4, the proper boundary condition is a combined convection and radiation

boundary condition (Table 4.3, which is based on the ceiling and headspace air

temperatures. Also the lowest average difference in temperatures between the recorded

and computed conditions at 60 cm was 1.3"C for the convection and radiation condition

(Table 4.5).

Table 4.4 shows that reducing the emissivity value from 0.7 to 0.3 caused a little

reduction in the SSR for the 60 cm temperature from 4388 to 4355. Also by changing air

speed from 2 x 10 '~ to 2 x IO-* and 2 x 10" mls, the 60 cm SSR increased from 4368 to

287 19 and 34496, respectively. The SSR at 60 cm increased by about 50% (22 13 to 4272)

when the thermal diffusivity (a = 4.08 x 10-') was multiplied by 3. Thus. for the

combined convection and radiation, the temperature at 60 cm below the surface was not

sensitive to ernissivity but had a high sensitivity to air speed and thermal diffusivity.

The results of calculated values of Q, U, and E for July 1993 data are listed in

Tables 4.6 to 4.9 and Figures 4.8 to 4.12. The trend of data shown in the graph and tables

are sirnilar to January 1994 data.

Table 4.6: Parameters estimated for the given surface temperature as b o u n d q condition (Eq. 4.5) for the July 1993 shipment.

--

ab, m2/s U, m/s 60 cm SSR 4 . 0 8 ~ 10 -~ 2x 1 o - ~ 9 12 4 . 0 8 ~ 1 O-' 8x 1 o - ~ 703 4 . 0 8 ~ 10 -~ 5x 1 569 4 . 0 8 ~ 3x 10 -~ 607 4 . 0 8 ~ 1 2x 1 1683 1 22x 1 2x 1 389 1 . 2 2 ~ 1 3x 1 0 - ~ 34 1

1 . 2 2 ~ 1 5x 1 442 1 . 2 2 ~ 1 O 926

ab = bulk cube thermal diffusivity u = intergranular air veIocity SSR = Sum of square of residuals

Table 4.7: Parameters estimated for convective boundary condition (Eq. 3.6) for the July 1993 shipment.

m. m2/s u, m/s Surface SSR 60 cm SSR

- - - - - - - -

a b = bulk cube thermal diffusivity u = intergranular air velocity SSR = Sum of square of residuals

Table 4.8: Parameters estimated for the combined convective and radiative boundary condition (Eq. 4.7) for the July 1993 shipment.

a, mZ/s u, m / s E Surface SSR 60 cm SSR

- - -

ab = bulk cube thermal diffusivity u = intergranular air velocity E = ernissivity SSR = Surn of square of residuals

Table 4.9: Optimum values for the specified boundary conditions for the July 1993 shipment.

Boundary conditions ab, m2/s u. rnfs E 60 cmSSR 6 0 c m x ~ . OC

a) Convection+ 4.08x10-~ 2x10*~ 0.5 545 1.6 Radiation

b) Convection 1 .22~ 1 2x 1 o - ~ - 1000 2.6 c) Constant 1.22x106 3x10-~ - 34 1 1.1

Surface Temp. ab = bulk cube thermal diffusivity u = intergranular air velocity E = ernissivity SSR = Sum of square of residuals ATD = Average temperature difference

30 - -

Y 25 --

3 r)

Cs L

Min SSR= 341 15 -

u =3x 1 0 - ~ d s -6 '

CL^ = 1 .Ex10 mb/s 1 10 -

O 5 1 O 15 20 25 30 35

Tmnspottation period, d

Figure 4.8: Recorded and computed temperatures at 60 cm below the surface of cube with constant surface temperature condition for the July 1993 shipment.

1 - Recorded 60 cm - Cornpuied 60 m. /

Min SSR= IOOO

u = 2x10-~ d s

O 5 10 15 20 25 30 35

Trans portation period, d

Figure 4.9: Recorded and computed temperatures at 60 c m below the surface of cube with convection boundary condition for the JuIy 1993 shipment.

1 - Recorded air - Recordcd sudacc - Cornputeci surface 1

O 5 10 15 20 25 30 35

Transportation period, d

Figure 4.10: Recorded and computed surface temperatures of cube with the convective boundary condition for the July 1993 shipment.

- Recorded 60 cm - Compuied 60 cm 1 1

Min SSR=545

= ~ X I O ~ ds E = 0.5

O 5 1 O 15 20 25 30 35

Transportation period, d

Figure 4.1 1: Recorded and computed temperatures at 60 cm below the surface of cube with convection and radiation boundary condition for the July 1993 shipment.

/- Recorded air - Recorded surface - Cornputed surface i 1

50

Figure 4.12: Recorded and cornputed surface temperature of cube with the combined convection and radiation boundary condition for the JuIy 1993 shipment.

CHAPTER FIVE

MOISTUR-33 BALANCE

5.1 Introduction

The analyzed data of containerized bulk cubes showed that the humidity ratio in the

headspace increased during shipments from Canada to Taiwan (Appendix B). The

moistwe content of the cubes on the surface of the load also increased. The moisture

content inside the pile did not change. In this chapter, moisture transfer within the cube

pile and between cubes and the headspace is modelled mathematically. The humidity

ratio and moisture contents of the cubes inside the container are calculated. The computed

moisture balance is compared to the recorded humidity ratio in the headspace.

5.2 Mode1 Development

For the development of a moisture balance, it is assumed that the container is

completely sealed. Moisture transfer inside the cube pile in the container is due to vapor

diffusion. The result of thermal modeling inside the cube container showed that the flow

of air within the bulk of cubes was very small, on the order of 2 x 1 0 ' ~ m/s. Since

expenmental data for shipments showed that the cube moisture at 60 cm below the

surface did not change appreciably. It was assumed for sirnplicity that convective mûss

transfer within the pile was not a major factor. Thorpe (1982) also did not include this in

his models of grain storage systems. An equation analogous to equation 4.1 is written for

moisture balance within the cube pile:

where m is moisture flow in kg. s-' . m-'. C is moisture concentration in kg/m3 and t is

time in S. Equation 5.1 is written for a point as:

Using Fick's diffusion Iaw, the moisture flow within the bulk by diffusion only is

descnbed by:

where Dm is a moisture diffusion in m2/s. Substituting Eq. 5.3 in 5.2 results in:

Equation 5.4 describes moisture concentration within the bulk. The initial condition for

the problem is:

For boundary conditions, it is assumed that the cube pile is sufficiently deep so the

moisture content of the bottom layer remain constant. This assumption is justified by

experimentai data on the cube shipments whereas the moisture content of cubes rit 60 cm

deep did not show significant changes dunng the entire trip.

The second boundary condition on the surface of the cube pile at x = O is:

where ho is the mass transfer coefficient (rn/s) between the surface of the bulk and the air

in the headspace above the cubes. Equation 5.7 States that the moisture concentration C

within the bulk is bdanced by the moisture concentration in the headspace which is Ca.

Pixton and Griffiths (197 1) used grain moisture concentration gradients to describe

the diffusion process of moisture through a grain bulk (Eq. 5.4). They pointed out that the

isothemal diffusion of moisture through stored wheat is a very slow process. Thorpe

(1981) studied moisture diffusion through bulk grain and considered interstitial water

vapor pressure as the fundamental driving force. Thorpe (1982), using the ideal gas law,

recast Eq. 5.4 into the fonn of Eq. 5.8:

where P, is the partial pressure of interstitial water vapor in Pa, D, is the diffusion

coefficient of water vapor in air in m2/s, R, is the universal gas constant for water vapor

(46 1-52 J . kg" . K I ) , T is temperature in OC, E, is the porosity of bulk product. and y! is

an obstructive factor. The obstructive factor lumps the effects of tortuosity of the bulk and

the constrictive nature of the diffusion channek due to variations in the cross-sections of

the diffusion path.

Thorpe (1982) did not explain why the ideai gas law was applied only to the left

hand side of Eq. 5.4 while the nght hand transit term was kept intact. It appears that this

is a convenient step in solving this complicated problem. The nght hand side of Eq. 5.8

represents the gradient vapour pressure in the voids: the left hand side of the equation

describes the transient moisture changes in the solid.

Van Brakel and Heertjes (1974) reported the experimental values of the obstructive

factor for randornly packed granular media to be in the range of 0.50-0.60, and 0.55 was

selected for bulk alfalfa cube. Sokhansanj et ai. (1993) found a value of 0.45 for the

porosity of regular size alfalfa cube samples at 12% moisture content.

It is assumed that there is always equilibrium of rnoisture between the cube and air.

The partial pressure of water vapor in air can be found from its relative humidity and

saturation vapor pressure of water, P,:

where rh is the relative hurnidity (fraction) and P, is the saturation vapor pressure (Pa)

that can be found from psychometrîc properties (ASAE, 1994d). The relative humidity

inside a bulk cube c m be calculated from equilibrium moisture relations. In this study.

the Chung-Pfost equation for an alfalfa cube as given by Fasina and Sokhansanj (1993)

was used:

where M, is the moisture content in dry basis, fraction; a = 276.774, b = 35.856, and

c = 19.1 15 . The concentration of water in bulk cube was found from:

where p, is the dry bulk density of alfalfa cubes in kg/m3 which is assumed constant at

475 kg/m3 (Sokhansanj et al., 1993). Differentiating Eq. 5.1 1 with respect to time,

assuming no change in dry bulk density and using the chah rule, we get:

Finally Eq. 5.8 is written in the final form:

a p v When - is found from differentiating Pv with respect to Me in Eqs. 5.9 and 5. IO., aMe

equation 5.14 results in:

Equation 5.14 can be expressed as:

where p is a function of T and M, through the relationship:

The initiai and boundary conditions were used to solve Eq. 5.16:

The Lewis's reIation can be used to estimate the value of hD (Holman, 1990):

where h is the convection heat transfer coefficient in W . rn-'. K-'. Based on calculation

from Eq. 4.22, the value of 10 W. m-'. K-' was used for h. p, is the dençity of the air in

kg/m3, and c, is the specific heat of the air in J . kg" . K1, p,= 1.16 kg/rn3 and c, = 1007

J . kg-' . Ki. Holman (1990) reported the following empincai relationships for Dv,

diffusion of water vapor into air:

where T is in OC and D, is in m2/s.

5.3 Numerical Solution

A finite difference procedure similar to that used for heat transfer in Chapter four

was used to solve Eq. 5.16. The initial value for P, was calculated using the initial

temperature and relative humidity inside the buIk cube. Initially, aH P,'s including the P,,

of the headspace were set equal to the initial vapor pressures. The nodal points for the

mass transfer equations are depicted in Figure 5.1. Node O is in the headspace, node 1 is

on the cube surface and nodes 2, 3, 4, ..... are within the cube pile. Node B is at the

bottom of cube pile. The following finite difference equation was applied for the nodes

(2,3,4,. . .) in the cube pile:

The equation for the node I at the surface is expressed as:

Initially, Pv, is set equal to P, . the vapor pressure of the air in the headspace. P, is not

known beyond time zero. The only information available assurning a sealed condition is

the headspace temperature. Thus the set of equations is indeterminate. Ln other words, the

number of unknown nodal values for P,'s is one more than the number of equations. To

solve this problem another equation was developed. A moisture balance between the cube

top layer and the air in the headspace yields:

where H is the headspace humidity ratio in kgkg, A is the surface area of the bulk cube

perpendicular to the flow of moisture in m'. p, is the density of dry air in kg/rn3 and V, is

Headspace

Alfalfa Cubes

Figure 5.1: Finite difference nodes for the numerical solution of rnoisture transfer within bulk cubes and between cubes and the headspace. Drawing is not to scale.

the volume of headspace

pressure using the ideal gas

P

in m3. Concentration of moisture can be related to vapor

law :

where Pv is the vapor pressure in the air, R, is the universal gas constant for water vapor

and TabS is the absolute air temperature. Since R, and Tabs were assumed constant over a

small increment dx and dt, Eq. 5.25 can be expressed in terms of Pv:

In finite difference form, Eq.5.27 is expressed as:

where L is the depth of the headspace in m. Vapor pressure of air was found from the

following equation (ASAE. 1994d):

At intervals. the cdculated PV1s were used to estimate the corresponding humidity

ratio of headspace and moisture contents within the bulk cube.

The forward time central space finite difference method (FTCS) was used to solve

the goveming Eq. 5.16. The finite difference equations were written in FORTRAN code

with a time step (At) of 2 min and a space step (Ax) of 0.003 m.

5.3 Results and Discussion

5.3.1 Headspace humidity ratio

Equation 5.16 was solved for typicd recorded data of containerized alfalfa cube

shipments for May 1993, July 1993, October 1993, January 1994, and June 1994

(Appendix B). Figures 5.2 to 5.6 plot the recorded and the computed humidity ratio of the

headspace inside the container. The recorded hurnidity ratio was cdculated from the

psychometric relations between the measured temperature and relative hurnidity in the

headspace. The cornputed humidity ratio was from the numencal solution of Eq. 5.16

using the recorded temperatures in the headspace and the recorded temperatures inside the

cube pile. This computed humidity ratio represents the condition where the container is

assumed completely sealed.

The recorded humidity ratio of air inside each container is summarized in Table B.5

(Appendix B). Figures 5.2 to 5.6 show that after the containers were loaded with the

t 1 -Recordrd - 1

Cornputcd 1

35 t Canada Ocean Taiwan

Figure 5.2: Humidity ratio inside the cube container calculated from the recorded temperature and RH and the simulated humidity calculated from Eq. 5.16 (computed at sealed condition) for the May 1993 shipment

Canada i 35 Ocean : Taiwan

Transporta tion pe rio& d

Figure 5.3: Humidity ratio inside the cube container caiculated from the recorded temperature and RH and the simulated hurnidity calculated from Eq. 5.16 (computed at sealed condition) for the July 1993 shipment

35 Canada Ocean Taiwan

Figure 5.4: Humidity ratio inside the cube container calculated from the recorded temperature and RH and the simulated humidity calculated from Eq. 5.16 (computed nt sealed condition) for the October 1993 shipment

. Canada : Ocean Taiwan

Figure 5.5: Humidity ratio inside the cube container calculated from the recorded temperature and RH and the simulated humidity calculated from Eq. 5.16 (computed at sealed condition) for the January 1994 shiprnent.

1 - actual - catukted /

Ocean Taiwan

Figure 5.6: Humidity ratio inside the cube container calculated frorn the recorded temperature and RH and the simulated hurnidity calculated from Eq. 5.16 (computed at sealed condition) for the June 1994 shipment.

cubes, the overail humidity ratio started to increase until the container was placed on the

vesse1 in Vancouver. The hurnidity decreased during ocean travel and again started to

increase by the time the container arrived at a port in Taiwan. At the tirne of unloading.

the hurnidity ratio increased to the highest level. For example, during the May 1993

shipment (Fig. 5.2), the hurnidity ratio in the space was about 6 g k g of air in the

container after the container was loaded with cubes. The absolute hurnidity increased to

about 10.8 g/kg in Canada. It decreased to about 7 g k g during the ocean and increased to

12.2 g k g in Taiwan, and finally to 21.9 g k g at the time of unloading.

The recorded headspace hurnidity fluctuated during shipment, specially in Canada

and Taiwan. The fluctuation was more significant during warmer months. This mny be

from the repetitive evaporation and condensation on the surface of cube pile. For

instance, during the July 1993 shipment (Fig. 5.3), the recorded humidity ratio in the

headspace fluctuated daily from 10 to 20 g/kg while the load was in transit in Canada.

The humidity ratio decreased to about 8 @kg on the ocean and then increased to 22 @kg

when the load anived in Taiwan. While in Taiwan, the humidity ratio showed diurnai

variations representing repetitive evaporation and condensation. On the other hand the

computed humidity ratio did not show large variations. It showed an increase from 8 g/kg

to about 14 g/kg during the entire time of travel (Fig. 5.3).

The results show the recorded overall humidity ratio was higher than the computed

hurnidity ratio. Most of the moisture accumulation happened during the warmer months

(Figs. 5.3 and 5.6) and once in Taiwan when a container waç waiting to be unloaded.

Also, based on the recorded data, the moisture content of the cube pile (especially the

middle of the pile) was almost constant during transport. It can be concluded that the

difference between the recorded and computed humidity ratio was due to the moisture

penetrating from outside into the headspace.

During Canada, the difference between the recorded humidity ratio and the

computed values increased to about 5 g/kg in July 1993 and 1.2 g/kg in January 1994.

Whereas, the difference decreased during ocean transport and the difference increased

when the container arrived in Taiwan (about 13 g k g in July 1993 and 4 g k g in January

1994). The large difference between the hurnidity ratio in sumrner compared to winter

indicates that the surnmer air has higher hurnidity ratio than the winter air. Hence the

fluctuations of headspace humidity ratio (Figs. 5.3 and 5.6) is more pronounced during

sumrner than winter because the shipping container was not completely sealed.

5.3.2 Interna1 moisture transfer

The moisture contents recorded for the containerized cube shipments consisted of

the initia! moisture contents of cube samples at the time of loading and the moisture

contents of sarnples at the time of unloading (Table B.4). These moisture contents were

measured on the samples that were sent from Alberta and from Taiwan to the University

of Saskatchewan in Saskatoon. The exact sampling and shipping rnethod was not known.

Therefore, it was difficult to know how representative the reported moisture contents

were.

The simulation model of the vapor pressure in Eq. 5.16 dong with the equilibrium

moisture content relation in Eq. 5.10 were used to compute the moisture content on the

surface and 10 cm inside the cube pile. The recorded headspace temperature and

computed headspace relative humidity were used. The equilibrium moisture content in

Eq. 5.10 assumes that the entire cube is at a uniform moisture content and is completely

in equilibrium with its surrounding.

Figures 5.7 to 5.10 plot the computed moisture contents for shipments in May 1993,

July 1993, October and January 1994, respectively. Each shipment represents a season.

For the May 1993 and July 1993 shipments, the moisture contents show an increase when

the product is on the ocean. The cubes dried as they approached their pon of destination

in Taiwan. For October 1993 and January 1994 shiprnents, it appears that most moisture

fluctuations occurred in Canada and once on the ocean, the moisture content did not

fluctuate as much.

The recorded headspace temperature and relative humidity were used in Eqs. 5.10

and 5.16 to caiculate the surface moisture content and this value was compared to the

simulated moisture contents. Figure 5.1 1 plots the moisture contents on the surface and a

at several depths. It is noted that the computed moisture contents were lower than the

recorded moisture contents because of the assumption of "no l e a k in the model. The

surface moisture contents show fluctuation in both recorded and model calculations. But

the moisture content fluctuation leveled off at greater depths in the cube pile.

Table 5.1 shows the recorded and computed final moisture content of the cubes on

the surface at the time of unloading in Taiwan. For the May 1993 and July 1993

shipments, the recorded final moisture was higher than the cornputed moisture content.

For the October 1993 and January 1994 shipments the recorded final moisture content

and the computed moisture content are not much different. It can be deduced from the

data that moisture penetration into the container from the outside rnight be more severe in

warmer months than in colder months.

The model shows that under specified boundary conditions and assumption (no air

exchange occurred between the inside of the container and the outside air) the humidity

ratio of the headspace air did not increase. Correspondingly, there \vas no increase in

Canada Taiwan

Transportation period, d

Figure 5.7: Computed moisture contents on the surface and 10 cm into the cube pile for the May 1993 shipment.

1 O 15 20 25 Transportation perioà, d

Figure 5.8: Computed moisture contents on the surface and 10 cm into the cube pile for the July 1993 shiprnent.

/ - surface - 10 cm

1 Canada

Ocean

Figure 5.9: Computed moisture contents on the surface and 10 cm into the cube piIe for the October 1993 shipment.

- surface - 10 cm i Canada : Ocean Taiwan

Figure 5.10: Computed moisture contents on the surface and the January 1994 shipment.

30 35

10 cm into the cube pile for

Occan Taiwan

15 20

Transportaion period, d

I *-- Rccordcd ai 5 cin

Rccordcd ni 15 ciii

Rccordcd ai 20 cm

I- Coinputed nt ihe suriricc

Figure 5.1 1 Recorded and computed rnoisture content of bulk cube nt different levels for the june 94 shipment.

Table 5.1 : Recorded initiai and final and the computed final moisture content (%. w.b.) of cubes on the surface at the time of unloading in Taiwan.

Time of Recorded Computed

shipment Initial Final Final

May 1993 13.0 11.7 7.0

July 1993 12.3 22.9 8.5

October 1993 - 11.2 11.1

January 1994 11.6 11.9 10.8

June 1994 - 14.5 8.9

'-' not available

moisture content of the cubes. This suggests that perhaps there had been a leakage in the

container and some air exchange must have taken place. Since the mass of air in the

container is small as compared to the mass of products, the humidity ratio in the air is

sensitive to the changes in the cube moisture content.

It was also assumed that moisture content of the cube at the time of loading was

uniform through the pile. This assumption may not be valid as experience has shown that

the moisture content of cubes rnight Vary considerably, Le. 2 2% w.b. The accumulation

of moisture in the air and on the cube surface could also corne from successive

evaporation-condensation cycles that could have taken place inside the container. The

evidence also points to the fact that there is more moisture accumulation during warmrr

months when temperature variations are extreme, than colder months.

Considering al1 the above elements, it is concluded that there rnight have been some

degree of air exchange between the container headspace and the outside air. Consequently

the assumption of no air exchanpe used in the thermal mode1 (Chapter 4) could be

invalid. Nevertheless, the effect of air exchange on the thermal mode1 is difficult to asses

because data on ambient air condition during transport was not recorded.

CHAPTER SIX

CONCLUSIONS AND FUTURE WORK

6.1 Conclusions

This study focused on the effect of ambient conditions on the quality of alfalfa

cubes during their shipment from Canada to overseas. The containerized cubes were

exposed to extreme variations in temperature and humidity during transport. The data

obtained from commercial shipping showed the cubes absorbed moisture and sometimes

became moldy.

The research consisted of a laboratory experiment to study and develop a time-

dependent functional relationship between alfalfa cube quality and ambient temperature

and relative humidity. The heat and moisture transfers within the cube pile in a container

were modelled mathematically. The mathematical mode1 was used to identiw important

ambient factors to be monitored during shippings.

The following conclusions can be drawn:

1) The storage stability of alfaifa cubes is sirnilar to other agricukural products:

they can be kept safe at low temperature and humidity. Particularly, this research

showed that maintaining temperatures and relative humidity in the container

below 16°C and 70% RH extends the shelf life of the cubes to at least 90 days

(maximum number of days tested in this work). Therefore, in shipping cubes,

temperature and relative humidity should not exceed the aforementioned values.

2) Equations were developed to describe the quality of alfalfa cubes. The

independent variables are temperature and relative humidity of the environment.

The dependent variables are the important alfalfa quality factors.

a) the number of days that the cubes are mold free:

where d is number of days before visible mold is detected on the sample. k l

is a function of constant temperature and relative hurnidity (Table 6.1).

b) change in the green colour of cubes:

where a is the instantaneous colour and ai is the initial colour coordinate 'a',

k2 is given in Table 6.1 and t is time in days.

C) change in hardness:

where H and Hi are instantaneous and initial cube hardness (kN), k3 and IQ

are given in Table 6.1 and t is tirne in days.

w here

ki = ai + bi T + ci RH

Therefore, the quality models can be used to predict the cube quality during

storage or transit, provided that an online record of temperatures and humidities

is available.

Table 6.1: Constants a, b, and c and their estimates of standard errors (in parenthesis).

Constants a b c

k3 -0.142 0.00003 0.0028 (0.0 13) (0.00020) (0.0002)

k-4 4.132 0.004 0.03 1 (O. 13 1) (0.002) (0.002)

Table 6.2: Critical conditions and number of days before visible mold during the storage.

Temperature, RH, %

OC 60 65 70 75 80 85

'-' combination of temperature and RH was not attained 'N' mold did not appear '*' mold slightly

3) The appearance of visible mold on the cubes is the single most important factor

in downgrading and rejection of cubes. Table 6.2 summarized the critical

temperatures and relative humidities and nurnber of days before visible mold

was developed during the storage periods.

4) The calculated spoilage potentiai agreed with the observation of mold on the

cube. This mode1 can be used to predict the onset of mold growth dunng transit.

5) Based on the analysis of a one dimensional heat transfer of the containerized

cubes, cube temperature can be computed frorn the measurements of the

headspace temperature and the temperature of the container ceiling. Ceiling

temperature is important for the computation of radiative heat transfer to the

surface of the cubes.

6 ) The development of a one dimensional moisture transfer mode1 was carried out

with the assumption of no moisture exchange between the inside of the

container and the outside. The computed rnoisture contents were compared to

the recorded moisme contents obtained during commercial shippings. It is

shown that moisture increase in the cubes was a result of moisture penetration

into the container.

6.2 Suggestions for Future Research

The following are suggestions for further research:

Study of the feasibility to insulate, seal, andior ventilate the container filled with

alfalfa cubes to minimize temperature and moisture stratification within the

container.

Study methods of preventing direct contact between moist air and the cubes by

covering the cubes with a suitable barrier.

Conduct experiments in containers equipped with temperature and humidity

monitoring devices and a transmission signal to indicate in-transit conditions

within the container including the onset of visible mold.

Simulate of temperature and moisture distribution of cubes in containers

considering air exchange between the head space and the outside.

Extend the heat and mass transfer to two and three dimensional cases.

6.3 Practical Recommendation to Industry

Reduce the moisture content of cubes to 10%. The cube should be uniform in

moisture and d l are cooled and cured properly.

Seal or ventifate the container to minimize temperature and moisture

stratification on natural currents within the container. and to prevent the inflow

of additional moisture - especially from ocean air aboard ship.

Insulate the container wall especially the ceiling

Cover the cube surface with a shield to prevent direct contact between the cube

surface and wet condensate.

Place the container away from direct sunshine, rain, etc. A good location is

below the deck on the vessel. Placement of the containers below waterline

during sumrner is preferred.

Shorten the transit period especially rninimize time to unloading in Taiwan.

Equip each container with a temperature and humidity monitoring device and a

transmission signal to indicate the storage age of the cube. This device will give

the spoilage index for the cube. It sends a signal if cube has reach its storability

index of one.

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Jiang, S. and J-C. Jofnet. 1987. Finite element prediction of silage temperatures in

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Patil, R. T., S. Soldiansanj, M.H. Khoshtaghaza, and L. Tabil. 1996. Compression

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200.

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45 1-452.

APPENDIX A

ALFALFA CUBES QUALITY CHARACTERISTICS DURING

66 AND 90 DAYS OF STORAGE

Table Al : CoIour. moisture, hardness, and density variations over rime during 66 days of

RH. %

L. a. b= colour coordinates value M= rnoisture content, w.b.. 5%

storage at Test

chamber

1.1

1.2

I

2.1

9 7 -."

r

3.1

3.2

4.1

4.2

p= density. ~ g / r n ~ shaded= molded sarnplc

16.

H= hardness. kN

Table A.2: Colour. moisture. hardness, and density variations over time during 66 days of storage at 24.0°C.

C

M= rnoisture content. w.b.. % shaded= molded sample

110

Test chambei

5.1

5.2

6.1

6.2

7.1

7.2

L

8.1

8.2

L. a. b= cc

60I2

)ur coordinates value P= densi ty, ~ g / r n ~ H= hardness, kN

M H P L a b M .

H P

10.3 0.34 0.81 44.9 -4.8 15.0 10.3 0.34 0.81

10.7 0.24 0.80 47.2

1 1 .O 0.23 0.78 47.0

11.5 0.25 0.78 8

-4.8 -4.0 -4.5

12.0 0.27 0.74 45.3 -3.2

15.7 10.7 0.21 _

15.0 12.1 0.27

12.5 0.27 0.78 46.1

12.7 0.27 0.72 36.3

-2.6 15.6 ] 14.8

-1.6 15.5 12.5 0.24

0.80

11.1 0.22

15.5 12.5 0.25

0.7 1

11.5 . 0.23

0.68 0.83 _ 0.73 j 0.74

Table A3: Colour, moisture, hardness, and density variations over time during 66 days of storage at 3 1.4"C.

IuEoordinates value p= density. ~drn) H= hardness. kN

Test chamber

9.1

9.2

10.1

10.2

1 1 . 1

11.2

12.1

12.2

L. a. b= col( M= moisture content. w.b.. % shaded= molded sample

-

Table A4: Colour, moisture. hardness. and density variations over tirne during 90 days of storage at 8.8OC.

L. a. b= colour coordinates value H= hardness. kh'

M= wet basis moisture content. 92 p= density. ~ g l m '

Table A5: Colour. moisture. hardness. and density variations over time during 90 days of

- T

chai - 1;

17

18.

18.2

19.1

19.2

20.1

20.2

L. a. b= c

- - --

davs of storaee

our coordinates value p= density. ~ g / r n ~ H= hardness. kN

M= wet basis moisture content. % shaded = moided sample

Table A6: Colour, moisture, hardness, and density variations over time during 90 days of storage at 39.1 OC.

r

Test chamber

21.1

2 1.2

22.1

22.2

23.1

23.2

1

24.1

24.2

L. a. b= c DUT coordinates value p= density. ~ g / m ~ H= hardncss. kh' M= wet basis rnoisture content. % shaded = molded sample

I l4

Table A7: Temperature and RH of test chambers stored at 16°C for 66 days of storage.

T = temperature, OC RH = relative hurnidity, %

Table A8: Temperature and RH of test chambers stored at 24°C for 66 days of storage.

Table Ag: Temperature and RH of test charnbers stored at 3 1 OC for 66 days of storage.

Table A10: Temperature and RH of test chambers stored at 8OC for 90 days of storage.

T = temperature, OC RH = relative humidity, 5%

T = temperature, O C RH = relative humidity, %

T = temperature, O C

RH = relative hurnidity, %

APPENDIX B

EXPERIMENTAL DATA ON ALFALFA CUBES SHIPMENT

B.l Background

Althouph moisture movement induced by free convection or diffusion within bulk

forage ioads during transport has not been studied, Christensen and Meronuck ( 1986)

have documented numerous cases in which loads of grain, otherwise thought to be dry or

safe, were spoiled in transit. It has been shown that moisture migrates within bulk stored

grain of unifonn initial moisture content when temperature gradients exist within the

mass (Muir, 1973). The environment conditions which result in severe moisture

migration have not been reported in detail.

Jiang et al. (1986) measured temperature profiles in storage and found that the

surface layers of grain stored in bins were largely affected by outside tempentures. This

influence decreased towards the centre of the bin. Smith and Sokhansanj (1989) have

derived a criteria for the determination of the significance of natural convection. The

critena depended on the storage dimensions, particle size and packing factor.

B.2 Material and Methods

B.2.1 Cube containers and instrumentation

Two size of containers were used: 1) the Hi-cube larger size container (2.3 rn wide

x 12.2 m long x 2.9 m high; volume 76.0 m'), and 2) the standard size container (2.3 m

wide x 12.2 m long x 2.3 m high; volume 67.3 m3). The containers were not vented and

the doors were supposed to be sealed tightly.

After loading, the designated containers were instrumented with the following

sensors. Four temperature probes, one RH sensor and two condensation indicators.

Temperature (T) probes (type T thermocouples) measured the temperature of the roof, air,

cube surface and 60 cm depth of the cube pile. The RH sensor (mode1 RH-2, General

Eastern Instruments, 20 Commerce Way, Woburn, MA) was mounted on a stake above

the cube pile surface. Condensation boards (CB), which measured resistance across

parallel wires, were mounted under the roof and above the surface of the cube pile. A

condensed water droplet falling ont0 the CB caused a short circuit across the parallel

wires resulting in a reduction in electrical resistance being recorded. The roof

condensation sensor was fixed to the container ceiling with heat conductive cernent. Al1

sensors were installed at the centre back of the container about 1 m from the door. Figure

B 1 shows the location of the sensors within the container.

Two Campbell Scientific 21X data loggers were used to record data every 4h in the

ouers early shipments. Due to failure of the data loggers in the July 1993 shipment, data Io,,

in subsequent shipments in two containers were backed up with tape recorders and the

frequency of recording was increased to hourly in order to dump data to tape every day

when the memory block was full. Data loggers in the other two containers were backed

up with Radio Shack Mode1 102 portable cornputers and tape recorders, also with hourly

frequency of recording. In the June 1994 shipment the data loggers were placed below the

cube pile surface where the temperature was lower and more stabIe.

B.2.2 Loading, transportation, and unloading

Environmental monitoring equipment was installed in four containers dunng eac h

of five shipments of alfalfa cubes from Alberta to Taiwan. For al1 shipments, containers

were filled with alfalfa cubes at Bow Island, Alberta. The containers were loaded with

cubes to ensure a uniform load distribution. The cubes that were studied in this work were

produced from either the 1993 or 1994 crop. The cubes originated from alfalfa grown in

the following crop years. Trip #I was 1992 crop. Trips #2, #3, #4 were 1993. Trip #5 was

1994 crop. The average moisture content of alfalfa cubes at the time of loading was

10.1%-13.2% (w.b.).

The containers were immediateiy weighed on a commercial scale and tmcked to

Lethbridge, Alberta. The containers were then transported to Vancouver by rail and

loaded aboard the ocean vessel. For the ocean portion of the trip, the shipping Company

was asked to position the containers in four locations aboard the vessel as follows: above

deck port side (PS) which was exposed to Sun. above deck starboard (SB) which was

shaded, below deck above water line (BD-AW) and below deck below the water line.

Figure B2 shows the location of containers.

Condensation Boards ,

12.2 m

Figure B 1: Location of sensors in the container.

-

Dec kline

4 -3

3.2

1.3

B c l o w Dcck

Figure 82: Relative locations of cube containers onboard ship for trips 1, 3 , 4 , and 5.

1.4

4.1 3.1

Abovc Dcck

B.2.3 Cube inspection and testing

At Bow Island, cube samples were collected randomly as the cubes were loaded into

a container. The sarnples were placed in dark plastic bags and transported to the Forage

Laboratory of the University of Saskatchewan for physical tests. In Taiwan, often at a

farm site, the Overseas Merchandise Inspection Company (OMIC) retrieved the

monitoring equipment and inspected the cubes for mold damage prior to unloading the

containers. The OMIC staff also collected cube samples from the surface and middle,

placed these cubes in plastic bags in cardboard boxes and shipped them to the University

of Saskatchewan for testing. The data loggers and backup tape recorders and computers

were transported to a local computer shop where the information was down loaded to a

computer diskette. Al1 equipment was repackaged into suitcases and shipped back to

Canada to the Agriculture and Agri-food Canada Research Centre in Swift Current.

The laboratory tests consisted of cube moisture content, durability, cube hardness

and density.

B.3 Results

Table B. 1 lists twenty containers monitored and inspected at the origin and

destination. An identification number was assigned to each container by the shipping

Company. The approximate cube tonnage in each container as weighed on a commercial

scale after loading was about 25 tonnes. Table B. 1 also lists the container location on the

deck, dates of loading in Bow Island, sailing from the Port of Vancouver, arrival at the

port of Kaoshuing, Taiwan, and the date of unloading of the cubes (devanning). The

location of each container on the vesse1 is specified by two letters.

Al1 of the twenty containers were instrumented but data was collected only for sorne

containers. The remaining data were lost either due to equipment malfunction. loss of

data in transfer from the data logger to the diskette, or loss of data in transit. Table B2

lists the available data. For the July 1993 shipment, data loggers failed in three containers

probably due to excessive temperatures. Alfalfa cubes remained in containers between 30

and 70 days from loading to devanning, typically 8 days in Canada, 20 days aboard the

Table B 1 : Description and history of cube shipmenrs frorn Canada to Taiwan. - --

Container ~ & m e s season ~ ~ c a t i o n Load Sail Arriva1 Devan No nurnber lofCuberl 1 on ?+hip' 1 Date 1 Date 1 Date 1 Date 1 Ft;L 1

2.1 1 MOLU8040068 1 25.85 1 S u 93 1 D-P 1 7/28/93 1 3/4/93 1 8/24/93 1 9/6/93 1 No 1

1.1 1.2 1.3 1.4

MOLU8069441 1 25.85 1 Fa 93 1 D-S 1016193 1 10/14/93

MOLU2045782 MOLU2039060 MOLU8127424 MOLU8098244

2.2 2.3 2.4

3.4 1 MOLU8007542 1 26.04 1 Fa93 1 BD-AW 1 10/6/93 1 10/14/93 1 11/1/93 1 1 III8193 1 No 1

- 26.04 25.88 25.93

25.68 25.84 25.82

MOLU2 134200 MOLU2086380 MOLU8031744

3.2 3.3

S p 93 Sp 93 Sp 93 SD 93

S u 93 Su 93 Su 93

MOLU2125595 MOLU8168974

4.1 4.2 4.3 4.4 5.1

Sp = spring SU = surnmer Fa = faIl Wi = winter Note 1: BD-AW = Below Deck - Above Water D-P = On Deck - Port Side

BD-BW = BeIow Deck - Below Water D-S = On Deck - Starboard Side Note 2: Al1 containers were standard size 68 m3 except two containers rnarked '*' which were large

size (Hi-cube) 76 m3

5.2 5.3 5.4

BD-AW BD-BW

D-P D-S

25.53 25.94

GSTU7633691 GSTU8622829* GSTU2174901

MOLU01 10636* ILTU5443660

I

LJFCU2220506 GSTU8361598 GSTU6704785

5/26/93 5/26/93 5/26/93 5/26/93

BD-AW D-S

Fa 93 Fa93

26.04 25.75 26.16 25.85 25.79

7/28/93 7/28/93

1

26.02 24.65 25.94

6/3/93 6/3/93 6/3/93 6/3/93

9/6/93 9/4/93

BD-BW

8/4/93 8/4/93

D-P BD-BW

Wi 94 Wi94 Wi 94 Wi 94 Su 94

No Yes

6&7/09/93

8/24/93 8/24/93

Su 94 Su 94 Su94

6/21/93 6/21/93 6/21/93 6/21/93

No 7/28/93 1 8/4/93

10/6/93 10/6/93

D-S BD-BW D-P

BD-AW BD-BW

8/24/93

6/27/93 6/28/93 6/28/93 6/27/93

1/7/94 1/7/94 1 /7/94 1iï/94

6/24/94 BD-AW 1 6/24/94

Yes Yes Yes No

10/14/93~- I0/14/93

7/4/94 7/4/94 7/4/94

BD-AW BD-BW

1 1 1/93 11/6/93

1 1/1/93 11/1/93

1/13/94 1/13/94 1 / 1 3/94 1/13/94 7/4/94

6/24/93 6/24/94

Yes Yes

7/16/94 7/16/94 7/16/94

2/1/94 2/1/94 2/ 1/94 2N94 7/16/94

8/19/94 9/3/94 8/27/94

U4/94 2/5/94 2/6/94 34/94 811 1/94

Yes Yes Yes

Yes Yes Yes Ycs Yes

Table B2: The available data for analysis are marked by "x". Blank cells indicate no data or bad data

r ri^ No 1.1 1.2 1.3

RH

x x x

Container Nurnber

MOLU2045782 MOLU2039060 MOLU8127424

Roof temp.

x x x

Condensation Roof l~urface

Ourside lemp.

x x x

x x x

x

60 cm be- low temp.

x x x

Air temp.

x x

Cube sample Surface. temp.

x x x

load

x

x

unload 1

x x

x

vessel and 7 to 50 days in Taiwan.

Appendix C lists a sample of the collected data. Column A is the day number and

fraction of the day from the start. Temperature values have been rounded to the nearest

degree. Condensation numbers are such that a value of 50 indicates no condensation and

below 50 indicates condensation. Outside air temperatures during travel on the ocean

were recorded by shipboard instruments and provided by the shipping Company.

Figure B3 plots inside air temperature and RH during the May 1993 shipment. The

temperature was about 2 0 ' ~ at the time of loading followed by a period of daily

fluctuations while on Canadian soil. Temperatures fluctuated little during the ocean

travel. The temperature in the container decreased as the vessel travelled north and

increased gradually as it approached the port of destination in Taiwan. Temperature

fluctuations resumed when the container arrived at the port in Taiwan. The roof

temperature reached as high as 6 0 O ~ during the day and dropped as low as 2 0 ' ~ d u h g

the night.

Temperature and RH data for a July shipment is sumrnarized in Table B.3. The cube

temperature at the 60 cm depth in the pile did not fluctuate rapidly but gradually

increased from 19.4"C in Canada to 26.9"C in Taiwan. Maximum RH was attained at

minimum temperature. In Canada, when the temperature was high (30.0°C), the humidity

in the container was low (63%). Conversely, when the temperature was low (18.1°c) the

RH was high (86%). These conditions were not conducive to mold growth. At the

destination. however, the minimum temperature of 2 7 O ~ was observed at a high RH of

79% that could provide an ideal environment for moId growth.

Table B4 lists data on rnoisture content and physical properties of cubes sampled at

loading and at unloading. Test results shown in Table 8.4 are based on duplicate analysis

for moisture content and durability and with five replicates for hardness and density.

Generally, values for hardness showed large variations which are common for this test.

The vacant space in the table indicates that samples were not received for testing. The

sample moisture contents prior to a shipment ranged from 10.0% to 13.2%. The moisture

contents of the cubes were as high as 26.5%.

Other data and obsemations recorded were the degree of cube spoiIage and profile

Reiative

Canada Ocean Taiwan 1 ' 0 O 5 1 O 15 20 25 30 35

Transportation period, d

Figure B3: Air temperature and relative humidity inside the container, May 1993.

Table B3: Summary of temperature and relative hurnidity data of a shipment of cubes from Alberta to Taiwan, in July 1993.

Location I Canada 8 days

Roof temp., OC

25.2 37.2 16.4

Average Maximum Minimum

Sea 14 days

Destination 8 days

Average Maximum Minimum Average

Maximum Minimum

Inside air temp., OC

23 -5 30.0 18.1 19.8 22.1 18.1 3 1 -4 35.8 28.2

Cube surf. temp., O C

21.2 22.1 20.5 19.3 20.2 18.8 29.4 30.0 28.7

60 cm below cube temp.. OC

19.4 19.6 19.2 17.8 18.3 17.4 26.9 27.4 26.6

Relative humidity, %

75 86 63

80 82 79 73 79 66

Table B4: Moisture content and physical properties tested in the processing laboratory at University of Saskatchewan

Trip Season No

Initial

m.c Dura- Hard- Den- bility ness sity

(a) (96) (NI (kg/m3

Average 13.0 91.4 325 825 2.1 Su93 12.3 76.1 151 670 2.2 Su 93 12.2 85.6 239 650 2.3 Su 93 12.2 89.3 122 650 2.4 Su 93 12.6 87.7 193 690

Average 12.3 84.7 176 665 3.1 Fa93 - - 3.2 Fa93 - - 3.3 Fa93 - - 3.4 Fa 93 - -

Average

Average 11.6 79.0 368 753 5.1 Su 94 - -

Sampling at ~ e s h n a t i o n

surface middle

bility ness sity biiity ness sity

Sp = spring SU = sumrner

m.c. = moisture content, w.b.

F a = fall Wi = winter

of the cube pile in the container after loading and at the time of unloading. These profiles

are depicted in Figure B4. A slight drop in the height of cube pile in the container, and a

more uniform profile were observed. Cube spoilage will be discussed in the next section.

Table B5 lists the inspectors report on the cube quality inspected by OMIC at the

time of unloading. "Good" means that no visible mold was observed. Ten percent mold

means that 10% (estimated) of the total content load of cubes was judged moldy. A little

mold means that patches of mold were observed on the cubes.

Condensation occurred at both the above cube surface and on the wall (and ceiling)

during winter and summer trips, though its occurrence was higher in summer than in

winter. The condensation board in the container showed that the surface condensation

started in Canada and occurred daily. Condensation disappeared once the container was

on the ocean. The condensation reappeared when the container arrived in Taiwan.

B.4 Discussion

B.4.1 Cube spoilage

In addition to visible mold growth, cubes lost durability and hardness in transit.

Table B4 lists the averages of moisture contents and other physical properties during each

shipment. For the summer shipments in 1993 and 1994 the cube surface moisture content

increased substantidly, almost from 1 1%- 12% to 25%-27%. This high moisture with

prevailing high temperatures caused mold growth. The data in Table B4 also show that

other properties such as durability and hardness decreased especiaily for those sarnples

removed from the surface of the pile. Cubes removed from the middle of the pile showed

no mold and a lesser change in properties compared to the surface cubes.

One interesting point to note is that the density of individual cubes decreased during

transit indicating a net increase in the volume of the cubes. Figure B4 shows that there

was a net drop in the height of the cube pile in the container. One may then conclude that

cube packing in the container increased substantiaIly during transit. It should also be

noted that the Iower density for cubes meant that they lost their integrity via expansion.

Front Rear

Figure B4: Typical cube profiles within container: a) at loading, b) during transit, c ) upon anival.

Table B5: Humidity ratio of the headspace.

MOLU2045782 MOLU2039060 MOLUS 127424 MOLU8098244

Average MOLU8040068 MOLU2 134200 MOLU2086380 MOLU803 1744

Average

MOLU806944 1 MOLU2 125595 MOLU8 168974 MOLU8007542

Average

GSTU763369 1 GSTU8622829 MOLU2 17490 1 MOLUO 1 10636

Average

ILTU5443660 UFCU2220506 GSTü836 1598 GSTU6704785

Average Overall average

Sp = spring SU = sumrner

Inspection 1 Load 1 Sail

good good good good

10% moldy 10% moldy 10% moldy 14.72 16.00 10% moldy

14.72 16.00

good good good good

good g ood good good

good 12.86 13.39 good 11.06 10.13

little mold 9.29 11.32 little mold 10.09 10.04

10.83 11.22

ratio. @,o. at

Arrivnl 1 Unloïd

13.95 1 18.93 Wi = winter

B.4.2 Moisture content

The fact that the moisture content of cubes increased during shipment indicated that

moisture was added to the space within the containers. Table B4 showed that the moisture

content of the cube pile was either unchanged or slightly increased even for cubes

removed from the middle of the pile. Therefore the source of moisture must have been

from outside sources.

Relative humidity of the space above the cubes and the air temperature were used to

calculate absolute hurnidity (g of H20 per kg of air) using Psychometric formulas given

in the ASAE Standard D371.2 (ASAE, 19944). Humidity ratio, H, was a measure of the

arnount of actual water in the air in the form of vapor. The humidity ratio was used to

determine if water was added or removed from the air during transport.

Figure B5 shows a typical plot of absolute humidity (H) during a January 1994

shipment. The hurnidity ratio in the space was about 3 g/kg of air in the container after

the container was loaded with cubes. The absolute humidity (H) increased to about 4 @kg

by the time the container was placed on the vessel. There was an increase in H to about

10 g/kg when the container arrived in Taiwan. At that point there was a fluctuation in H

frorn 8 to 13 gfkg.

Table B5 summarizes the absolute hurnidity (H) of air inside each container at the

time of loading, sail date, arriva1 in Taiwan, and at the time of unloading. The data show

that the amount of water in the airspace increased with the length of time cubes remained

in the container. The overall average hurnidity was 8 @cg at the tirne of Ioading. It

increased to 9.1 g/kg in Vancouver, to 13.95 g/kg in Taiwan, and finally to 18.93 @kg rit

the time of unloading.

The data in Table B5 show that the most pronounced moisture accumuIation in the

container was during warmer months and during the time when a container was waiting

to be unloaded in Taiwan. It appears that the source of increased humidity is air

movement from the outside to the inside of the container. No discernible difference in the

humidity ratio between different locations of the container on the vessel was detected. It

is then concluded that hurnidity of the outside air might be the source of humidity

35

Canada i Ocean Taiwan

T nippmg aare. ~ u i y LWYJ

f

- shipping;date. Jan 7/94

O 5 1 O 15 20 25 30 35

Transportation period, d

Figure B5: Typical hurnidity ratio versus tirne of the shipment during surnmer and winter.

increase in the container. Rain or snow, and rnist were not a factor.

B.4.3 Location of the container on the vessel

Table B6 lists the maximum, minimum, and average temperature of the container

ceiling during transit. The ceiling temperahue was the most sensitive indicator of the

changing environment around the container. Therefore this temperature was used to test

the effect of the segments of the trip (Canada, ocean, Taiwan) and placement of the

container on the vessel. The degree of temperature fluctuation was calculated around a

moving average. Equation B 1 was used to calculate the average:

Table B.6: Maximum, minimum, average, and deviation of ceiling temperatures from the mean.

Sipment ID 1 In- 1 placement

1 . 1 MOLU2045782

spnng 93 1.2

MOLU2039060 spring 93

1.3 MOLU8 127424

spring 93 2.3

MOLU2086380 sumrner 93

3.1 MOLU806944 1

faIl 93

fa11 93 4.1

GTSU763369 1 winter 94

- -

4.2 GSTU8622829

winter 94 4.3

GSTU2 t 7490 1 winter 94

5.1 ILTU5443660

sumrner 94 5 -2

UCFCU2220506 summer 94

5.3 GSTU8361598

Ocean BD-AW Taiwan Canada Ocean BD-BW +-

Taiwan Canada Ocean D-P Taiwan Canada Ocean D-S

Taiwan Canada Ocean D-S

Taiwan Canada Ocean D-P Taiwan Canada Ocean BD-BW

Ocean 1 D-S

Ocean BD-BW Taiwan

Ocean Taiwan

Ocean BD-BW Taiwan Canada Ocean BD-AW + Ocean 1 BD-AW

BD-BW summer 94 Taiwan

Ceiling temperature, O C

N. Max Mi Avg. Devia- data n. tions

where Tm is the mean temperature and a is a smoothing factor (a=0.25 was used). the

subscnpt 'j' indicates the data point. The temperature deviation (s-d.) was calculated by:

where n was the number of the data points. Equation (B.2) was calculated for each

segment of the trip. The resulting averages were rounded to whole number and listed in

Table B.6.

Table B6 shows that the ceiling temperature depended on the season, transit period.

and placement of the container on the vessel. The maximum temperatures ( 7 5 ' ~ . load

1.1) was recorded in Taiwan, and the minimum temperature (-16O~. load 4.2) was

recorded in Canada. Generally the average temperatures in Canada were IOOC to 2 0 " ~

lower than the average ceiling temperature in Taiwan. In al1 cases the least variations

were during the ocean trip, followed by Canada, and Taiwan.

The variations in temperatures for the below deck placement were 1. 0, O compared

to 2, 3, 2 for the above deck placement (Spring 1993, Fa11 1993 and Winter 1993).

Adequate data for the Summer 1993 and for the Summer 1994 were not available to

compare the above deck and the below deck placements. Judging from the few data

available, we rnay conchde that there was less variation in ceiling temperature for the

below deck containers. It is interesting, however, to note that the average temperature is

warmer below the deck than above deck (probably due to engine heat)

Data are available from two shipments to compare above deck starboard and port

side container placements (D-S and D-P, Fa11 1993 and Winter 1994). Variations were

1°C for the starboard side and 3°C and 2°C for the port side. Port side containers were

exposed to sun whereas the starboard side was relatively shaded. To compare below water

and above water (BW and AW), the following variations 3O , 8O, 2°C for the AW and Io.

O", 0°C for the BW were found. Less variation was evident for the containers transported

below the water line.

APPENDIX C

A SAMPLE OF DATA FOR ONE SHIPMENT (JULY 1993)

1 Shipping Date:July 28/93 Note: T,= roof temperriture,"C Cs= Surface condensation 2 Container: Molu 2086380 Ti= air temperature,"C C,= roof condensation 3 Cube Condition: 10% moldy T,= surface temperature,"C 4 Date of Unloading: Sept 4/93 (Julian 247) T e 60 cm below the surface temperature."C 5 Extemal Extemal 6 RH ternp."C RH tempV0C 7 Day T, Ta T, Ta Cr Cs % Air Sea Day T, Ta T, T60 Cr Cs 8 Air Sea -- 8 0.67 39 28 20 18 50 50 64 17.00 21 19 15 14 19 50 78 23 23