ABSTRACT
The effect of sample thickness (5, 10 and 15 mm) and method of drying; hot air drying (50, 60 and 70 ⁰C), microwave drying (200, 300 and 400W), open sun and solar drying on the drying characteristics and kinetics of cooking banana slices were investigated. Results showed that cooking banana drying is a diffusion-controlled process. The rate of drying increased with increase in drying temperature, microwave power rating and solar intensity. The values of drying rate constant (k) and effective moisture diffusivity (Deff) increased with the increase in microwave power and drying temperature levels. The dependence on effective diffusivity coefficient were expressed by an Arrhenius type relationship. The results also showed that sample thickness, method of drying, drying air temperature and microwave power levels affected the drying rate and thus the drying time. It was observed that cooking banana slices dried completely within 3hrs – 11hrs:30 mins, 15 - 45 mins, 10 – 30hrs and 18 – 42 hrs under hot air drying, microwave drying, solar and open sun respectively. Irrespective of the drying methods, all the samples exhibited constant rate and falling rate period. Fifteen thin-layer mathematical drying models were fitted to the experimental data and compared with three statistical parameters, the coefficient of determination (R2), chi-square (X2) and root mean square error (RMSE). The Midilli and KucuK model (2002) was shown to have a better fit to the experimental data obtained from oven drying, microwave drying, solar and open sun drying respectively when compared to other tested models. Effective moisture diffusion coefficients (Deff) were determined utilizing Fick’s second law equation and the values of Deff found to be in range of 1.393 - 8.89 x 10-8 m2/s and 1.074 x 10-7 – 8.89x10-8 m2/s for oven and microwave drying respectively. The Arrhenius-type relationship that describes the temperature dependence of effective moisture diffusivity for oven drying was determined to be 23.599kJ/mol, 24.809 kJ/mol and 24.223kJ/mol for 5, 10 and 15 mm sample thicknesses respectively. Modified Arrhenius equation was applied to identify the activation energy of microwave drying and its value ranged from 18.619 to 11.940kJ/mol for 5, 10 and 15 mm sample thicknesses. Respective empirical equations were also generated from Midilli-Kucuk (2002) thin layer drying model that can predict the drying curves of cooking banana under the listed drying methods.
TABLE OF CONTENTS
Title Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgement v
Table of Contents vi
List of Tables vii
List of Figures viii
List of Plates ix
Abstract x
CHAPTER ONE: INTRODUCTION
1.1 Background of Study 1
1.2 Statement of Problem 3
1.3 Objectives of the Study 5
1.3.1 General objective of the study 5
1.3.2 Specific objectives of the study 5
1.4 Justification of the Study 5
1.5 Scope of the Study 7
CHAPTER TWO: LITERATURE REVIEW
2.1 Overview of Cooking Banana 8
2.1.1 Origin in Nigeria 9
2.1.2 Nutrition value and chemical composition of cooking banana 10
2.1.3 Utilization and processing of cooking banana 11
2.2 Basic Principles in Drying 12
2.2.1 Heat transfer 14
2.2.2 Mass transfer 14
2.3 Drying Rate Period 17
2.3.1 Initial drying period 18
2.3.2 Constant rate period 18
2.3.3 First falling drying rate period 19
2.3.4 Second falling drying rate period 19
2.4 Drying Rate 20
2.4.1 Parameters affecting the drying rate. 21
2.5 Drying Kinetics 28
2.6 Classification of Drying Methods 32
2.6.1 Natural air drying 33
2.6.2 Supplemental heat and low temperature drying 34
2.6.3 Forced air drying 34
2.6.4 Heated air drying 35
2.6.5 Unheated air drying 36
2.7 Solar Drying 37
2.7.1 Types of solar dryers 38
|2.8 Thin Layer Drying Models 41
2.8.1 Theoretical models 42
2.8.2 Semi-theoretical models 42
2.8.3 Empirical models 51
2.9 Microwave Drying 54
CHAPTER THREE: MATERIALS AND METHODS
3.1 Material Selection 58
3.1.1 Experimental set-up and methodology 58
3.1.1 Oven drying method 58
3.1.2 Microwave drying method 59
3.1.3 Solar drying method 59
3.1.4 Open sun drying method 60
3.2 Experimental Design 61
3.2.1 Experimental design for oven drying 61
3.2.2 Experimental design for microwave drying 61
3.2.3 Experimental design for solar and sun drying 62
3.3 Moisture Content Determination 62
3.4 Determination of Moisture Ratio 62
3.5 Drying Rate Calculation 63
3.6 Determination of Effective Moisture Diffusivity 63
3.7 Determination of the Activation Energy 64
3.7.1 Oven drying 65
3.7.2 Microwave drying 65
3.8 Mathematical Modelling of Drying Kinetics 66
3.8.1 Statistical evaluation of drying models 66
CHAPTER FOUR: RESULT AND DISCUSSIONS
4.1 Preliminary Investigations on Drying Kinetics of Cooking Banana Slices 68
4.1.1 Initial moisture content determination 68
4.1.2 General Observations during oven drying 69
4.1.3 Effect of temperature on drying curves 69
4.1.4 Effect of temperature on drying rate 71
4.1.5 Effect of temperature on drying rate constant (K) 73
4.1.6 Effect of temperature on effective moisture diffusivity (deff) 75
4.1.7 Effect of temperature on activation energy 77
4.1.8 Mathematical modelling of oven drying kinetics 78
4.1.8.1 Validation of best fit model 80
4.1.8.2 Validation of generated empirical equations 81
4.2 Microwave drying kinetics of cooking banana slices 87
4.2.1 Effect of microwave power on drying curves 87
4.2.2 Effect of microwave power on drying rate 89
4.2.3 Effect of microwave power on drying rate constant (K) 91
4.2.4 Effect of microwave power on effective moisture diffusivity (deff) 94
4.2.5 Effect of microwave power on activation energy 94
4.2.6 Mathematical modelling of microwave drying kinetics 97
4.2.6.1 Validation of best fit model 99
4.2.6.2 Validation of generated empirical equations 100
4.3 Solar drying kinetics of cooking banana slices 106
4.3.1 Solar drying characteristics of cooking banana slices 109
4.3.2 Mathematical modelling of solar drying kinetics 110
4.3.2.1 Validation of best fit model 111
4.3.2.2 Validation of generated empirical equations 115
CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions 120
5.2 Recommendations 121
References 123
Appendices 136
LISTS OF TABLES
2.1 Taxonomical description of cooking banana 9
2.2 Advantages and limitations of microwave drying 55
3.1 Experiments done showing factorial design used (Oven drying) 61
3.2 Experiment done showing factorial design used (Microwave drying) 61
3.3 Experiment done showing Factorial design used
(Solar and open sun drying) 62
3.4 Thin layer mathematical models used 67
4.1 Drying rate constant (K) values (Oven drying) 75
4.2 Effective moisture diffusivity values (Oven drying) 77
4.3 Statistical results obtained from selected models 79
4.4 Statistical results of Midilli and Kucuk model (Oven drying) 80
4.5 Drying rate constant (K) values (Microwave drying) 93
4.6 Effective moisture diffusivity values (Microwave drying) 94
4.7 Statistical results obtained from the selected drying models (Microwave drying) 98
4.8 Statistical results of Midilli and Kucuk model (Microwave drying) 98
4.9 Statistical results obtained from the selected drying models
(Solar and open sun drying) 111
4.10 Statistical results of Midilli and Kucuk model
(Solar and open sun drying) 111
4.11 Statistical results of Newton model (Oven drying) 136
4.12 Statistical results of Page model (Oven drying) 136
4.13 Statistical results of modified Page model (Oven drying) 136
4.14 Statistical results of Henderson and Pabis model (Oven drying) 137
4.15 Statistical results of modified Henderson and Pabis model (Oven drying) 137
4.16 Statistical results of Midilli and Kucuk model (Oven drying) 137
4.17 Statistical results of modified Midilli model (Oven drying) 138
4.18 Statistical results of Logarithmic model (Oven drying) 138
4.19 Statistical results of Two-term model (Oven drying) 138
4.20 Statistical results of Two-term exponential model (Oven drying) 139
4.21 Statistical results of Demir et al. model (Oven drying) 139
4.22 Statistical results of Verma et al. model (Oven drying) 139
4.23 Statistical results of Approximation of diffusion model (Oven drying) 140
4.24 Statistical results of Hii et al. model (Oven drying) 140
4.25 Statistical results of Wang and Singh model (Oven drying) 140
4.26 Statistical results of Newton model (Microwave drying) 141
4.27 Statistical results of Page model (Microwave drying) 141
4.28 Statistical results of modified Page model (Microwave drying) 141
4.29 Statistical results of Henderson and Pabis model (Microwave drying) 142
4.30 Statistical results of modified Henderson and Pabis model (Microwave drying) 142
4.31 Statistical results of Midilli and Kucuk model (Microwave drying) 142
4.32 Statistical results of modified Midilli model (Microwave drying) 143
4.33 Statistical results of Logarithmic model (Microwave drying) 143
4.34 Statistical results of Two-term model (Microwave drying) 143
4.35 Statistical results of Two-term exponential model (Microwave drying) 144
4.36 Statistical results of Demir et al. model (Microwave drying) 144
4.37 Statistical results of Verma et al. model (Microwave drying) 144
4.38 Statistical results of Approximation of diffusion model
(Microwave drying) 145
4.39 Statistical results of Hii et al. model (Microwave drying) 145
4.40 Statistical results of Wang and Singh model (Microwave drying) 145
4.41 Statistical results of Newton model (Solar and Sun drying) 146
4.42 Statistical results of Page model (Solar and Sun drying) 146
4.43 Statistical results of modified Page model (Solar and Sun drying) 146
4.44 Statistical results of Henderson and Pabis model (Solar and Sun drying) 147
4.45 Statistical results of modified Henderson and Pabis model (Solar and Sun drying) 147
4.46 Statistical results of Midilli and Kucuk model (Solar and Sun drying) 147
4.47 Statistical results of modified Midilli model (Solar and Sun drying) 148
4.48 Statistical results of Logarithmic model (Solar and Sun drying) 148
4.49 Statistical results of Two-term model (Solar and Sun drying) 148
4.50 Statistical results of Two-term exponential model (Solar and Sun drying) 149
4.51 Statistical results of Demir et al. model (Solar and Sun drying) 149
4.52 Statistical results of Verma et al. model (Solar and Sun drying) 149
4.53 Statistical results of Approximation of diffusion model
(Solar and Sun drying) 150
4.54 Statistical results of Hii et al. model (Solar and Sun drying) 150
4.55 Statistical results of Wang and Singh model (Solar and Sun drying) 150
4.56 Average experimental drying data for thickness of 5mm (Oven drying) 151
4.57 Average experimental drying data for thickness of 10mm (Oven drying) 151
4.58 Average experimental drying data for thickness of 15mm (Oven drying) 152
4.59 Average experimental drying data for thickness of 5mm (Oven drying) 153
4.60 Average experimental drying data for thickness of 10mm (Oven drying) 153
4.61 Average experimental drying data for thickness of 15mm (Oven drying) 154
4.62 Average experimental drying data for thickness of 5mm (Oven drying) 155
4.63 Average experimental drying data for thickness of 10mm (Oven drying) 155
4.64 Average experimental drying data for thickness of 15mm (Oven drying) 155
4.65 Average experimental drying data for thickness of 5mm and 200W (Mw) 156
4.66 Average experimental drying data for thickness of 5mm and 300W (Mw) 156
4.67 Average experimental drying data for thickness of 5mm and 400W (Mw) 157
4.68 Average experimental drying data for thickness of 10mm and 200W (Mw) 157
4.69 Average experimental drying data for thickness of 10mm and 300W (Mw) 157
4.70 Average experimental drying data for thickness of 10mm and 400W (Mw) 158
4.71 Average experimental drying data for thickness of 15mm and 200W (Mw) 158
4.72 Average experimental drying data for thickness of 15mm and 300W (Mw) 158
4.73 Average experimental drying data for thickness of 15mm and 400W (Mw) 159
4.74 Average experimental drying data for thickness of 5mm (Solar drying) 159
4.75 Average experimental drying data for thickness of 10mm (Solar drying) 160
4.76 Average experimental drying data for thickness of 15mm (Solar drying) 160
LIST OF FIGURES
2.1 General drying rate curve 18
2.2 Constant and falling rate periods in thin-layer drying of high
moisture grain 28
4.1 Drying curve of moisture content against drying time for cooking
banana of 5mm thickness at 50ºC 68
4.2 Effect of temperature on the moisture ratio (M_t/M_o ) vs drying time at
slice thickness of 5mm. 70
4.3 Effect of temperature on the moisture ratio (M_t/M_o ) vs drying time at
slice thickness of 10mm. 70
4.4 Effect of temperature on the moistures ratio vs drying time at slice
thickness of 15mm 71
4.5 Effect of drying rate on drying time at different temperatures for slice
thickness of 5mm. 72
4.6 Effect of drying rate on drying time at different temperatures for slice
thickness of 10mm. 72
4.7 Effect of drying rate on drying time at different temperatures for slice
thickness of 15mm. 73
4.8 Log of moisture ratio of 5, 10 and 15mm thickness against drying
time of 50oc 74
4.9 Log of moisture ratio of 5, 10 and 15mm thickness against drying
time of 60oc 74
4.10 Log of moisure ratio of 5, 10 and 15mm thickness against drying
time of 70oc 75
4.11 Deff versus drying temperature at different levels of slice 76
4.12 Deff versus slice thicknesses at different levels of drying temperature 76
4.13 Ln(deff) versus 1/tabs of different levels of slice thicknesses 78
4.14 Comparison of experimental and predicted moisture ratio values by
Midilli and kucuk model for 5mm thickness 80
4.15 Comparison of experimental and predicted moisture ratio values by
Midilli and kucuk model for 10mm thickness 81
4.16 Comparison of experimental and predicted moisture ratio values by
Midilli and kucuk model for 15 mm thickness 81
4.17 Comparison of empirical MR with experimental MR for 5mm
thickness at 50⁰C 82
4.18 Comparison of empirical MR with experimental MR for 5mm
thickness at 60⁰C 83
4.19 Comparison of empirical MR with experimental MR for 5mm
thickness at 70⁰C 83
4.20 Comparison of empirical MR with experimental MR for 10mm
thickness at 50⁰C 84
4.21 Comparison of empirical MR with experimental MR for 10mm
thickness at 60⁰C 84
4.22 Comparison of empirical MR with experimental MR for 10mm
thickness at 70⁰C 85
4.23 Comparison of empirical MR with experimental MR for 15mm
thickness at 50⁰C 85
4.24 Comparison of empirical MR with experimental MR for 15mm
thickness at 60⁰C 86
4.25 Comparison of empirical MR with experimental MR for 15mm
thickness at 70⁰C 86
4.26 Moisture ratio vs drying time at various microwave powers for 5mm 88
4.27 Moisture ratio vs drying time at various microwave powers for 10mm 88
4.28 Moisture ratio vs drying time at various microwave powers for 15mm 89
4.29 Drying rate vs drying time at various microwave powers for 5mm 90
4.30 Drying rate vs drying time at various microwave powers for 10mm 90
4.31 Drying rate vs drying time at various microwave powers for 15mm 91
4.32 Relationship between LN MR and drying time (microwave drying)
for 5mm 92
4.33 Relationship between LN MR and drying time (microwave drying)
for 10mm 92
4.34 Relationship between LN MR and drying time (microwave drying)
for 15mm 93
4.35 Effective moisture diffusivity versus microwave power for
cooking banana 94
4.36 Arrhenius-type relationship between the values of Ln (Deff) versus sample
mass/power for 5mm 95
4.37 Arrhenius-type relationship between the values of Ln (Deff) versus sample
mass/power for 10mm 96
4.38 Arrhenius-type relationship between the values of Ln (Deff) versus sample
mass/power for 15mm 96
4.39 Comparison of experimental and predicted moisture ratio values by Midilli
and Kucuk model for 5mm thickness (Microwave drying) 99
4.40 Comparison of experimental and predicted moisture ratio values by Midilli
and Kucuk model for 10mm thickness (Microwave drying) 100
4.41 Comparison of experimental and predicted moisture ratio values by Midilli
and Kucuk model for 15mm thickness (Microwave drying) 100
4.42 Comparison of empirical MR with experimental MR for 5mm thickness at 200W 101
4.43 Comparison of empirical MR with experimental MR for 5mm thickness at 300W 102
4.44 Comparison of empirical MR with experimental MR for 5mm
thickness at 400W 102
4.45 Comparison of empirical MR with experimental MR for 10mm
thickness at 200W 103
4.46 Comparison of empirical MR with experimental MR for 10mm
thickness at 300W 103
4.47 Comparison of empirical MR with experimental MR for 10mm
thickness at 400W 104
4.48 Comparison of empirical MR with experimental MR for 15mm
thickness at 200W 104
4.49 Comparison of empirical MR with experimental MR for 15mm
thickness at 300W 109
4.50 Comparison of empirical MR with experimental MR for 15mm
thickness at 400W 109
4.51 Drying curves for cooking banana (Solar drying). 106
4.52 Drying curves for cooking banana (Open sun drying). 106
4.53 Drying rate curves for cooking banana (Solar drying). 107
4.54 Drying rate curves for cooking banana (Open sun drying). 107
4.55 Drying parameter values for thickness of 5mm (Solar drying) 108
4.56 Drying parameter values for thickness of 10mm (Solar drying) 108
4.57 Drying parameter values for thickness of 15mm (Solar drying) 109
4.58 Comparison of experimental and predicted moisture ratio values by
Midilli-Kucuk model for 5mm thickness for solar drying 112
4.59 Comparison of experimental and predicted moisture ratio values by
Midilli-Kucuk model for 10mm thickness for solar drying 112
4.60 Comparison of experimental and predicted moisture ratio values by
Midilli-Kucuk model for 15mm thickness for solar drying 113
4.61 Comparison of experimental and predicted moisture ratio values by Midilli-Kucuk model for 5mm thickness for open sun drying 113
4.62 Comparison of experimental and predicted moisture ratio values by
Midilli-Kucuk model for 10mm thickness for open sun drying 114
4.63 Comparison of experimental and predicted moisture ratio values by
Midilli-Kucuk model for 15mm thickness for open sun drying 114
4.64 Comparison of empirical MR with experimental MR for 5mm thickness
for solar drying 116
4.65 Comparison of empirical MR with experimental MR for 10mm thickness
for solar drying 116
4.66 Comparison of empirical MR with experimental MR for 15mm thickness
for solar drying 117
4.67 Comparison of empirical MR with experimental MR for 5mm thickness
for open sun drying 118
4.68 Comparison of empirical MR with experimental MR for 10mm thickness
for open sun drying 118
4.69 Comparison of empirical MR with experimental MR for 15mm thickness
for open sun drying 119
LIST OF PLATES
1.1 Typical cooking banana (Musa Bluggoe:ABB) 1
3.1 A pictorial view of the convective dryer used for the experiment 162
3.2 A pictorial view of the microwave oven used for the experiment 162
3.3 A pictorial view of the active solar dryer used for the experiment 163
4.1 Dried sliced cooking banana by oven drying method 163
4.2 Dried sliced cooking banana by microwave drying method 164
4.3 Dried sliced cooking banana by solar dryer method 164
4.4 Preparation of the samples prior to drying 165
CHAPTER ONE
INTRODUCTION
1. 1 Background of Study
Cooking bananas (Musa spp., ABB genome) are derived generally from the hybridization of Musa accumulata and Musa balbisiana (Stove and Summonds 1987; Robinson, 1996) and are rather similar to unripe desert banana (M. Cavendish acculata) but appears often large in exterior appearance. Most of the world’s cooking bananas are eaten either raw, in their ripe state, or cooked form or the remaining proportion is processed in order to obtain a storable product (Robinson, 1996).
Fig.1.1 A. Typical Cooking banana (Bluggoe:ABB) (Retrieved from http://www.thespruce.com/types-of-bananas-4018334)
It was introduced into southeast Nigeria in the late 1980's from Asia by the International Institute of Tropical Agriculture (IITA) as a short-term strategy to combat the incidence of black sigatoka disease on bananas (Vuylsteke, 1995).They are also rich in carbohydrate, calcium, phosphorus, iron and other food nutrients (Raynold, 2003). Cooking bananas are not seasonal in nature like many other fruit crop and are available in large quantity throughout the year. But like other fruits are highly perishable food item and could not be preserved for long time after harvesting. The moisture content in ripe cooking banana is about 70% and therefore very susceptible to post-harvest losses and considerable weight loss during transportation and storage. (Valmayor et al., 2000).
Drying is one of the most widely used primary methods of food preservation. The aim of drying is usually to reduce water level to a point at which microbial spoilage and deterioration reactions are greatly minimized (Akpinar and Bicer, 2008). In the development and improvement of equipment used for drying, the simulation and attainment of theoretical information about the behaviour of each product during water removal is important. For the simulation, which is based on the principle of successive drying of thin layers of the product, a mathematical model that represents, satisfactorily, their water loss during the drying process is used in these analyses (Berbet et al., 1995).
Mathematical modelling and simulation of drying curves under different conditions is important to obtain better control of a unit operation and overall improvement of the quality of the final product. Models are often used to study the variables involved in the process, predict drying kinetics of the product and optimize the operating parameters and circumstances (Karathanos and Belessiotis, 1999).
The most critical aspects of drying technology are the mathematical modelling of the process and the experimental setup. The modelling is basically based on the design of a set of equations to describe the system as accurately as possible (Darvishi and Hazbavi, 2012).
Mathematical modelling serves to be a most effective way to know the depth of drying in post-harvest processing of agricultural materials. Numerous mathematical equations can be found in literatures that describe drying phenomena of agricultural products. Thin layer drying models has extensive application due to its simplicity of use (Geetha et al., 2014). Thin-layer drying models for describing the drying phenomenon of agricultural products are usually based on liquid diffusion theory, and the process can be explained by the Fick’s second law (Doungporn et al., 2012).
Many mathematical models have been used to describe the drying process of agricultural products. A considerable amount of work has been done on thin layer drying of different agricultural products. Some of the thin layer models reported were for drying of olive fruit (Mahdhaoui et al., 2013), date palm (Darvishi and Hazbavi, 2012), cocoa (Hii et al., 2008), potato mash (Goyal et al., 2014), rapeseed (Duc et al., 2011), litchi (Janjaia et al., 2011), sorghum (Shen et al., 2011), hazelnuts (Ozdemir and Devres, 1999) and finger millet (Radhika et al., 2011).
1.2 Statement of the Problem
According to Baini and Langrish (2007) and many other authors, the high moisture content of bananas, generally as a fruit makes them susceptible to mould growth, when not properly dried prior to storage. India, the world’s largest banana producer, reported post-harvest losses as high as 35–45%, whereas Brazil reported a product loss of approximately 40% (FAO, 2011). As cooling is not a viable technique to prolong the shelf life of cooking bananas, an alternative to this end is drying and operations of dryers as well as improving the existing drying systems.
Simulation models are used for the design, description and operation of dryers. Several researchers have developed simulation models for natural and forced convection drying systems (Onwude et al., 2016). In the past 60 years, the study of drying behaviour of different materials has been the subject of interest for various investigators on both theoretical and practical grounds (Mohammadi et al., 2008).
Knowledge of the changes in physico-chemical and drying characteristics of agricultural products is of fundamental importance for correct storage and processing, as well as for the design, fabrication and operation of equipment used in the post-harvest processing of these products (Bleoussi et al., 2010).
Thus, the use of mathematical models in estimating the drying kinetics, the behaviour, and the energy needed in the drying of agricultural and food products becomes indispensable.
1. 3. Aim and Objectives
1.3.1 General Objective of the Study
The general objective of this work is to evaluate a suitable thin layer drying model that can accurately predict the single-layer drying kinetics of cooking banana (sv; Bluggoe).
1.3.2 Specific Objectives
i. Investigate the single layer drying characteristics of the cooking banana at three slice thicknesses (5, 10, and 15) mm on three temperature levels (50, 60 and 70) ̊C using an oven dryer.
ii. Investigate the single layer drying characteristics of the cooking banana at three slice thicknesses (5, 10, and 15) mm on three microwave power levels (200, 300 and 400)W using a domestic microwave oven.
iii. Investigate the single layer drying characteristics of the cooking banana at three different thicknesses (5, 10, and 15) mm, on solar and sun drying using an existing solar dryer
iv. Investigate the effect of temperature, slice thickness, microwave power and solar intensity on the drying kinetics of cooking banana.
v. Calculate the effective moisture diffusivity and activation energy.
1.4 Justification of Study
No work on mathematical modelling of thin/single drying kinetics of cooking banana has been found so far. Since its introduction in Nigeria, research efforts have been concentrated mainly on the agronomic aspects (Hahn et al., 1990) and processing methods (Hahn et al., 1990; Ferris et al., 1996) with little attention given to its processing and preservation techniques. As a result, little is known about cooking banana modelling and drying kinetics, hence the data obtained would be fitted into generally accepted thin layer drying models and evaluated for best fit. Models are often used to study the variables involved in the drying process, predict drying kinetics of the product and optimize the operating parameters and circumstances (Karathanos and Belessiotis, 1999). Mathematical Modelling of thin layer drying is important for performance improvements of drying systems (Alibas, 2012).
Drying of many fruits and other agricultural products has been successfully predicted (Mohammadi et al., 2008). Drying influence physicochemical and quality characteristic of products, thus, Modelling of drying kinetic is one tool for process control (Rayaguru and Routray, 2012).
In many agricultural countries, large quantities of food products are dried to improve shelf life, reduce packing costs, lower shipping weights, enhance appearance, encapsulate original flavour and maintain nutritional value (Gunhan et al., 2005)
In the past decades and to present day, Nigeria has suffered a tremendous loss of food products due to inadequate proper and adaptable processing and storage facilities. Losses have been estimated at 30-50% of production (INIBAP, 2000). A bulk of harvested cereals, grains, fruits and tubers are lost due to poor methods of milling and inadequate facilities for drying and storage. Studies have shown that dependence purely on experimental drying practices, without mathematical considerations of the drying kinetics, can significantly affect the efficiency of dryers, increase the cost of production, and reduce the quality of the dried product (Rayaguru and Routray, 2012).
The post-harvest losses of agricultural products can be reduced drastically by using proper drying techniques with knowledge and data gotten from experimental investigation of mathematical modelling of single layer drying kinetics of agricultural products.
1.5 Scope of Study
This thesis work is proposed to cover the modelling of single layer drying kinetics of cooking banana. Fifteen thin layer drying models (Table 3.4) will be evaluated for their suitability. This work focuses on modelling the drying kinetics of cooking banana using four drying methods. The various drying characteristics of cooking banana such as moisture ratio, drying rate constant, diffusion coefficients and activation energy will be determined. Also the effect of drying air temperature, and slice thickness on the model coefficient describing the drying characteristics of the cooking banana slices will be investigated.
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