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MODELING OF GRAIN DRYERS: THIN LAYERS TO

DEEP BEDS

Díaz Martínez Jorge Alonso

Bioresource Engineering Department, McGill University, Montreal

A thesis submitted to McGill University in partial fulfilment of the

requirements of the degree of M.Sc.

© Díaz Martínez Jorge Alonso, 2011

2

ABSTRACT

Díaz Martínez Jorge Alonso – MSc Bioresource Engineering

Modeling of Grain Dryers: Thin Layers to Deep Beds

In order to store grain safely, it has to be dried; however, this process

consumes large amounts of energy. Traditionally, grain is dried in small amounts

using natural air, but now a days, agro industry requires to dry large amounts of

grain in a short time. Burning the fossil fuels is the main energy source for drying

grains, resulting in a polluting and expensive process. The use of alternative

energy sources, biomass or sun, is not commonly used because they are neither

reliable nor cheap. Heat pumps and microwaves are other ways to reduce the

energy consumption in the drying process; however, the initial investment is

higher. Moreover, they use electricity which is several times more expensive than

thermal energy from fossil fuels depending on the location and the mode of

energy conversion to electricity.

The energy consumed for drying grains is mainly used in three process

steps: warming up of the grain, evaporating water, and heating the humid air. In

order to make the drying process really efficient, it is necessary to recover the

energy from these three steps, or to extract the water in liquid form from the

3

kernel. However, developing these alternatives has taken several decades.

Meanwhile, it is important to improve the performance of the present dryers.

In the present study, a predictive mathematical model, based on the

process thermodynamics, was developed to simulate the drying kinetics of

grains. The model describes how the grain and air conditions change during the

drying process. It allowed to measure the impacts of process parameters such

as: ambient air temperature and humidity, initial grain moisture, bed depth, and

drying air flow and temperature on the performance of the drying process. The

model permitted to develop control strategies to increase process performance,

to reduce drying time and minimize energy consumption.

4

RÉSUMÉ

Díaz Martínez Jorge Alonso – MSc Génie des Bioressources

Modélisation des séchoirs à grains: des couches minces aux

couches épaisses

Afin d’assurer leur conservation, les grains fraîchement récoltés doivent être

séchés avant l’entreposage. Autrefois, les grains étaient séchés en petites

quantités en faisant circuler de l'air ambiant. De nos jours, les entreprises

agricoles doivent sécher des quantités phénoménales de grains dans de très

cours laps de temps et ce processus consomme de grandes quantités d'énergie.

Dans les pays industrialisés, les combustibles fossiles sont la principale source

d'énergie pour le séchage des grains. Malheureusement, c’est un processus

polluant et coûteux. L'utilisation de sources d'énergie alternatives comme les

biomasses ou l’énergie solaire ne sont que rarement utilisés parce qu'ils ne sont

pas assez fiables ou plus coûteux. Des travaux de recherche ont démontré qu’il

était possible de sécher les grains à l’aide de pompes à chaleur ou avec des

micro-ondes. Toutefois, leur utilisation à l’échelle commerciale n’est pas

présentement économique. De plus, ces technologies demandent de grandes

quantités d’énergie électrique qui est plus souvent qu’autrement issue de

l'énergie thermique produite à partir de combustibles fossiles.

5

L'énergie utilisée pour le séchage des grains permet de les réchauffer et

d’en extraire l’humidité sous forme de vapeur. L’énergie s’échappe du séchoir

sous forme d’air chaud et humide. Il est possible d’accroître l’efficacité des

séchoirs à grain en récupérant une partie de cette énergie et en développant des

modèles mathématiques permettant de mieux comprendre et de mieux contrôler

le processus de séchage.

Dans la présente étude, un modèle mathématique pour simuler la cinétique

de séchage de grains a été élaboré et validé. Le modèle prend en charge la

température et la teneur en eau de l’air ambiant, la nature et le débit du grain, le

taux initial d'humidité du grain, la profondeur de la couche, la température et le

débit d'air de chauffage, et la source d’énergie. Le modèle a permis d’évaluer les

effets des paramètres du procédé sur la cinétique de séchage des grains et sur

la consommation d’énergie. Les résultats obtenus ont permis de développer de

nouvelles stratégies de contrôle afin d’améliorer la performance du procédé, de

réduire les temps de séchage et de minimiser la consommation d’énergie.

6

ACKNOWLEDGEMENTS

Two years ago, I did not know that grain had to be dried safely for storage

purposes. Now, with the help of Dr. G. S. Vijaya Raghavan and Mr. Yvan

Gariépy, we have developed a model to identify how the drying process

performance can be upgraded reducing its energy consumption.

7

CONTRIBUTIONS OF THE AUTHORS

The work reported here was performed by Jorge Alonso Díaz Martínez and

supervised by Dr. G.S.V Raghavan of the Department of Bioresource Engineering,

Macdonald Campus of McGill University, Montreal.

The authorship of the first and second papers (chapters 3 and 4 respectively)

includes Jorge Alonso Díaz Martínez, Yvan Gariépy and G. S. V. Raghavan.

M. Yvan Gariépy from the Department of Bioresource Engineering was

involved in the development, implementation and data analysis.

8

TABLE OF CONTENTS

TABLE OF CONTENTS ................................................................................................................... 8

LIST OF FIGURES ......................................................................................................................... 10

LIST OF TABLES ........................................................................................................................... 12

NOMENCLATURE.......................................................................................................................... 13

1.1. Subscripts...................................................................................................................... 14

1. INTRODUCTION.................................................................................................................... 15

1.1. Hypothesis..................................................................................................................... 18

1.2. Objectives...................................................................................................................... 19

1.2.1. Sub-objectives: ......................................................................................................... 19

1.3. REFERENCES .............................................................................................................. 19

2. LITERATURE REVIEW ......................................................................................................... 21

2.1. Grain Storage ................................................................................................................ 21

2.2. Energy demand for grain drying .................................................................................... 22

2.3. Grain Drying Theory ...................................................................................................... 23

2.3.1. Desorption ................................................................................................................. 23

2.3.2. Equilibrium Moisture ................................................................................................. 23

2.3.3. Diffusion .................................................................................................................... 25

2.3.4. Vapor Pressure Deficit (VPD) and the Differential (VPDI) ....................................... 26

2.3.5. Drying Periods .......................................................................................................... 29

2.3.6. Heat and Mass Transfer ........................................................................................... 29

2.4. Drying Models ............................................................................................................... 30

2.4.1. Thin Layer Model ...................................................................................................... 30

2.4.2. Deep Bed Model ....................................................................................................... 32

2.5. Grain Dryers .................................................................................................................. 33

2.5.1. Commercial Dryers ................................................................................................... 34

2.5.1. Alternative Dryers ..................................................................................................... 36

2.5.1.1. Solar Energy ....................................................................................................... 39

2.5.1.1. Heat pump .......................................................................................................... 39

2.6. Conclusion..................................................................................................................... 42

2.7. References .................................................................................................................... 43

CONNECTING TEXT ..................................................................................................................... 46

3. MODELING OF GRAIN DRYERS:THIN LAYERS TO DEEP BEDS ..................................... 47

3.1. Abstract ......................................................................................................................... 47

3.2. Introduction.................................................................................................................... 48

3.3. Materials and methods .................................................................................................. 50

9

3.3.1. Equations used ......................................................................................................... 50

3.3.2. Model Validation ....................................................................................................... 54

3.3.2.1. Thin Layer: .......................................................................................................... 54

3.3.2.2. Deep Bed ............................................................................................................ 55

3.4. Results and discussion ................................................................................................. 56

3.4.1. Thin Layer ................................................................................................................. 56

3.4.2. Deep bed (in bin batch dryer- study case) ................................................................ 59

3.4.3. Deep bed (in bin batch dryer- general performance) ................................................ 60

3.5. Conclusions ................................................................................................................... 66

3.6. Acknowledgement ......................................................................................................... 66

3.7. References .................................................................................................................... 66

CONNECTING TEXT ..................................................................................................................... 68

4. INCREASING THE IN-BIN BATCH DRYER PERFORMANCE BY MONITORING

THE EXHAUST AIR ............................................................................................................... 69

4.1. Abstract ......................................................................................................................... 69

4.2. Introduction.................................................................................................................... 70

4.3. Materials and methods .................................................................................................. 71

4.3.1. Reference conditions ................................................................................................ 72

4.3.2. Regulating Temperature ........................................................................................... 72

4.3.3. Regulating Airflow ..................................................................................................... 73

4.4. Results and discussion ................................................................................................. 73

4.4.1. Reference conditions ................................................................................................ 73

4.4.2. Regulating drying temperature ................................................................................. 74

4.4.3. Regulating airflow ..................................................................................................... 76

4.4.4. Reference versus regulating results. ........................................................................ 78

4.5. Conclusions ................................................................................................................... 80

4.6. References .................................................................................................................... 80

5. SUMMARY AND CONCLUSIONS ......................................................................................... 82

6. REFERENCES....................................................................................................................... 85

10

LIST OF FIGURES

Figure 2-1: Allowable storage time for shelled corn. Adapted from Sauer (1992). ....................... 21

Figure 2-2: Energy requirements for production of corn in the Midwestern United States as

a percentage of the total. Adapted from Brooker et al. (1992). ................................. 22

Figure 2-3: Comparing Samapundo et al. (2007) desorption data with modified

Henderson’s equation ................................................................................................ 25

Figure 2-4: “Vapor Pressure Deficit (VPD) enhances or inhibits the crop’s ability to

transpire”(Prenger & Ling, 2000). .............................................................................. 27

Figure 2-5: Modified psychometric chart showing the vapour pressure values (Prenger &

Ling, 2000). ................................................................................................................ 28

Figure 2-6: Constant and falling rate periods in thin-layer drying of high moisture grain

(Gunasekaran & Thompson, 1986) ............................................................................ 30

Figure 2-7: Thin layer test setup used by Farkas and Rendik (1997) ........................................... 31

Figure 2-8: Illustration of a deep bed as a series of thin layers. (T and H are temperature

and humidity ratio of drying air, respectively. Subscripts n, n + 1, and n + 2

represent the corresponding grain layer.) from Morey et al. (1978). ......................... 33

Figure 2-9: Schematic diagram of basic simulation approach, adapted from Thomson et al.

(1968). ........................................................................................................................ 33

Figure 2-10: Dimensionless drying rate curves, (Hukill, 1947) ...................................................... 34

Figure 2-11: A typical batch dryer bin (Raghavan & Sosle, 2007) ................................................ 35

Figure 2-12: A typical stationary continuous flow dryer with an air recirculating system

(Raghavan & Sosle, 2007). ........................................................................................ 36

Figure 2-13: Schematic of a continuous-flow two-stage concurrent flow dryer by Ferrell-

Ross CCF (Bakker-Arkema et al., 1981). .................................................................. 38

Figure 2-14: Solar dryer with collectors used by Stratford (1984) ................................................. 40

Figure 2-15: Schematic view of solar cabinet and open drying setup (Perumal, 2007) ................ 41

Figure 2-16: Vacuum assisted solar and open sun drying set up for tomato slices (Perumal,

2007) .......................................................................................................................... 41

Figure 2-17: Two configurations of the heat exchangers vis-à-vis the drying chamber. The

dark, thick streams indicate airflow (Sosle, 2002). .................................................... 42

Figure 2-18: Comparison of energy consumption among the different modes drying (Sosle,

2002). ......................................................................................................................... 43

Figure 3-1: Schematic diagram of basic simulation approach, adapted from Thomson et al

(1968) ......................................................................................................................... 50

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Figure 3-2: Corn thin layer test results taken from Misra and Brooker (1980), and

simulation curves from SEDA .................................................................................... 57

Figure 3-3: Corn batch drying test results taken from Martínez-Vera et al. (1995), and

simulation curves from SEDA .................................................................................... 58

Figure 3-4: In bin batch dryer case – Grain moisture (simulation results). .................................... 61

Figure3-5: In bin batch dryer case – Grain temperature (simulation results). ............................... 61

Figure3-6: In bin batch dryer case – Exhaust air properties (simulation results). ......................... 62

Figure 3-7: In bin batch dryer case – Energy demand (simulation results). .................................. 62

Figure 3-8: Energy demand. Initial Grain Moisture 0.316db (24%wb), Dryer Air Temp.

54 °C, Air Flow 15 m3·min-1·ton-1 ............................................................................. 63

Figure 3-9: Energy demand at three weather scenarios . Initial Grain Moisture 0.316 db

(24% wb). ................................................................................................................... 64

Figure 3-10: Drying Time . Ini. Moisture 0.316 db (24% wb), RH 75%, Amb. Temp. 10 °C. ........ 64

Figure 3-11: Moisture Difference . Initial Moisture 0.316 db (24%wb), RH 75%, Amb. Temp.

10 °C. ......................................................................................................................... 65

Figure 4-1: Reference setup – Simulation results. ........................................................................ 74

Figure 4-2: Regulating drying temperature – Simulation results ................................................... 75

Figure 4-3: Regulating airflow – Simulation results ....................................................................... 77

Figure 4-4: Setups with the best performance from the three scenarios. ..................................... 79

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LIST OF TABLES

Table 2-1: Dryer Types, from (1Bakker-Arkema et al., 1981; 2Raghavan & Sosle, 2007;

3Sauer, 1992) ............................................................................................................ 37

Table 3-1: SEDA Parameters for corn. .......................................................................................... 54

Table 3-2: Drying conditions and R2 value for the thin layer tests and model curves shown

in Figure 3-2. .............................................................................................................. 58

Table 3-3: Drying conditions, and R2 values for the batch drying tests and its model

curves are shown in ................................................................................................... 59

Table 3-4: Drying conditions shown in Figure 3-9. ........................................................................ 65

Table 4-1: Conditions of the four reference cases. ....................................................................... 72

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NOMENCLATURE

AF Air Flow (std m3·min-1·ton-1 of dry mass)

aw Water Activity

ca Heat capacity at constant pressure of dry air (1.006 kJ/kg K)

cg Heat capacity at constant pressure of dry grain mass ( kJ/kg K)

cv Heat capacity at constant pressure of water vapor (1.84 kJ/kg K)

cw Heat capacity at constant pressure of liquid water (4.18 kJ/kg K)

Dc Grain Moisture Diffusivity - Constant surface coefficient

Dc2 Grain Moisture Diffusivity - Constant center coefficient (s-1)

Dm Grain Moisture Diffusivity – Moisture correction factor

Dmc Grain Moisture Diffusivity – Moisture correction coefficient

Dt Grain Moisture Diffusivity - Temperature correction factor

Dtc Grain Moisture Diffusivity - Temperature correction coefficient (°C-1)

Ha Air Enthalpy (kJ)

Hg Grain Enthalpy (kJ)

HR Humidity Ratio (g/kg dry air)

ΔHR Delta Humidity Ratio (g/kg dry air)

k constant

lw Specific latent heat of evaporation of water at 0°C (2501 kJ/kg K)

ma Mass of dry air (kg)

mg Grain Dry Mass (kg)

mw Water mass inside grains (kg)

Ma Average Grain Moisture (kg/kg dry basis)

Mc Center Grain Moisture (kg/kg dry basis)

Me Equilibrium Grain Moisture (kg/kg dry basis)

Ms Surface Grain Moisture (kg/kg dry basis)

R2 Coefficient of determination

RH Relative Humidity of Air (Percent)

STc Surface Transfer – Constant Coefficient (g)

STaf Surface Transfer – Air flow correction factor

STaf1 Surface Transfer – Air flow coefficient #1 (s)

STaf2 Surface Transfer – Air flow coefficient #2

STVPD Surface Transfer – Vapor Pressure Deficit Correction factor

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STVPD1 Surface Transfer – VPD Coefficient #1 (kPa-1).

STVPD2 Surface Transfer – VPD Coefficient #2.

t Time (s)

Δt Delta Time (s)

T Temperature (°C)

ΔT Delta Temperature (°C)

Tg Grain Temperature (°C)

Ta Air Temperature (°C)

vpair Vapour Pressure of the air moisture (kPa)

VPD Vapour Pressure Deficit of air (kPa).

vpsat Water Saturation Vapour Pressure (kPa)

WVa Air Water Vapour (g)

WT Surface Water transfer between grain and air (g)

1.1. Subscripts

a dry air

A Average

af air flow

c constant coefficient

C Center

d Diffusivity

e equilibrium

g grain

t temperature correction factor

v water vapor

w water

1,2.. Consecutive

15

CHAPTER I

1. INTRODUCTION

More than 70 million tons of water have to be removed from grain crops

annually to ensure their safe storage and delivery (Raghavan & Sosle, 2007); It is

like evaporating a water cube with a side of more than 400 m. Moreover, the total

amount of grain required to feed a hungry world is constantly increasing. Without

removal of moisture, the growth of mold occurs, so drying is absolutely essential.

The current drying methods, however, require a huge expenditure of energy,

which is mostly produced by nonrenewable resources, particularly fossil fuels. In

terms of energy consumption for grain production, the drying of grain is the

largest factor. The agricultural industry is among the world’s greatest energy

consumers, consequently, “drying, a major unit operation in agro-food industry,

carries a huge environmental cost” (Sosle, 2002). Paradoxically, grain, especially

corn, has been used as an important energy source in the form of biofuel for a

number of years. It is desirable, therefore, from both economic and

environmental perspective to reduce the expenditure of costly and polluting

sources of energy.

The present commercial drying methods are very energy inefficient; they are

designed to obtain the highest drying capacity with the lowest initial investment

cost; especially in countries where it is only possible to harvest once per year, in

16

the fall, like in Canada, the whole grain crop has to be harvested in a short period

of time, and dried as soon as possible to prevent spoilage. The basic principle for

removing water from grains is to expose the grain to airflow, thereby decreasing

its moisture content. The energy invested in evaporating the water is dissipated

to the air; this drying principle requires a theoretical minimum of 2500 kJ per kg

of water evaporated (water latent heat of evaporation at 0 °C and 1atm);

however, the practical value is between 3000 and 8000 kJ per kg (Gunasekaran

& Thompson, 1986).

More energy efficient methods for drying grain are available; unfortunately,

these are unreliable or expensive (Sauer, 1992); they have not reached, in the

last 20 years, the reliable and productive levels required by agro industry. Sauer

argues that solar power is a free energy source for drying grain; however, it is

weather dependent, and usually it is most needed when less sunshine is

available. Heat pumps reduce energy consumption, but the increase in the

equipment investment is similar to the reduction in the energy cost. Heat pumps

also require more drying time, which increases the risk of spoilage. Microwave-

vacuum can dry grain fast and with higher quality, but it requires more energy

than conventional process. Other unexplored alternatives are recovering energy

from the water vapor or extracting water inside the kernel in liquid form.

17

The fastest way to improve the grain drying efficiency is finding how the

traditional drying method, which involves blowing hot air, uses the energy

invested in it to identify better drying practices. The improvement task, of finding

the best dryer configuration, has been tried for decades by running field tests;

unfortunately, the results from different tests are not comparable due to the

importance of variable factors. There are too many factors that impact dryers’

performance, the main ones are pressure, ambient temperature and relative

humidity, initial grain moisture and temperature, retention time, and drying air

temperature. Controlling all these factors, to compare different improvement

strategies, is practically impossible, even in lab conditions. A computer

simulation, however, can predefine the required values for each one of the main

factors, and to achieve this, it is only necessary to write a number of equations

that describe the drying kinetics based on these factors.

A system of equations is to be written to simulate the operational

parameters of an actual drying equipment; this simulation helps to increase the

efficiency of the drying process. The system could be validated using thin layer

lab tests, and it could simulate the air flow through several grain layers. While

more efficient and cheaper technologies are developed, it is useful to provide

tools to dryer users and manufactures to help them achieve the most efficient

setup from their actual and existing equipment.

18

1.1. Hypothesis

Drying kinetics have been studied for many years, and several models have

been developed; however, these are limited in scope or very complex

(Gunasekaran & Thompson, 1986). Thin layer is the basic drying model, and the

deep bed model was developed based upon this concept. The air output of a thin

layer will be the incoming air for the next layer. Deep bed drying models have

been developed based on differential equations of thin layer models, and solved

using numerical methods like Runge- Kutta (Tórrez et al., 1998). Some models

are focus in the water diffusion inside grain (Aguerre & Suarez, 2004;

Samapundo et al., 2007); other models are based in the heat and mass balance

for the grain surface evaporation (Lopes et al., 2005). This result in models that

are useful only in specific conditions such temperature, airflow or moisture. It is

therefore hypothesized that it is possible to develop a system of equations useful

in a wider range of conditions by simulating the inside water migration and

surface water evaporation simultaneously. This system correspond with the finite

difference approach for the deep bed model based on the drying kinetics of thin

layer lab tests, to be solved through finite time steps.

In this model, each of the two main components of the system (grain and

air) is described as an array of numerical values representing the physical

properties of each element. A group of recurring equations, also known as

19

difference equations, are to be used to change air and grain arrays from a time

range of “t” to “t+1”. The air output from one grain element will be the incoming

air for the next one as noted earlier. With the above in mind, one can now

establish the present work objectives.

1.2. Objectives

The objective of this work is to develop a system of equations that links a

finite-element array of corn with the drying air, which allows us to identify how the

drying process can be improved.

1.2.1. Sub-objectives:

To validate a mass transfer equation that takes into account the

Vapor Pressure Deficit of air (VPD).

To develop three types of equations for corn drying: heat and mass

balance, empirical, and literature based.

To validate the model using thin layer tests with different

configurations of air flow, temperature, and moisture.

To build a deep bed model to simulate an In-Bin hot air dryer.

To simulate control strategies to improve In-Bin drying performance.

1.3. REFERENCES

Aguerre, R. J., & Suarez, C. (2004). Diffusion of bound water in starchy

materials: application to drying. Journal of Food Engineering, 64(3), 389-

395. doi: DOI: 10.1016/j.jfoodeng.2003.11.007

20

Gunasekaran, S., & Thompson, T. L. (1986). Optimal energy management in

grain drying. CRC Critical Reviews in Food Science and Nutrition, 25(1),

1-48.

Lopes, D. d. C., Martins, J. H., Neto, A. J. S., & Filho, A. J. S. (2005). Simulação

da secagem de grãos com baixas temperaturas utilizando-se o modelo de

Hukill: uma nova abordagem. exacta, 3, 85-93.

Raghavan, V. G. S., & Sosle, V. (2007). Grain Drying. In A. S. Mujumdar (Ed.),

Handbook of industrial drying (pp. 563-573). Boca Raton, FL ::

CRC/Taylor & Francis.

Samapundo, S., Devlieghere, F., Meulenaer, B. D., Atukwase, A., Lamboni, Y., &

Debevere, J. M. (2007). Sorption isotherms and isosteric heats of sorption

of whole yellow dent corn. Journal of Food Engineering, 79(1), 168-175.

doi: DOI: 10.1016/j.jfoodeng.2006.01.040

Sauer, D. B. (1992). Storage of cereal grains and their products. St. Paul, Minn.,

USA :: American Association of Cereal Chemists.

Sosle, V. (2002). A heat pump dehumidifier assisted dryer for agri-foods.

Tórrez, N., Gustafsson, M., Schreil, A., & Martínez, J. (1998). Modeling and

simulation of crossflow moving bed grain dryers. Drying Technology,

16(9), 1999 — 2015.

21

CHAPTER II.

2. LITERATURE REVIEW

2.1. Grain Storage

Grain has been a key link in the food chain throughout human history; its

drying and storage, then, are essential for human well being (Raghavan & Sosle,

2007). Safe storage time depends on the grain moisture and temperature. High

moisture and temperature enhance growth of mold, hence, increasing grain

spoilage. This correlation can easily be seen in Figure ‎2-1.

Figure ‎2-1: Allowable storage time for shelled corn. Adapted from

Sauer (1992).

0

20

40

60

80

100

120

140

18 20 22 24 26 28 30

Sto

rage

Tim

e -

Day

s

Moisture Content - %wb

27 °C (80°F)

15.6 °C (60°F)

10 °C (50°F)

4.4 °C (40°F)

22

Corn at 18% moisture content (wb) and 4.4 C can be safely stored for more

than 140 days; however, if its conditions are 30% (wb) and 27 C, spoilage can

occur in less than one day. Keeping grain moisture and temperature at low levels

is fundamental to increase its lifespan.

2.2. Energy demand for grain drying

Figure ‎2-2: Energy requirements for production of corn in the Midwestern

United States as a percentage of the total. Adapted from Brooker et al. (1992).

The drying of grain is absolutely essential before storage; however, grain

drying is an intensive energy demand process compared with the rest of the

grain production process. For example, in the Midwest of United States, 60% of

the energy required to produce corn is used to dry it (Brooker et al., 1992) (See

Figure ‎2-2). More significantly, “Crop drying requires a minimum of approximately

2.50 to 2.67 MJ/kg of water removed, depending on the temperature at which

DRYING 60%

TILLAGE 16%

PLANTING AND CULTIVATION

12%

HARVESTING 6%

TRANSPORT 6%

23

water is evaporated. However, actual energy requirements for evaporating water

from grain range from 3 to 8 MJ/kg of water” (Gunasekaran & Thompson, 1986).

Any reduction in the energy use for grain drying will be an important improvement

for the agro industry.

2.3. Grain Drying Theory

2.3.1. Desorption

To understand how grains are dried, one must first understand how water is

held inside grain. Grains are hygroscopic materials; they retain water through

sorption forces, absorption or adsorption. Absorption assimilates water in liquid

form into the solid structure of the grain; adsorption is the adherence between

grain and water surfaces.

To dry grains, moisture must be desorbed. Desorption occurs when the

forces that pushes water outside the grain are higher than the sorption forces. In

the grain drying process with air, sorption and desorption forces are related to a

number of key factors: temperature, pressure, grain structure, moisture content,

air relative humidity, and air velocity (Aguerre & Suarez, 2004; Kaymak-Ertekin &

Gedik, 2004; Martinez-Vera et al., 1995; Samapundo et al., 2007).

2.3.2. Equilibrium Moisture

When sorption and desorption forces are equal, the grain moisture remains

constant; this is known as equilibrium moisture (Me). Water activity (aw),

24

temperature, and pressure are the main factors that define the Me (Samapundo

et al., 2007); however, the equations found in the literature only use water activity

and temperature to calculate the equilibrium moisture, making it necessary to

take into account the pressure factor. In the grain drying with air, water activity is

the centesimal part of the air relative humidity (RH). Kaymak-Ertekin and Gedik

(2004) relate water activity with moisture content through moisture sorption

isotherms; “For food materials these isotherms give information about the

sorption mechanism and the interaction of food biopolymers with water”.

Several equations have been developed to describe isotherms; however,

most of them change the equation’s constants depending on the temperature

range. Modified Henderson’s equation [‎2.1] represents the isotherms for corn as

a function of temperature without changing the equation’s constants (Thompson

et al., 1968). Samapundo et al. (2007) have used the gravimetric method to

determine the isotherms for yellow dent corn, and they fitted the test results with

Guggenheim-Andersen-de Boer (GAB), Oswin, Halsey, Henderson, Chung-Pfost

and polynomial models. These have different constants depending of the

temperature range, making rather complex its simulation in a long range. In

Figure ‎2-3, Samapundo’s lab test results are compared with Henderson’s

modified equation with the constants used by Lopes et al. (2005); the advantage

of this equation is that its constants do not change with temperature.

25

Me 0.01ln 1 RH-( )

8.65- 105-

T 49.81+( )

1

1.8634

=

[‎2.1]

Figure ‎2-3: Comparing Samapundo et al. (2007) desorption data with

modified Henderson’s equation

2.3.3. Diffusion

Water is distributed inside the grain mass, and it has to reach the grain

surface to be removed by air. This diffusion process in capillary moist solids

depends on the nature of the material, moisture content and moisture bonding

(Aguerre & Suarez, 2004). Partial pressure difference determines diffusion;

moisture migrates from high partial pressure zones to lower ones. When the air

relative humidity that surrounds a kernel is lower than the RH for equilibrium

moisture at determined temperature, the water on the grain surface is evaporated

into the air. This reduction in the surface water creates a partial pressure

0.1

0.15

0.2

0.25

0.3

30% 40% 50% 60% 70% 80% 90% 100%

Gra

in E

qu

ilib

riu

m M

ois

ture

db

Air Relative Humidity

25C 30C 37C

25 30 37

T ( C) 25 30 37

Samapundo

Henderson

26

difference within the kernel which drives the diffusion process associated with

water.

2.3.4. Vapor Pressure Deficit (VPD) and the Differential (VPDI)

Vapor Pressure of the air moisture (vpair) is a key factor for drying of grass

in swaths (Haghighi, 1990) and grains (Fenton, 1941); however, it is not taken

into account in drying models; the author could not find an equation that relates

vpair with grain drying in the scientific literature. There are two main concepts

related with vpair that are important to understand the drying phenomena. The

first is Vapor Pressure Deficit (VPD) of air; the second is Vapor Pressure

Differential (VPDI) between grain surface and air.

VDP is the difference between the Saturation Vapor Pressure (vpsat) and the

Vapor Pressure of air (vpair). According with Prenger and Ling (2000) “Vapor

pressure deficit (VPD) is the difference (deficit) between the amount of moisture

in the air and how much moisture the air can hold when it is saturated”, and it is

mainly used to control greenhouse condensation (Figure ‎2-4). vpsat is a function

of temperature; equation [‎2.2] describes this relation. vpair is found based on air

temperature and RH; this relation is part of a psychometric chart (Figure ‎2-5).

VPDI is the difference of the pressure between the water vapor in grain and

the vapor in air. This pressure difference leads the migration phenomena, and it

is described by the Fick’s law of diffusion. Gunasekaran & Thompson (1986)

27

recognize the importance of VPDI by citing two key principles from Fenton

(1941):

Grain gains or loses moisture because of the vapor pressure

difference between the grain itself and the surrounding air. If the vapor

pressure of the grain is higher than the pressure in the space

surrounding the grain, moisture will flow out of the grain. If the reverse is

true, moisture will flow into the grain and there will be a gain in moisture

content.

Figure ‎2-4: “Vapor Pressure Deficit (VPD) enhances or inhibits the crop’s

ability to transpire”(Prenger & Ling, 2000).

Dry air

Water Vapor

Higher VPD- Transpiration is

unhindered- Plants can dry out

Lower VPD- Transpiration is stifled by

inability to release moisture to the air

- Moisture on plant surfaces leads to disease problems

28

[‎2.2]

Figure ‎2-5: Modified psychometric chart showing the vapour pressure

values (Prenger & Ling, 2000).

vpsat

77.3450 0.0057 T 273.15+( )+7235

T 273.15+( )-

8200 T 273.15+( )=

29

The rate at which a grain gains or loses moisture is roughly

proportional to the magnitude of the vapor pressure differential which

prevails between the grain and the surrounding space. This rate is

affected by the resistance to movement of moisture vapor set up by

surface layers of the grain.”

Vapor Pressure Differential (VPDI) is not possible to calculate because

there is no way to measure the vapor pressure in the grain; however, VPD can

be calculated, and it can be expected that higher VPD values increase moisture

transfer from grain to surrounding air.

2.3.5. Drying Periods

The drying grain process has two phases, constant and falling rate. The first

one is proportional to the air capacity to carry moisture, while the second one is

limited by the water diffusivity inside the kernel. Cereals drying process occurs

mainly in the falling rate period (Brooker et al., 1976).

2.3.6. Heat and Mass Transfer

Grain drying with air involves heat and mass interchange between grain and

air. The incoming air brings energy to the process, and the exhaust air carries the

water removed from the kernels. This transfer occurs at the grain surface. Water

inside the kernel is in liquid form, but air carries moisture as vapor. The water

30

phase changes from liquid to vapor, absorbing heat. This heat comes from the

temperature reduction of grain and air. Aguerre and Suarez (2004) describe the

drying process as a simultaneous relation between heat and mass transfer; this

makes its modeling rather complicated.

Figure ‎2-6: Constant and falling rate periods in thin-layer drying of high

moisture grain (Gunasekaran & Thompson, 1986)

2.4. Drying Models

2.4.1. Thin Layer Model

In the thin layer test a constant air flow goes through a layer having a single

kernel depth. The air conditions (pressure, flow, temperature, and humidity)

31

remain constant during the drying time, and the sample weight is taken

periodically to determine the moisture content. The moisture content vs. time

data is plotted to obtain the drying curves for a specific grain in determined air

conditions as shown in Figure ‎2-6. Sometimes, thin layer lab tests also measure

the conditions of the exhausted air, temperature and moisture; these are useful

to validate the heat and mass balance equations. One of this setup is shown in

Figure ‎2-7.

Figure ‎2-7: Thin layer test setup used by Farkas and Rendik (1997)

The Thin Layer is the basic drying lab test for grains; this is mainly used to

find the constant values for the drying or rewetting empirical equations. Misra &

Brooker (1980) have listed several of these equations “Chittenden, 1961; Chu,

1966; Muh, 1974; Page, 1949; Rodriguez-Arias, 1956; Rugamayo, 1978;

32

Sabbah, 1968; Thompson, 1967; Troeger, 1967; del-Giudice, 1959”. However,

they point out these equations to have several limitations such as: narrow

temperature range, not considering the air flow, or changing the parameters for

the same drying process. “These partial differential equation models did not

become very popular because of their complexity…” (Gunasekaran & Thompson,

1986). Aguerre and Suarez (2004) found that “to reduce the complexity of the

problem some assumptions are usually performed. One of them is to assume

that drying is an isothermal process, where only one parameter, the diffusion

coefficient, is necessary to describe the drying kinetics”. An accurate thin layer

model can be the basis to a consistent deep bed model

2.4.2. Deep Bed Model

A series of several thin-layers compose a Deep Bed (Gunasekaran &

Thompson, 1986; Misra & Brooker, 1980). In a Deep Bed Model, each thin layer

modifies the quality of the air; therefore, only the first thin layer receives an

invariable air flow reducing its moisture faster than subsequent layers. Hukill

(1947) proposed an equation system to calculate the moisture at any location

and time in the deep bed (Figure ‎2-10); it has been used by several authors to

describe the drying process (Gunasekaran & Thompson, 1986; Lecorvaisier et

al., 2010; Lopes et al., 2005). The mathematical approach can be seen in

33

Figure ‎2-8 and Figure ‎2-9. The deep bed model is the basic concept to simulate

grain dryers.

Figure ‎2-8: Illustration of a deep bed as a series of thin layers. (T and H are

temperature and humidity ratio of drying air, respectively. Subscripts n, n + 1, and

n + 2 represent the corresponding grain layer.) from Morey et al. (1978).

Figure ‎2-9: Schematic diagram of basic simulation approach, adapted from

Thomson et al. (1968).

2.5. Grain Dryers

Before agriculture industrialization, corn ears were dried by being hung in

barn lofts; in the last century, rapidly growing population has pushed agro

Thin Layer of Corn

Drying Air

Temp. = Ta(°C)

Humidity Ratio = WR (g/kg dry air)

Exhaust Air

Temp. = Ta- ΔTa (°C)

Humidity Ratio = WR + (g/kg dry air)

Corn Before Drying

Moist. Content = Ma (% db)

Temp = Tg (°C)

Corn After Drying

Time of Δt .

Moist. Content = Ma - ΔMa (% db)

Temp = Tg - ΔTg (°C)

34

industry to develop mechanical methods for drying grains on a large scale

(Raghavan & Sosle, 2007). Today, most commercial dryers use fossil fuels to dry

grain quickly; choosing the adequate dryer depends mainly on the amount of

grain to be dried and not necessarily on the required efficiency of the process.

However, there are a few more efficient alternatives that use biomass, solar

energy, or energy recovery.

Figure ‎2-10: Dimensionless drying rate curves, (Hukill, 1947)

2.5.1. Commercial Dryers

All the commercial dryers are based on a hot air current going through the

grain to remove moisture. The two main factors that characterize commercial

35

dryers are air temperature and grain flow type. Air temperature range is typically

between ambient air and 140 °C; however, it can reach 315 °C in some dryers.

Flow processes are batch, or continuous. Drying selection depends mainly on the

annual grain production of the farm. In Table ‎2-1, the main commercial dryers are

summarized. A small scale dryer can be found in the In-Bin Batch Dryer,

presented in Figure ‎2-11, and one of the most complex is the continuous-flow

two-stage concurrent flow dryer, presented in Figure ‎2-13. The dryer type that is

most commonly used in agro industry is the continuous flow dryer with an air

recirculating system, presented in Alternative Dryers.

Figure ‎2-11: A typical batch dryer bin (Raghavan & Sosle, 2007)

36

2.5.1. Alternative Dryers

In order to reduce the fossil fuel consumption in the grain drying industry,

several alternatives have been tested such as solar energy, heat pumps and

biomass. However, farmers generally have not implemented them because of

their high cost and low reliability (Sauer, 1992). The following describes their

function and shortcoming.

Figure ‎2-12: A typical stationary continuous flow dryer with an air

recirculating system (Raghavan & Sosle, 2007).

Table ‎2-1: Dryer Types, from (1Bakker-Arkema et al., 1981; 2Raghavan & Sosle, 2007; 3Sauer, 1992)

Dryer Type Grain / Air

Flow

Air Drying Time

Comments Temperature

(°C)

m3/min per

ton of grain

In B

in

See F

igure

‎2-1

1

2In Storage /

From floor

Natural Air 3 – 11 Several weeks

- Recommended for farms with 22 to 60 tons of annual production

4 - 12 over Ambient

1.5 – 4 1 to 3 weeks

- Supplemental heat.

- Recommended for farms with 60 to 445 tons of annual production

2,3Batch /

Counter 35 – 65 10 – 25 12 - 24 hours

- For annual productions up to 500 tons.

- Moisture of the dried batch varies around 5% from bottom to top.

3Continuous

/

Counter

Up to 80 10 – 25 6 – 12 hours


- Grain recirculation is used with the last portion of grain that will be storage in the bin.

Hig

h T

em

p.

Colu

mn

See

Fig

ure

‎2-1

2

3Batch /

Cross Flow

80 - 140 80 - 100 3 - 5 hours


- The great drying rate produces grain quality problems

2Continuous

/ Cross Flow

- For annual productions above 3750 tons.

- The great drying rate produces grain quality problems

Con

cu

rre

nt

Flo

w

See F

igure

‎2-1

3 1

Recycling Batch/

Concurrent 150 - 315

12 m3/min

per ton/hr 3 - 5 hours

- For annual productions above 1250 tons.

- The continuous process has three air temperatures, around 290, 230 and 180 °C

- The hottest air is in contact with the wettest grain; this reduces the grain breaking susceptibility compare with high temperature column dryers.

1,2Continuous

/Concurrent 150 - 290

Figure ‎2-13: Schematic of a continuous-flow two-stage concurrent flow dryer

by Ferrell-Ross CCF (Bakker-Arkema et al., 1981).

39

2.5.1.1. Solar Energy

“Solar energy for crop drying is weather dependent and may be least

successful during the period of the years when it is needed most” (Sauer, 1992).

The drying time is usually too long in a solar dryer, and the product can be

damaged when it is not covered (Perumal, 2007). A solar dryer can increase

temperature between 15 and 20 °C (Bonaparte, 1995). There are two types of

solar dryers: direct and indirect. The second one uses solar collectors to heat air,

and the hot air is moved through the grain bed as illustrated in Figure ‎2-14. In

the first type, the sunlight heats directly the grain. Perumal (2007) compared the

drying kinetics of tomatoes in solar cabinet and vacuum assisted solar dryers, as

illustrated in Figure ‎2-15 and Figure ‎2-16, and found that “…The higher diffusion

values in both the solar cabinet and vacuum assisted solar dryers were due to

the higher temperature and lower relative humidity which prevailed in the drying

chamber compared to open sun drying…”

2.5.1.1. Heat pump

A heat pump moves thermal energy from a cold source to a hotter sink. This

process requires an input of energy which drives the process. The advantage of

this process is that for each unit of energy invested, it is transferred several times

to the heat sink; this ratio is known as the coefficient of performance (COP). The

most common type of heat pump is a reversed refrigeration cycle. First, a fluid, in

40

gaseous state, is compressed to increase its pressure and temperature. Then, a

condenser releases the energy to the heat sink and the fluid returns to its liquid

phase but remains warm. When the fluid is in liquid form, a strangle valve

reduces its pressure. Finally, the low pressurize liquid is evaporated; this process

extracts energy from the surroundings. And the process starts again.

Figure ‎2-14: Solar dryer with collectors used by Stratford (1984)

Sosle (2002) tested a Heat Pump Dehumidifier (HPD) for drying apples

under several conditions; the basic scheme of the process can be seen in

Figure ‎2-17. These tests gave specific energy consumptions (SEC) higher than

the energy consumption of the commercial dryers. While lower the SEC value is,

the drying process is more energy efficient. The inverse of the SEC is the

Specific Moisture Extraction Rate (SMER), which is presented in Figure ‎2-18. In

the graphic, the two modes with the lowest energy consumtion, “Hor Air + HPD”

41

at 45 and 65 °C, did not take into account the energy for heating the air; this

extra energy increases the SEC to 6.58 and 4.68 MJ per kg of water removed

respectively.

Figure ‎2-15: Schematic view of solar cabinet and open drying setup

(Perumal, 2007)

Figure ‎2-16: Vacuum assisted solar and open sun drying set up for tomato

slices (Perumal, 2007)

42

2.6. Conclusion

Industrial grain drying is an important process in the human food chain, but

its impact, from energy consumption and emissions, on food quality and

environment has to be reduced. New technologies have been developed to

improve the drying processes; however, their cost and/or low productivity have

restricted their implementation to the agro-food industry. While more efficient and

economic drying equipment are being developed, it is important to optimize the

present operational dryers.

Figure ‎2-17: Two configurations of the heat exchangers vis-à-vis the drying

chamber. The dark, thick streams indicate airflow (Sosle, 2002).

43

Figure ‎2-18: Comparison of energy consumption among the different modes

drying (Sosle, 2002).

2.7. References




Bakker-Arkema, F. W., Rodríguez, J. C., & Brook, R. C. (1981). Grain quality and

energy efficiency of commercial grain dryers. American Society of

Agricultural Engineers, 81-3011, 8.

Bonaparte, A. (1995). Solar drying of cocoa beans (Theobroma cacao) in St.

Lucia. M.Sc. Thesis, McGill University, Macdonald Campus, Ste-Anne-de-

Belleveu QC. (ix, 85 leaves)

Brooker, D. B., Bakker-Arkema, F. W., & Hall, C. W. (1976). Drying Cereal

Grains. 265 Seiten, 124 Abb., 32 Tab. The Avi Publishing Company, Inc.,

Westport, Connecticut 1974. Food / Nahrung, 20(1), 95-96. doi:

10.1002/food.19760200143

44

Brooker, D. B., Bakker-Arkema, F. W., & Hall, C. W. (1992). Drying and storage

of grains and oilseeds. New York :: Van Nostrand Reinhold.

Farkas, I., & Rendik, Z. (1997). Intermittent thin layer corn drying. Drying

Technology: An International Journal, 15(6), 1951 - 1960.

Fenton, F. C. (1941). Storage of grain sorghums. Agric. Eng.(22), 185.



1-48.

Haghighi, K. (1990). Finite element simulation of the thermo-hydro stresses in a

viscoelastic sphere during drying. Drying Technology, 8(3), 465-498. doi:

10.1080/07373939008959896

Hukill, W. V. (1947). Basic principles in drying corn and grain sorghum. Agric.

Eng, 28, 335-338, 340.

Kaymak-Ertekin, F., & Gedik, A. (2004). Sorption isotherms and isosteric heat of

sorption for grapes, apricots, apples and potatoes. Lebensmittel-

Wissenschaft und-Technologie, 37(4), 429-438. doi:

10.1016/j.lwt.2003.10.012

Lecorvaisier, E., Darche, S., da Silva, Z. E., & da Silva, C. K. F. (2010).

Theoretical model of a drying system including turbulence aspects.

Journal of Food Engineering, 96(3), 365-373. doi: DOI:

10.1016/j.jfoodeng.2009.08.008




Martinez-Vera, C., Vizearra-Mendoza, M., Galin-Domingo, O., & Ruiz-Martinez,

R. (1995). Experimental Validation of a Mathematical Model for the Batch

Drying of Corn Grains. Drying Technology, 13(1-2), 333-350. doi:

10.1080/07373939508916956

45

Misra, M. K., & Brooker, D. B. (1980). Thin-Layer Drying and Rewetting

Equations for Shelled Yellow Corn. Transactions of the ASAE, 0001-

2351/80/2305-1254$02.00, 1254-1260.

Morey, R. V., Keener, H. M., Thompson, T. L., White, G. M., & Bakker-Arkema,

F. W. (1978). The present status of grain drying simulation. American

Society of Agricultural Engineers, paper No. 78-3009.

Perumal, R. (2007). Comparative performance of solar cabinet, vacuum assisted

solar and open sun drying methods. Master of Science Thesis, McGill

University, Montreal, Canada.

Prenger, J. J., & Ling, P. P. (2000). Greenhouse Condensation Control-

Understanding and Using Vapor Pressure Deficit (VPD) Fact Sheet

(Series) AEX-800. Columbus, OH: The Ohio State University Extension.










Sosle, V. (2002). A heat pump dehumidifier assisted dryer for agri-foods. Ph.D.

Thesis, McGill University, Montreal, QC.

Stratford, C. J. (1984). A solar wall and roof air preheater for in situ hay drying for

the Province of Quebec. M.Sc. Thesis, McGill University, Montreal, QC

Canada.

Thompson, T. L., Peart, R. M., & Foster, G. H. (1968). Matllematical Simulation

of Corn Drying - A New Model. Transactions of the ASAE, 11(4), pp. 582-

586.

46

CONNECTING TEXT

The present paper deals with the mathematical model development of a thin

layer of grain, and a deep bed; and it shows a sensibility study of several

parameters that impact the in bin batch dryer performance: ambient temperature

and relative humidity, air flow and temperature, and initial grain moisture. This

paper was presented at the 46th Annual Convention of Indian Society of

Agricultural Engineers (ISAE) and International Symposium on Grain Storage

held February 27-29, 2012 at College of Technology, G.B. Pant University of

Agriculture and Technology, Pantnagar, Uttarakhand-India.

CHAPTER III

3. MODELING OF GRAIN DRYERS:THIN LAYERS TO DEEP BEDS

Díaz Martínez Jorge Alonso, Yvan Gariepy and Vijaya Raghavan

Bioresource Engineering, McGill University, 21 111, Lakeshore Road, Ste. Anne de Bellevue, Québec, Canada. [email protected]

Keywords: Grain Drying, Corn, Model Simulation, Thin Layer, Deep Layer, VPD

3.1. Abstract

A System of Equations for Drying with Air (SEDA) was developed to predict

the drying kinetics of grains, and its energy consumption. SEDA is based on heat

and mass transfer between dried grain and air; it simulates an air flow through

several thin layers of corn. Four independent parameters were considered to

predict the drying kinetics: air temperature, initial grain moisture content, relative

humidity (RH), dry air mass flow. A new factor included in SEDA is the Vapor

Pressure Deficit of air (VPD); it combines air RH and Temperature to regulate

corn moisture migration. Results from SEDA permitted to identify how energy

invested is distributed during grain drying. Grain heating, water evaporation, and

humid air heating are the main energy sinks of the drying process; however, in

spite of energy associated with evaporation being a constant value, the total

energy consumption changed considerably with ambient conditions. The energy

efficiency was higher when the exhaust air was hotter and hence it was able to

support a higher humidity ratio before it got saturated. This implied a higher

48

grain temperature leading to grain quality changes. SEDA was validated using

eleven thin layer test data sets available from the literature with an R2 higher than

0.84 for ten tests during the initial few hours of drying. Commercial dryers

usually take less time than this to dry corn. Results for deep layer drying were

validated with in-bin batch dryers’ data; drying time and moisture differences

were compared which gave consistent results.

3.2. Introduction

Alternative energy sources and more efficient processes are required to

reduce the dependency and pollutant impact of fossil fuels; in both cases, grain

drying has an important role. As an alternative energy source, grain is one of the

main inputs for biofuels production, but the grain drying process consumes large

amounts of energy (Raghavan & Sosle, 2007). Several new drying methods are

being developed; however, hot air drying remains as the main agro industrial

process to reduce grain moisture before storage. Even when air can be warmed

up with sun light or biomass, fossil fuel equipments are the most reliable and

cheapest alternative (Sauer, 1992), and this has not changed in the last 20

years. While new drying technologies reach the level of productivity desired by

the agro industry, it is important to develop tools that help to increase the energy

efficiency of the grain drying process with hot air.

49

Several equations have been developed to predict the drying kinetics of

grains; however, “none of the diffusion-type or empirical drying equations

presented illustrate the drying process of cereal grain precisely over the full

moisture content range” (Brooker et al., 1992). Diffusion, partial differential

equation, simultaneous heat and mass transfer, logarithmic, equilibrium, semi

theoretical, and empirical are the mathematical simulation models for grain drying

(Gunasekaran & Thompson, 1986). The more precise models become very

complex to resolve, and some assumptions are made to simplify their complexity

(Brooker et al., 1992; Gunasekaran & Thompson, 1986; Jumah, 1995). Jumah

(1995) found two simplified assumptions in the previous theoretical studies, the

first is to assume a thermal equilibrium between air and grain, and the second

one is that grain does not have an internal temperature gradient.

System of Equations for Drying with Air (SEDA) uses several equations to

represent grain drying kinetics. Such equations are not complex, but can result in

greater accuracy. There are three types of equations: heat and mass balance,

empirical, and literature based. The simplicity of the model allows for finding

solutions with spreadsheet software, and can run several simulations with

different drying conditions by writing a code in a general purpose programming

language.

50

Me 0.01ln 1 RH-( )

8.65- 105-

T 49.81+( )

1

1.8634

=

3.3. Materials and methods

SEDA is based on the energy and mass balance between air and several

thin layers of grain, see Figure ‎3-1. The exhaust air from a thin layer is the

incoming air for the next one. On each layer, air changes temperature and

moisture depending on the initial grain conditions.

Figure ‎3-1: Schematic diagram of basic simulation approach, adapted from

Thomson et al (1968)

3.3.1. Equations used

Equilibrium Moisture (Me):‎ Modified‎ Henderson’s‎ equation [‎3.1] (Lopes et al.,

2005) determines the equilibrium grain moisture; it is a function of the air

temperature and relative humidity.

[‎3.1]

Surface Moisture (Ms): During the drying process, the grain surface moisture

value is between the average grain moisture (Ma) and the equilibrium moisture. It

is dependant on the grain diffusivity which changes with grain temperature and

moisture. The empirical equation [‎3.2] was developed to express this relation

Thin Layer of Corn

Drying Air

Temp. Tn (°C)

Water Vapor. WVn (g)

Exhaust Air

Temp. Tn+1 = Tn- ΔT‎(°C)

Water Vapor. WV n+1 = WVn + WT (g)

Corn Before Drying

Moist. Content = Ma (% db)

Temp = Tg (°C)

Corn After Drying

Time‎of‎‎Δt‎‎‎‎‎‎‎‎‎.

Moist. Content = Ma - ΔMa (% db)

Temp = Tg - ΔTg‎(°C)

51

Ms Me MA Me- MA

MC

kd Dt Dm+

Dt 11

1 Dtc Tg+-=

Dm 11

1 Dmc Ma+-=

[‎3.2]

Temperature Correction Factor (Dt): Grain temperature drives moisture transfer.

Gunasekaran and Thompson (1986) have‎noticed‎that‎“…the‎grain‎temperature‎

has‎ a‎more‎ profound‎ effect‎ on‎ the‎ vapor‎ pressure‎ than‎ the‎moisture‎ content”.‎

This is particularly true when moisture content is more than 20%. Therefore,

temperature of the grain is considered to be the greatest single factor in grain

drying. Temperature correction factor, represented by equation [‎3.3], was

assumed‎“0”‎when‎grain‎temperature‎was‎0°C,‎and‎reached‎“1”‎asymptotically‎as‎

grain temperature increased. Water diffusion can occur in freezing temperatures,

but in this condition, commercial grain dryers do not work.

[‎3.3]

Moisture correction factor (Dm): Moisture diffusion rate is dependent on moisture

content (Aguerre & Suarez, 2004; Thompson et al., 1968). In equation [‎3.4], the

moisture‎correction‎ factor‎ is‎equal‎ to‎ “0”‎when‎moisture‎ is‎ “0”,‎and‎approaches‎

asymptotically‎ to‎ “1”‎ as‎ moisture‎ increases.‎ It‎ was‎ assumed‎ that‎ moisture‎

reaches faster than the grain surface when there was more water inside the

grain.

[‎3.4]

Exhaust air temperature: The exhaust air temperature is between initial air and

grain temperatures; it varies depending on the mass ratio of these two elements.

Equation [‎3.5] shows this relation.

52

Tan 1+

Tg Tan

Tg-

11

1 0.1Tc

ma

mg

+

-

+=

WT STc Ms Me- mg Δ t STVPD STaf=

STVPD VPDSTVPD1 STVPD2+

STaf 11

STaf1

ma

10Δ t mg STaf2+

-=

WVan 1+

WVan

WT+=

[‎3.5]

Surface Water Transfer: Equation [‎3.6] shows the predicted water transfer

between grain and drying air. It is directly proportional to the difference between

surface and equilibrium moisture. There are two correction factors; one for the air

Vapor Pressure Deficit (VPD) and the other for the air flow.

[‎3.6]

Vapor Pressure Deficit Correction Factor STVPD: VPD represents how much

water the air can hold before reaching saturation. In equation [‎3.7], the correction

factor is proportional to VPD.

[‎3.7]

Air flow correction factor STaf: Larger amounts of air produces faster drying rates;

however, at some point, the moisture transfer do not increase significantly with

increased airflow. Equation [‎3.8] represents‎this‎situation.‎The‎factor‎is‎“0”‎when‎

airflow‎is‎“0”,‎and‎aproaches asymptotically‎to‎“1”‎as‎airflow‎increases.

[‎3.8]

Mass Balance

Air Humidity Ratio HR: The lost moisture from corn increases the air humidity, as

can be illustrated by equations [‎3.9] and [‎3.10].

[‎3.9]

53

HRn 1+

WVan 1+

ma

=

mwn 1+

mwn

WT-=

Ha ca TaHR

1000cv Ta lw+ +

ma=

Hgn 1+

Hgn

Han

+ Han 1+

-=

Tgn 1+

Tgn

Hgn 1+

Hgn

-

cg mg cw mwn 1+

++=

[‎3.10]

Grain Moisture Ma: The moisture taken by air reduces the water inside the grain,

see equation [‎3.11]; therefore, the grain moisture decreases Equation [‎3.12].

[‎3.11]

[‎3.12]

Heat Balance - Air and grain Enthalpy: Once the air Temperature and HR are

estimated, its enthalpy is calculated, see equation [‎3.13]. This allows to balance

the energy of the system to obtain the grain enthalpy (Equation [‎3.14]), and

temperature (Equation [‎3.15]).

[‎3.13]

[‎3.14]

[‎3.15]

Center Moisture: This is an estimation of the moisture distribution inside the

kernel by keeping a record of the center’s grain moisture (MC) using Equation

[‎3.16]. MC tries to reach the average grain moisture even if there is no water

transfer‎on‎kernel’s‎surface.

Man 1+

mwn 1+

mg

=

54

Mcn 1+

Mcn

Mcn

Man

-

11

1 t Dc2+-

-

[‎3.16]

3.3.2. Model Validation

3.3.2.1. Thin Layer:

Thin layer lab tests for yellow dent corn found in scientific literature were

used for the SEDA validation; these had to report several drying conditions. In

the scientific literature there are several thin layer tests reported; however, they

report only the grain moisture curve versus time, and the air temperature. The

additional required parameters are: drying air humidity and flow rate. Misra and

Brooker (1980) reported all these information in eight thin layer tests (Figure ‎3-2).

Martinez-Vera et al (1995) reported the results for a batch dryer of 1200 g of corn

with three different air temperatures (50, 75, and 120 °C,

Figure ‎3-3); however, they did not report the room temperature and RH.

These were assumed as 20°C and 40% respectively. The SEDA parameters

obtained for corn can be seen in Table ‎3-1.

Table ‎3-1: SEDA Parameters for corn.

kd Dc2 (s-1) Dtc (°C-1) Dmc Tc STc ( s

-1) STVPD1

(kPa-1)

STVPD2 STaf1 (s) STaf2

70 5x10-5 0.008 0.03 10 0.053 0.7 10 15 2.2

SEDA is written in Microsoft Excel for thin layer validation. Lab tests and

equation results were plotted simultaneously, and by iteration process the

constants that match most of the lab tests were found. To find the air/grain mass

55

ratio, a bulk density of 721 kg m-3 (ANSI/ASAE, 1998), and a kernel diameter of

7.8 mm (Pabis & Henderson, 1962) were used. For all the thin layer simulations,

the initial grain temperature was assumed at 20°C.

The physical parameters for water were: liquid heat capacity (cw) 4.18

kJ·kg-1·K-1, vapor heat capacity (cv) 1.84 kJ·kg-1·K-1, latent heat of evaporation

(lw) at 0°C 2501 kJ·kg-1. Drier air heat capacity (ca) 1.006 kJ·kg-1·K-1; and Heat

capacity of dry grain mass (cg) 1.885 kJ·kg-1·K-1 (Jayas & Cenkowski, 2007).

3.3.2.2. Deep Bed

A computational program was written in Agilent VEE Pro to simulate

several thin layer elements represented by SEDA. Each element had 1 kg of dry

mass of corn. The results were plotted to see the changes with time of moisture,

temperature, and energy demand for an in-bin batch dryer. The initial grain

temperature was assumed at 5 °C above ambient temperature, and the target

moisture was fixed at 0.176 db (15 % wb). The drying effect of the end cooling

phase was considered when the ambient RH was less than 70% (Brooker et al.,

1974). The moisture transfer between grain and air was limited when the

resultant air RH was higher than 100%.

SEDA does not take in to account the heat losses. These are mainly

through bin walls, and increase the energy demand. Heat losses are very specific

of the type of bin, and its insulation. Therefore, to estimate the heat loss impact in

56

the drying process, it is necessary to have the performance data from the specific

dryer.

3.4. Results and discussion

The simulations were ran at three time intervals (Δt), 1, 10 and 60 seconds.

As expected, the 1 second simulation gave the most accurate results, but it is

with a higher computational time. The final results from the 60 second simulation

were acceptable and fast. However, at the beginning of the simulation, the data

reported was not consistent, and it highly fluctuated. This was noticed mainly in

the tests with hot air while the temperature difference between air and grain was

high. Once the grain temperature was near to the air temperature, the results

were consistent. Finally, the 10 second simulation gave good results in fair time;

hence it was selected to run all the tests.

3.4.1. Thin Layer

In ten of the eleven lab tests, Figure ‎3-2 and

Figure ‎3-3, the R-square was higher than 0.84; see Table ‎3-2 and

Table ‎3-3. These were obtained with simulation results at air temperatures and

RH between 16 and 120°C, and 4.8 and 83.2% respectively. The only test, where

the R2 was not relevant was the one with a very low air temperature (2°C). In this

case, R2 was negative (-0.146); the average of the data gives a closer result than

the one obtained from the simulation. SEDA is based on a thermo-dynamical

57

balance, and at 2°C, air does not have enough energy to be the main factor that

can drive the drying process. The grain respiration could be the factor that leads

the drying process at low temperatures.

Figure ‎3-2: Corn thin layer test results taken from Misra and Brooker (1980),

and simulation curves from SEDA

VPD is one of the main factors that drives drying at higher grain moisture.

While grain moisture is far away from its equilibrium moisture (Me), high VPD

values produce higher drying rates. However, when grain moisture is closer to its

Me, the sorption forces inside the kernel limit the drying process.

High humidity simulation, number 3 in Figure ‎3-2 and Table ‎3-2, shows a

moisture increase in the first minutes because warm and humid air touches grain

at 20 °C. The air goes below its dew point, and condensation on grain surface

occurs.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0 120 240 360 480 600

Gra

in M

ois

ture

(d

b)

time (min)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0 120 240 360 480 600

Gra

in M

ois

ture

(d

b)

time (min)

0 120240360480600

Gra

in M

ois

ture

(d

b)

time (min)

3

4

5

8

0.00.10.20.30.40.50.6

0120240360480600

Gra

in M

ois

ture

(d

b)

time (min)

1

2

6

7

58

Table ‎3-2: Drying conditions and R2 value for the thin layer tests and model curves shown in Figure ‎3-2.

Number

1 2 3 4

Description

High

Temperature

Low

Temperature High Humidity Low Humidity

Temperature °C 71.0 2.22 50 50

RH % 11.8% 80.0% 83.2% 4.8%

Speed m/s 2.295 0.84 0.8 0.8

Coefficient of

determination R2 0.980 -0.146 0.990 0.836

VPD kPa 27.2 0.1 1.9 11.7

Me db 0.038 0.237 0.176 0.026

Number 5 6 7 8

Description High Moisture Low Moisture High Airflow Low Airflow

Temperature °C 27 32 21 16

RH % 43.0% 42.0% 83.0% 50.0%

Speed m/s 2.295 2.295 2.295 0.025

Coefficient of

determination R2 0.991 0.919 0.978 0.997

VPD kPa 2.0 2.7 0.4 0.9

Me db 0.109 0.104 0.211 0.133

Figure ‎3-3: Corn batch drying test results taken from Martínez-Vera et al.

(1995), and simulation curves from SEDA

0.0

0.1

0.2

0.3

0.4

0.5

0 100 200 300

Gra

in M

ois

ture

(d

b)

Time (min)

50 °C

75 °C

120 °C

59

Table ‎3-3: Drying conditions, and R2 values for the batch drying tests and its model curves are shown in

Figure ‎3-3.

Temperature °C 50 75 120

Specific Air Flow m3/min-ton of dry corn 1177 1146 1196

Coefficient of determination R2 0.984 0.978 0.987

3.4.2. Deep bed (in bin batch dryer- study case)

Figure ‎3-4 to Figure ‎3-7 are plotted for in-bin batch dryer cases. Drying

conditions are: ambient temperature 10 °C, ambient RH 80%, initial grain

moisture 0.333 db (25% wb), final grain moisture 0.176 db (15% wb), bed depth

96 cm, air flow 25 m3·min-1·ton-1 of dry corn mass, drying air temperature 50 °C,

air flow direction “up”. Hot air starts to dry the bottom layer at the onset of the

process; however, the top layer increases its moisture due to condensation. The

air gets wet in the first layers; when it reaches the top layers, the cold grain cools

the air down below its dew point. The condensation on the top layer finishes

when it reaches 22 °C at 120 min; however, its moisture does not change to

reach equilibrium for at least 30 min more. The exhaust air temperature follows

the same top layer temperature pattern; hence both are cooled by the water

evaporation during the first few minutes, and reach 22 °C relatively fast. While

the drying front moves from the bottom to the top layer, the exhaust air

temperature remains very stable. When the top layer starts to be dried, after

220 min, the exhaust air temperature starts to get increased, and its RH goes

down. Despite the fact that the energy to heat the air up is constant; the specific

60

energy demand is high while the whole grain mass is warmed up which is up to

60 min. During this time, water removed from the grain is also low; hence air HR

and efficiency are low too.

Energy invested to warm up the grain is difficult to recover. When the grain

is cooling down in a separate bin, around 20% of the energy can be saved

(Raghavan & Sosle, 2007); however, this implies more equipment (Brooker et al.,

1992), and management (Sauer, 1992). Other option is to recycle the output air

(Gunasekaran & Thompson, 1986), but it is not useful during the whole process.

In the current simulated case, the exhaust air is saturated during the first 4 hours,

it does not enhance the drying rate when it is recycled. One can think of reusing

part of the exhaust air when its RH is below its respective grain equilibrium

moisture at the desired final moisture. Doing this, the energy to warm the air up

would be lower. In the present study, air recirculation can reduce the energy

consumption during the second half of the drying process; however, by following

this approach, the total drying time is increased.

3.4.3. Deep bed (in bin batch dryer- general performance)

Drying performance depends on several factors; some can be controlled,

but others may not be possible. Dryer air temperature, air flow, and bed depth

can be adjusted (Brooker et al., 1992; Raghavan & Sosle, 2007; Sauer, 1992);

however, ambient conditions, and fresh grain moisture onlfy can be forecasted.

61

Due to the large amount of possible combinations, this paper presents only some

general tendencies.

Figure ‎3-4: In bin batch dryer case – Grain moisture (simulation results).

Figure‎3-5: In bin batch dryer case – Grain temperature (simulation results).

0.1

0.2

0.3

0.4

0 120 240 360 480 600 720

Gra

in M

ois

ture

(d

b)

time (min)

Top

Average

Bottom

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

0 120 240 360 480 600 720

Gra

in T

emp

eratu

re (°C

)

time (min)

Top

Average

Bottom

62

Figure‎3-6: In bin batch dryer case – Exhaust air properties (simulation

results).

Figure ‎3-7: In bin batch dryer case – Energy demand (simulation results).

Dry and warm ambient conditions reduce considerably the energy demand

compared with a cold and wet atmosphere. In Figure ‎3-8, the energy required to

4000

6000

8000

10000

12000

14000

16000

18000

0 120 240 360 480 600 720

En

erg

y d

eman

d (

kJ

·kg

-1

H2O

)

time (min)

Instant

Accumulated

30%

50%

70%

90%

110%

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

0 120 240 360 480 600 720

Air

RH

(%

)

Air

Tem

per

atu

re (°C

), H

R (

g·k

g-1

dry

air

)

time (min)

Temperature

HR

RH

63

remove one kg of water is almost twice when ambient air is at 5 °C@95 %RH

than when it is at 20 °C@35 %RH.

Air flow and temperature can be adjusted to change drying time and

efficiency. Larger amount of air and high temperature reduce the drying time;

however, this can increase energy consumption, and damage the grain

(Raghavan & Sosle, 2007). It is more efficient to dry at low air rates, as illustrated

in Figure ‎3-9 and Table ‎3-4; however, this does not take into account the higher

price of the electric energy required to operate the fan for longer time (Sauer,

1992), see drying time in Figure ‎3-10. Grain drying is more efficient at higher

drying temperatures when the weather is cold; however, with warm weather, it is

more efficient to use lower temperatures.

Figure ‎3-8: Energy demand. Initial Grain Moisture 0.316db (24%wb), Dryer

Air Temp. 54 °C, Air Flow 15 m3·min-1·ton-1

3.5

4.0

4.5

5.0

5.5

6.0

6.5

30% 50% 70% 90%

En

erg

y D

eman

d (

MJ

·kg

-1 H

20

)

Ambient Air Relative Humidity

5 10 15 20

Ambient Air Temperature (°C)

64

Figure ‎3-9: Energy demand at three weather scenarios . Initial Grain

Moisture 0.316 db (24% wb).

Figure ‎3-10: Drying Time . Ini. Moisture 0.316 db (24% wb), RH 75%, Amb.

Temp. 10 °C.

0

10

20

30

40 50 60 70 80

Dry

ing

Tim

e (H

ou

rs)

Dryer Air Temperature (°C)

10 15 20 25

Air Flow (std m3·min-1·ton-1)

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

40 50 60 70 80

En

ergy

Dem

an

d (

MJ

·kg

-1H

20)


10 15 20 25


Cold and Wet

Warm and Wet

Warm and Dry

65

Table ‎3-4: Drying conditions shown in Figure ‎3-9.

Figure ‎3-11: Moisture Difference . Initial Moisture 0.316 db (24%wb), RH

75%, Amb. Temp. 10 °C.

Differential moisture is increased when hotter air is used to dry, and the air

flow is low, see Figure ‎3-11. Raghavan & Sosle (2007) explain the temperature

effect, “For a given grain depth, raising the air temperature speeds up drying but

increases the chance of over drying near the floor”. When the air flow is reduced,

it gets saturated with moisture more easily for the first few corn layers. Therefore,

last layers start to be dried later in the process, or not dried at all. When this

happens, the first layers have to be over dried to get the desired average

moisture for the whole grain batch.

0.05

0.10

0.15

0.20

0.25

40 50 60 70 80

Mois

ture

Dif

fere

nti

al

(db

)


10 15 20 25


Cold and Wet Warm and wet Warm and dry

Ambient Temperature °C 5 20 20

Relative Humidity % 95% 95% 35%

66

3.5. Conclusions

The SEDA simulated consistently the main parameters of the grain drying

process. The results allowed us to identify the performance of the process

(energy demand, moisture differential, and drying time) for a given drying

conditions (Initial and final grain moisture, ambient conditions, drying

temperature, and bed depth). SEDA can be used to identify control strategies to

improve the performance of the drying process. Exhaust air temperature and/or

RH can be monitored to control the dryer air temperature and/or flow.

3.6. Acknowledgement

We acknowledge the financial contribution of NSERC without which this

study wouldn’t have been possible.

3.7. References




ANSI/ASAE. (1998). Density, Specific Gravity, and Mass-Moisture Relationships

of Grain for Storage D241.4 (pp. 509-510). St. Joseph, MI: American

National Standards Institute / American Society of Agricultural Engineers.



Brooker, D. B., Bakker-Arkema, F.W., and Hall, C.W. (1974). Grain drying

systems. In I. Publishing Company (Ed.), Drying Cereal Grains (pp. 145–

184). Westport, Connecticut: AVI Publishing Company, Inc.

67



1-48.

Jayas, D. S., & Cenkowski, S. (2007). Grain Property Values and Their

Measurement. In A. S. Mujumdar (Ed.), Handbook of industrial drying (pp.

575-600). Boca Raton, FL :: CRC/Taylor & Francis.

Jumah, R. Y. (1995). Flow and drying characteristics of a rotating jet spouted

bed. Ph.D. thesis, McGill University, Montreal QC, Canada.







10.1080/07373939508916956



2351/80/2305-1254$02.00, 1254-1260.

Pabis, S., & Henderson, S. M. (1962). Grain drying theory III.The grain/air

temperature relationship. J. Agric. Eng. Res., 7, 21–26.







of Corn Drying - A New Model. TRANSACTIONS of the ASAE, 11(4), pp.

582-586.

CONNECTING TEXT

After developing a mathematical model that represents the drying kinetics of

corn it would be appropriate to use it to test a control strategy to reduce the

energy consumption of an in bin batch dryer. Low relative humidity or high

temperature of exhausted air means that the inlet air is bringing more energy

than that required to remove the available moisture on grain surface. Controlling

the inlet air temperature or flow, based on the exhaust air temperature, could

improve the efficiency of the drying process.

69

CHAPTER IV

4. INCREASING THE IN-BIN BATCH DRYER PERFORMANCE BY

MONITORING THE EXHAUST AIR

Díaz Martínez Jorge Alonso, Yvan Gariepy and Vijaya Raghavan

Bioresource Engineering, McGill University, 21 111, Lakeshore Road, Ste. Anne de Bellevue, Québec, Canada. [email protected]

Keywords: Corn, Dryer, Control Simulation, In-Bin Batch.

4.1. Abstract

The System Equations for Drying with Air (SEDA) (Díaz et al., 2012) was

used to identify control strategies for improving the performance of an In-Bin

Batch Dryer. Two control strategies were tested: one regulated the drying air

temperature while the other regulated the air flow. The latter gave the best

balance between energy efficiency and drying time. At the beginning of the

process, maximum air flow and temperature were required to heat the grain up

rapidly and to remove the surface moisture of the fresh grain. When the grain

surface was dry and the moisture migration inside the grain had limited water

transfer, less airflow was necessary; however, the air had to be hot to maximize

its Vapor Pressure Deficit, which enhances the drying rate, to carry the maximum

moisture before becoming saturated.

70

4.2. Introduction

The constantly increasing human population demands more food and

energy every day, and it is necessary to supply the first without compromising the

second. Grains are basic to human diet; however, their production consumes

large amounts of energy, this is, especially, true of the grain drying process,

which uses more than half of the total energy for grain production (Raghavan &

Sosle, 2007; Sauer, 1992). The standard industrial process for drying grain

involves heating air and blowing it through kernels, a process that burns large

quantities of fossil fuels. Although more efficient drying technologies have been

developed, they are expensive and/or unreliable. Although Sauer (1992)

articulated this problem 20 years ago, the situation remains the same. More

efficient technologies will take a number of years to become accessible to

farmers; in the short term, it is necessary to adjust the present equipments to get

the most from them.

A System of Equations for Drying with Air (SEDA) (Díaz et al., 2012) was

used to increase the performance of the present drying equipment. SEDA

introduced the Vapor Pressure Deficit (VPD) concept to drying simulations and

was developed to reproduce the main parameters of the drying process, namely

temperature, moisture, energy demand and retention time. In this paper, SEDA is

71

used for identifying simple control strategies that can help to improve the In-Bin

Batch Dryers’ energy efficiency without decreasing throughput significantly.

4.3. Materials and methods

SEDA was used to simulate four reference scenarios; these were compared

with the results from two control strategies. First, the four reference scenarios for

an In-Bin Batch Dryer were simulated with extreme conditions of air temperature

and flow. The parameters compared were the specific energy demand and the

retention time. Second, two control strategies were simulated: one regulated the

drying air temperature while the other regulated the air flow. Both were controlled

by monitoring the exhaust air temperature. Each control strategy was run with

four exhaust air temperature set points. Finally, the best performed set points

were plotted against the reference scenarios.

The relative humidity (RH) of exhaust air is the best parameter to indicate

how well the energy used to heat air up is invested, but measure it is difficult in

grain drying environments. Exhaust air temperature is a well indicator too, and

thermometers have better accuracy and are more reliable than RH meters.

Relative humidity meters have low accuracy in highly humid mediums, although,

usually, above 80%RH, their measured error is 5% or more. Another aspect is

that RH meters are easily clogged by grain dust, increasing measurement errors

even more.

72

For all the simulations, a number of parameters were fixed. The ambient

conditions were 10 °C for air temperature and 80% for RH. The grain bed depth

was 96 cm. Final grain moisture was 15% wet basis (wb). The initial grain

moisture was simulated between 21%(wb) and 30%(wb) with intervals of 3%.

4.3.1. Reference conditions

The extreme conditions of the drying air were simulated. For air flow, the

two values were 10 and 25 m3·min-1·t-1 (Sauer, 1992); the air temperature values

were 40 and 80°C (Raghavan & Sosle, 2007; Sauer, 1992). This resulted in four

simulations, as presented in Table ‎4-1. The specific energy demand and the

retention time were plotted versus the initial grain moisture to identify the lowest

energy consumption and fastest setup.

Table ‎4-1: Conditions of the four reference cases.

40@10 80@10 40@25 80@25

Temperature (°C) 40 80 40 80

Airflow (m3·min-1·t-1 ) 10 10 25 25

4.3.2. Regulating Temperature

Four conditions were simulated; for each one, the controller was set up with

a different exhaust air temperature. These set points were 20 °C, 24 °C, 28 °C,

and 32 °C. When the exhaust air reached the set point, the drying air

temperature was reduced with a proportional controller to obtain the desired set

point. The initial and maximum value for the drying temperature was 80 °C; the

mailto:40@10

mailto:80@10

mailto:40@25

mailto:80@25

73

minimum value was 40 °C. For each case, the specific energy demand and

retention time were plotted to identify the most efficient set point with the shortest

retention time.

4.3.3. Regulating Airflow

As in the case of temperature regulation, four set points were simulated for

airflow control with the same exhaust air temperature. When the exhaust air

reached the set point, the drying airflow was reduced with a proportional

controller to get the desired set point. The initial and maximum value for the

drying airflow was 25 m3·min-1·t-1. The minimum value was 10 m3·min-1·t-1. For

each case, the specific energy demand and retention time were plotted to identify

the most efficient set point with the shortest retention time.

4.4. Results and discussion

4.4.1. Reference conditions

As expected, the fastest drying setup was “80@25”, as shown in Figure ‎4-1

. The most efficient setups were “80@10” and “40@10” due to the low ambient

temperature and its high humidity; however, the retention time for “40@10” was

too large, making its implementation impractical. The impact of ambient

conditions in drying performance is shown in Chapter III (Díaz et al., 2012). The

setup “40@25” did not show any remarkable performance.

74

Figure ‎4-1: Reference setup – Simulation results.

4.4.2. Regulating drying temperature

The grain thermal inertia made it difficult to control the system around the

set point with a proportional controller. The exhaust air temperature fluctuated

several times around the set point before becoming stable. After the exhaust air

temperature reached the target value, the proportional controller changed the

inlet temperature relatively fast. While the incoming air was already at its lowest

value (40° C), the warm grain kept the outlet air temperature above the desired

target for almost one hour. When the exhaust air finally reached the desired

temperature, the proportional controller returned the inlet temperature to its

maximum value (80 °C) in around ten minutes. However, the relative cold grain

4.5

5.0

5.5

6.0

6.5

20% 25% 30%

Sp

ecif

ic E

ner

gy

Dem

an

d (

MJ

·kg

-1 H

20

)

Fresh Grain Moisture (wb)

40@10 80@10 40@25 80@25

Drying Air Quality (°C @ std m3·min-1·ton-1 )

0

10

20

30

40

20% 25% 30%

Ret

enti

on

Tim

e (h

ou

rs)


75

kept the exhaust temperature below the set point for another hour, and the cycle

started again. If a more precise control is required, it is necessary to implement a

proportional integral derivative controller (PID).

When the set point for exhaust air was adjusted at 28 and 32 °C, the dryer

consumed less energy and dried faster, as can be seen in Figure ‎4-2. When the

drying front reached the top layer, hotter air enhanced the drying rate because it

had larger VPD (Díaz et al., 2012). Therefore, the retention time was increased

as the set point for lowering the exhaust temperature.

Figure ‎4-2: Regulating drying temperature – Simulation results

The set points 20 and 24 C had the lowest energy efficiency; this is

explained by the lower VPD and saturation point of the incoming air. When the

grain was hot and the moisture migration limited the drying rate, the exhaust air

4.5

5.0

5.5

6.0

6.5

20% 25% 30%

Sp

ecif

ic E

ner

gy

Dem

an

d (

MJ

·kg

-1 H

20

)


20°C 24°C 28°C 32°C

Exhaust Air Temperature Set Point (°C)

0

10

20

30

40

20% 25% 30%

Ret

enti

on

Tim

e (h

ou

rs)


76

was hotter, and the controller reduced the drying air temperature. With lower

temperature, the drying air had lower capacity to hold water before getting

saturated, making the air use less efficient (Díaz et al., 2012).

In an In-Bin Batch Dryer, where there is no grain mixing, controlling the

drying process by regulating the inlet temperature may lead to a better grain

quality while increasing energy efficiency. At the beginning of the process, the

wet and cold grain was efficiently dried by hot air; the high evaporation rate kept

the first layers of corn warm. When the drying rate decreased, the controller

reduced the incoming air temperature. Therefore, the first layers of corn were not

overheated. At the end of the process the whole mass of grain was below 42 °C,

a safe value for producing seeds (Hall, 1980). The “three-stage continuous flow

concurrent flow grain dryer” (Bakker-Arkema et al., 1981) uses this energy

reduction principle; the hottest air is in contact with fresh grain, and the less hot

air is blown through the grain while this is moving through the dryer.

4.4.3. Regulating airflow

In this case, the proportional controller kept the exhaust air temperature

relatively close to the set point. When the exhaust air was hotter than the

targeted value, the proportional controller reduced the airflow. With less air going

through the grain, its energy was easily absorbed by water evaporation, and the

exhaust air temperature quickly reached the set point.

77

When the set point for the exhaust air temperature was between 20 and

28 °C, the best energy efficiency was obtained; however, the retention time was

longer. Incoming air always had the maximum VPD and capacity to hold moisture

because it was hot, 80 °C, during the whole process, and the energy excess was

controlled by reducing the airflow making the energy use more efficient. This

airflow reduction increased the drying time; however, with higher temperature set

points, the retention time was less impacted (See Figure ‎4-3).

Figure ‎4-3: Regulating airflow – Simulation results

In an In-Bin Batch Dryer, the airflow controller produced a good balance

between energy efficiency and throughput; however, bottom grain layers may

have inferior quality if there is no grain mixing. During the whole drying process,

the bottom layer was in touch with hot air. At the final part of the process, this

4.5

5.0

5.5

6.0

6.5

20% 25% 30%

Sp

ecif

ic E

ner

gy

Dem

an

d (

MJ

·kg

-1 H

20

)


20°C 24°C 28°C 32°C

Exhaust Air Temperature Set Point (°C)

0

10

20

30

40

20% 25% 30%

Ret

enti

on

Tim

e (h

ou

rs)


78

layer was overdried. This low evaporation rate allowed grain to reach thermal

equilibrium with the surrounding air, and grain quality was reduced as

temperature increased (Hall, 1980).

4.4.4. Reference versus regulating results.

Figure ‎4-4 shows the simulation results for the three scenarios of best

performance for further comparison. The fastest scenario was “Reference

80@25”, but it had the highest energy demand. In order to achieve a fast drying

speed with a good energy demand, controlling the airflow with a set point of

32 °C was the best option. The simulations “Reference 80@10”, “Flow control

28 °C” and “Temp control 32 °C” had the best energy efficiency. However, the

first took too much time to dry the grain; the second one had the best drying time

of the three at low initial grain moisture, between 21% and 25% (wb); and the last

one had a better time for high initial grain moisture, 25% to 30% (wb).

Even when three simulations gave almost the same energy efficiency, the

two with constant inlet temperature (80 °C), “Reference 80@10” and “Flow

control 28 °C”, can improve even more their efficiency in the process after drying.

The grain finished hotter in these two simulations than in the “Temp control

32 °C” scenario. At the end of the drying process, when the grain is hot, there is

an opportunity to remove moisture with the remaining thermal energy inside the

grain. When the exhaust grain is hotter, during the cooling or dryeration step, we

79

can extract more moisture from the grain without investing any thermal energy

(Brooker et al., 1992; Gunasekaran & Thompson, 1986; Raghavan & Sosle,

2007; Sauer, 1992). In dryeration, while the grain is resting hot, a natural

convection flow pushes the ambient air through the grain. This gentle flow

removes grain moisture efficiently but slowly; the thermal energy storage in grain

is mainly gone in evaporating its moisture and not for heating air resulting in a

free moisture removal condition.

Figure ‎4-4: Setups with the best performance from the three scenarios.

0

10

20

20% 25% 30%

Ret

enti

on

Tim

e (h

ou

rs)


Reference (80@10) Reference (80@25) Temp. Control (32°C)

Flow Control (28 °C) Flow Control (32 °C)

Simulation

4.5

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5.5

6.0

6.5

20% 25% 30%

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)


80

4.5. Conclusions

Deciding the air temperature and flow for drying grain is a compromise

between energy efficiency and throughput. High airflow and temperature leads to

high drying rates but sacrifices the efficiency. Low air flow and high temperature

reduces energy consumption; however, retention time is considerably increased.

Regulating the drying air conditions based on exhaust air temperature offers a

better balance between the two parameters. This control strategy ensures a high

energy input at the beginning of the process, when the system is more able to

absorb it by heating the grain and evaporating water; further, the power is

reduced when moisture takes more time to reach the grain surface to be

evaporated.

Depending on the desired quality of the grain, a temperature or airflow

control can be chosen. To get the best performance in terms of energy efficiency

and throughput, an air flow control is the best choice; this ensures the maximum

VPD and saturation point of the drying air. If the grain quality is the priority when

the grain is not mixed and the energy-time balance is key, a temperature control

has to be chosen.

4.6. References




81



Díaz, J., Gariepy, Y., & Raghavan, V. (2012). Modeling of grain dryers: thin

layers to deep beds. Paper presented at the ISAE Convention, Pantnagar,

India.



1-48.

Hall, C. W. (1980). Drying and storage of agricultural crops. Westport, Conn. ::

AVI Pub. Co.






82

CHAPTER V

5. SUMMARY AND CONCLUSIONS

The global concern about the use of fossil fuels and its consequences has

led the grain agro industry to find more efficient drying process. Grains are basic

in the food production chain (Raghavan & Sosle, 2007). For safe storage, the

grain has to be dried; this single process consumes more energy than all the

other required process for grain production. Fossil fuels are the main energy

source for grain drying, and electricity is only used for moving conveyors and

fans. In the last decades, more efficient technologies have been developed;

however, they are expensive or not reliable (Sauer, 1992). To improve drying

efficiency, in the coming years, it is necessary to optimize the use of present

dryers. Running computer simulations is a fast track to find the optimum setup for

present dryers.

The initial objective of this work was to develop a computation model that

represented the main parameters of the grain drying process. Air temperature

and relative humidity, grain temperature and moisture, and energy invested to

heat the air up were the operational parameters simulated consistently by the

System Equations for Drying with Air (SEDA). This model was validated with thin

layer test results extracted from the scientific literature; R2 values higher than

0.83 were obtained for ten different conditions with air temperature and relative

83

humidity (RH) ranges of [16 °C-120 °C] and [4.8%-83%] respectively, and grain

moisture between 0.1 and 0.6 (db).

With a robust mathematical model capable to simulate the drying process in

a wide range of conditions, the next step was to simulate a grain depth bed

equivalent to an In-bin batch dryer. This was built with a number of thin layers;

the exhaust air from a thin layer was the inlet air to the next one. The simulation

results were consistent with the dryer performance reported by other authors

(Brooker et al., 1992; Gunasekaran & Thompson, 1986; Hall, 1980; Raghavan &

Sosle, 2007; Sauer, 1992); they also permitted to identify where the energy went,

and knowing this condition, it was possible to suggest strategies for reducing

energy consumption. At the beginning of the drying process, most of the energy

is taken by the cold grain to increase its temperature. Then, the fresh grain

surface moisture was easily removed until the drying front reached the top layer.

At this moment, the exhaust air RH started to reduce, and its temperature

increased until the end of the drying process. This means that the energy

incoming to the system is higher than the energy required to evaporate the

available grain surface moisture.

Once the opportunity to increase drying performance was identified, two

control strategies were simulated. The exhaust air quality indicated how

efficiently the drying air is being used; therefore, its temperature was monitored.

84

When this parameter reached the set point, a proportional controller helped in

reducing the energy input to the system. One strategy considered was by

reducing drying air temperature; the other one was by reducing the air flow. The

latter strategy gave the best energy-throughput balance. However, it is expected

that the first strategy results in a better grain quality.

85

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