effect of storage time of effects of cone palm kernel …

EFFECT OF STORAGE TIME OF

PALM KERNEL ON THE PHYSICO-

CHEMICAL PROPERTIES AND

STABILITY OF PALM KERNEL OIL

Ekumankama, E.O., Nwobasi, V. N, Achebe,

V. N. and Ikegwu, O.J.

1

REFRACTANCE WINDOW™

DRYING OF RED ONIONS

(ALLIUM CEPA)

Akinjide A. Akinola, Sulaiman O.

Lawal and Adebayo.S.Osiberu

49

EFFECTS OF CONE

INCLINATION AND HEIGHT

ON CYCLONE PRESSURE

DROP AND POWER

REQUIREMENT

O.B. Okedere, J.A. Sonibare, B.S.

Fakinle and L.A. Jimoda

89

ARTIFICIAL NEURAL NETWORK

AND RESPONSE SURFACE

METHODOLOGY APPLICATIONS TO

MODELLING AND OPTIMISATION OF

OXALIC ACID BIOPRODUCTION

FROM SWEET POTATO STARCH

HYDROLYSATE

Betiku E., Taiwo A. E., Ayodele O. A.,

Fatuntele L. T., Adetayo C. A. and Solomon

B. O.

15

DETERMINATION OF

MOBILITY FACTOR AND

INVESTIGATION OF HEAVY

METALS MIGRATION IN

SOLUOS DUMPSITE SOIL IN

LAGOS STATE, NIGERIA

Salami L. and Susu A.A.

63

DIFFUSIVITY OF CAROTENE

FROM PALM OIL ON COAL-

BASED ACTIVATED CARBON

L. E. Aneke, and U.S.C. Echegi

97

BASIC MODELING OF THE RISER-

STRIPPER-REGENERATOR UNIT OF

A FLUID CATALYTIC CRACKER

Josiah, P. N., J. U. Nwalor, and T. O. Ajayi

31

INTEGRATING ANAEROBIC

DIGESTION AND

HYDROTHERMAL

LIQUEFACTION FOR

RENEWABLE ENERGY

PRODUCTION: AN

EXPERIMENTAL

INVESTIGATION

Eboibi, B. E., Lewis, D. M., Ashman,

P. J., Chinnasamy, S.

73

SEPARATION OF OIL/WATER

EMULSIONS BY CROSS FLOW

MICROFILTRATION

Nwobasi, V. N, Wakeman, R. and

Ekumankama, E. O.

107

JOURNAL OF THE NIGERIAN SOCIETY OF CHEMICAL ENGINEERS A Publication on the Science and Technology of Chemical Engineering

Editorial Board

Prof. D. S. Aribike, Chairman/Editor-in-Chief

Department of Chemical and Petroleum Engineering, University of Lagos, Akoka, Lagos.

Prof. Olufemi Taiwo Deputy/Editor-in-Chief

Department of Chemical Engineering, Obafemi Awolowo University, Ile-Ife.

Prof. Ayoade O. Kuye, Associate Editor

Department of Chemical Engineering, University of Port Harcourt, Port Harcourt.

Prof. E. N. Bassey, Associate Editor

Department of Chemical Engineering, Akwa Ibom State University, Ikot Akpaden.

Prof. Emmanuel Aluyor, Associate Editor

Department of Chemical Engineering, University of Benin, Benin-City.

Prof. A. S. Ahmed, Associate Editor

Department of Chemical Engineering, Ahmadu Bello University, Zaria.

Prof. S. S. Adefila, Associate Editor

Department of Chemical Engineering, Covenant University, Ota.

Dr. J. I. Ume, Associate Editor

Project Development Institute (PRODA), Enugu.

2014 NIGERIAN SOCIETY OF CHEMICAL ENGINEERS

BOARD OF DIRECTORS AND OFFICIALS CHAPTER CHAIRMEN Engr. (Dr) A. L. Yar’Adua, FNSChE

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49

REFRACTANCE WINDOW™ DRYING OF RED ONIONS (Allium Cepa)

Akinjide A. Akinola, Sulaiman O. Lawal and Adebayo. S. Osiberu

Chemical Engineering Department, University of Lagos, Lagos, Nigeria

ABSTRACT

A Refractance Window dryer was fabricated and used to study the drying characteristics of

red onions (Allium Cepa). The data for the drying of the 1mm, 2mm and 3mm sized shreds of red

onions were obtained experimentally and the drying curves and mathematical model that best fit

the drying data were determined. The quality of the dried red onion powder was determined and

compared with dried onion powder obtained from the local supermarket. The results obtained,

established that size affects the drying time of the onion samples. The 1mm sized shreds dried

fastest reducing from a moisture content of 7.19kg water per kg dry solids to less than 0.2kg

water per kg dry solids within 40 minutes of drying. The time of drying increased with size. Of

the 13 Thin-layer drying curve models studied, the regression results showed that the Haghi and

Ghanadzadeh model best describes the drying curve of the 1mm, 2mm and 3mm sized shreds

with the highest coefficient of determination (R2) values of 0.99196, 0.98570 and 0.99523

respectively. The dried onion powder had a Bulk density of 0.4501 grams/ml while those of the

market onion powder were 0.4616 grams/ml.

Key words: Refractance Window™ Drying, Red Onions,

INTRODUCTION

Drying practice has produced various

techniques and equipments such as drum

dryers, rotary dryers, spray dryers, tray

dryers, fluidised bed dryers, freeze drying,

microwave drying, infrared drying,

Refractance Window drying etc.,

(Mujumdar, 2006; Clarke, 2004). Vegar-

Mercado, Gongora-Nieto and Barbosa-

Canovas (2001) have grouped these drying

methods into four generations noting that the

latest developments in drying have produced

drying technologies that fall in the fourth

generation. Also Nindo and Tang (2007)

opined that the fourth generation drying

techniques give food products with greater

retention of food quality and they include

microwave drying, infrared drying,

Refractance Window drying etc.

Onion (Allium Cepa) is a common

vegetable crop grown and used throughout

the world. Onion is grown for its bulb which

comprises fleshy connective scales enclosed

in paper-like wrapping leaves. Onion is

utilised for various uses such as flavouring

and seasoning of a wide variety of dishes,

used as garnish in soups and salads and as a

medicinal herb in many communities as it is

claimed to minimize high blood pressure

and other heart diseases according to Sani

and Jaliya (1993). Onions are crops of world

trade, ranking second after Tomatoes in

importance among the vegetables, with a

total world export production amounting to

2 million metric tons, and worth over 299

million US dollars in 1987 (Sani and Jaliya,

1993). With the increasing rise in world

population and opening of international

trade one would therefore infer that world

Journal of the Nigerian Society of Chemical Engineers, 29(1), 2014

50

onion export production would be well over

2 million metric tonnes as at year 2014.

Abasi et al. (2009) also reported that annual

global production of onion is 47 million

tonnes.

In Nigeria onions have been grown for a

long time though with low yield (Sani and

Jaliya, 1993). This therefore means that

wastage cannot be afforded in the country.

However this has been contended with for

long by harvesters, transporters, market

sellers and in domestic usage. Three

common varieties are grown in Nigeria;

White Onions, Red Onions and Yellow

Onions. The red variety typically has red

skin and red and white flesh and don’t store

well. The quantity of onions wasted in

markets and houses in Nigeria is high

because of inadequate storage facilities and

epileptic power supply needed to keep cut

onions refrigerated domestically.

Bulk handling of onion produce by

transporters to the market is another major

problem faced. Onion has high water

content (Idah et al., 2010). Dried onions

should as much as possible preserve the

colour, aroma, flavour and nutritional

components of the fresh onions. The time to

dry onions is also very important since over-

exposure to heat can destroy sensitive

qualities of onion. Therefore what is needed

is a drying technique that ensures that

onions produced in Nigeria (especially the

red onion type) retain their colour, aroma,

nutrients and nutritional value as much as

possible.

Refractance Window Drying

technology is a novel drying technique that

falls within the contact, indirect and film-

drying techniques (Nindo and Tang, 2007).

This technique has advantages over close

rival techniques like Spray Drying and

Freeze Drying in the areas of retention of

colour, aroma and sensitive components of

the dried products etc. (Nindo and Tang,

2007). Hence Refractance Window drying is

efficient for the dehydration of fruits and

vegetables where the retention of colour,

aroma, flavour and nutritional components

are of the essence. Several fruits, vegetables

and some leaves have been dried

successfully with Refractance Window with

very good retention of quality. These

include strawberries, mangoes, carrots

(Nindo and Tang, 2007), okra slices,

moringa leaves (Osiberu, 2014) etc.

The aim of this study is to obtain the

Refractance Window drying characteristics

of Red Onion. To achieve this, a

Refractance Window dryer equipment was

fabricated to carry out the drying

experiment. The effect of onion shred size

and shape on drying time was studied and a

thin layer drying model that best describes

the drying kinetics of the red onions was

determined. According to Shahari (2012)

three methods are used to analyze the drying

behavior of materials. They include

Experimental based modelling, Mass and

heat transfer based modelling and Models

based on porous media with an equilibrium

approach. However, experimental based

modelling is mathematically and

computationally easier (Shahari, 2012).

The experimental based modelling

involves obtaining an empirical model,

varying input set-up parameters, measuring

output quantities through data logging

devices and the derivation of correlations

that best fit the data obtained and can be

Akinjide A. Akinola, Sulaiman O. Lawal and Adebayo S. Osiberu

51

used for prediction (Shahari, 2012). The

derivation of correlations that best fit the

data obtained and can be used for prediction

can be done by non-linear regression.

METHODS AND MATERIALS

A Refranctance Window Type dryer

was fabricated and used in this study. The

equipment and other ancillary equipment

used are described. The experimental rig is

shown and the method of experimentation is

described. Other calculations used in this

work are presented and the method of

analysis of the quality of the dried onion is

explained.

Equipment

The Refractance Window Dryer

The dryer (see Fig. 1) was constructed

from iron sheets of 1 mm thickness into a

cuboid shaped basin of dimensions 10 in by

14 in by 6 in to contain a water bath. The

basin is opened at the top side with a 8 in by

8 in square opening covered by a plastic film

of 0.05 mm thickness held in place by two

iron flanges of 7 in by 7 in (outer flange)

and 6 in by 6 in (inner flange) dimensions.

Therefore the effective area opened for

drying is 6 in by 6 in. The flanges also make

the plastic film come in contact with water

before the basin gets filled up. This had the

advantage of keeping the contact between

the water and the film even after some water

has evaporated. The rear portion of the top

of the iron basin is made into an elevated

opening which serves to allow water make-

up as the water evaporates and to measure

water temperature through a probe from a

digital thermometer. Heat was applied

directly to the equipment from a LPG burner

placed directly below the dryer.

Measurements

A Type K thermocouple is used to

measure the temperature of the water. The

thermocouple is attached to a digital

thermometer model number 6802 II which

has a 5 digit display.

A MB45 OHAUS Moisture Analyzer

manufactured by OHAUS Corporation, New

Jersey, U.S.A. was used to determine the

moisture content of the sample both before

and after the experiment.

Fig. 1 Schematic Diagram of the

Refractance Window Dryer Equipment Used

A graduated vernier caliper was used to

measure the thickness and diameter of the

onion slices.

Other Equipment

A HVAC type fan of model number

YZF 5-13 was used to remove overhead air-

vapour mixture from the top of the dryer.

The fan rotates at 1300/1800 rpm and uses

A.C. voltage of 200-250 V-50/60 Hz and

was positioned beside the Refractance

Widow dryer

The onions were shredded with a

Benriner Japanese Mandolin Shredder

manufactured by Benriner Co. Ltd.,

Iwakuni-City, Japan. The onions were

shredded to size ranges of 0-1 mm, 0-2 mm

and 0-3 mm respectively.

The dried onion samples were collected

in plastic jars with plastic caps each of 10 ml


52

volume. The plastic jars were bought from

Ojota market in Lagos, Nigeria.

Preparation of Samples

Red onions purchased from the market were

used in the work done here. The onions were

kept in the laboratory at room temperature to

ensure that all samples are at the same initial

condition. Onions taken at random and the

top (base of scape), bottom (disc and

adventitious roots) and dry outer covering

(tunic) are removed with a sharp knife. This

is then shredded using the Benriner Japanese

Mandolin Shredder into 1mm fine, 2mm

mid and 3mm coarse sizes with the

appropriate blades. The shredded onions are

then kept in a covered plastic container to

prevent air contact

Experimental Set-up and Experimentation

Fig. 2 Experimental Set-up

The set-up of the experiment is shown

in Fig. 2. The set-up comprises the

Refractance Window dryer (1) placed atop

of a cylinder burner (2). Water is poured

into the basin through the overhead opening

(3) until and after the water bath contacts the

plastic film. The digital thermometer (4)

which monitors the temperature of the water

bath is placed on a support so that its probe

can adequately dip into the water bath. The

USB thermocouple (5) reads the temperature

of the drying material-red onion samples (6)

and is connected to a computer logging

system (7). The air-vapour mixture above

the dryer is removed by the extractor fan (8).

20 grams of the onion samples were

weighed in the OHAUS digital weighing

balance for each drying run. Drying was

done starting from 5 minutes with

increments of five minutes up to 70 minutes

for each sample size. The 20 grams weighed

sample was spread into a thin film on the 6

in by 6 in drying area of the Refractance

Window dryer. The drying was then timed

using a countdown timer with alarm to alert

the end of the drying time. The ‘dried’

samples were then removed from the drying

area after drying time had elapsed and put in

the plastic jars labelled accordingly. The

moisture contents of the dried samples were

thereafter measured with the OHAUS

moisture analyser which gives results as

initial mass of sample, % moisture, % solids

and mass of solids (in grams).

Analysis of Experimental Data

Moisture content on dry basis was

calculated from the data obtained as in

equation 1


53

1

1

Where M.C. is moisture content on dry

basis.

Bulk Density ( b ) and Rehydration Ratio

(RR) Determination:

The bulk densities and the rehydration ratio

of the dried onion samples were determined

using the procedure described by Abul-Fadl

and Ghanem (2011).

For the bulk density, 3 grams of the dried

onion samples and the purchased onion

powder (control sample) were poured into a

10 ml graduated measuring cylinder and

then tapped to reduce inter-particle pore

spaces. The volume occupied was recorded.

The bulk density was then calculated as in

equation 2. This procedure was carried out

in triplicate.

s

sb

V

M 2

Where ρb is bulk density, Ms is mass of

sample used in grams and Vs is volume in

ml occupied by sample in the measuring

cylinder.

Modeling the Drying Characteristics of

Onion

Drying curves were plotted from the data

obtained from the experiments by plotting

the Moisture Content (dry basis) and

Moisture Ratio (MR) against Drying Time.

The Moisture Ratio (MR) also called

Dimensionless Moisture Content is an

important property of drying materials when

considering their Drying Kinetics and was

calculated from experimentally observed

data according to equation 3.

ei

et

MCMC

MCMCMR

3

Where MCt is the moisture content

of onion after drying for time t; MCe is the

equilibrium moisture content of dried onion

and MCi is the initial moisture content of

fresh onions all in the unit of kg of water

removed/kg of solids.

The drying curves were fitted to

thirteen thin-layer drying models given in

the works of Haghi and Ghanadzadeh

(2005), Mohamed et al. (2010), and Taheri –

Garavand et al. (2010). They are listed in

Table 1. To calculate the parametric

coefficients (k, n, a, b, c, d, e, f, g, h) of each

model and select the best model for

describing the drying curves, nonlinear

optimization was carried out using a data

regression software called Datafit 9.1

developed by Oakdale Engineering,

Oakdale, (2014) PA USA. The software

employs the Levenberg-Marquardt method

with double precision to carry out the

nonlinear regression (Gavin, 2013).

54

Table 1 Thin Layer Drying Models

No. Model Name Model

1 Newton MR = exp (-k.t)

2 Page MR = exp (−k.tn)

3 Henderson and Pabis MR = a.exp (-k.t)

4 Logarithmic MR = a.exp (-k.t) + c

5 Two term MR = a.exp (−k0.t) + b exp (−k1.t)

6 Two term exponential MR = a.exp (-k.t) + (1-a) exp (-k.a.t)

7 Wang and Singh MR = 1+ a.t + b.t2

8 Approximation of diffusion MR = a.exp (-k.t) + (1-a).exp (-k.b.t)

9 Modified Henderson and Pabis MR = a.exp (-k.t)+b.exp (-g.t)+c.exp (-h.t)

10 Verma et al. MR = a.exp (-k.t) + (1-a).exp (-g.t)

11 Aghbashlo et al. MR = exp (-k1.t/1+k2.t)

12 Midilli et al. MR = a.exp (−k.tn) + b.t

13 Haghi and Ghanadzadeh MR = a.exp (-b.tc) + d.t

2 + e.t + f

Statistical Analysis of Modeling Result:

Software used to Analyze Results

The Datafit 9.1 software (Oakdale

Engineering, 2014), carried out regression

analysis on the Moisture Ratio-Time data

supplied to it with the 13 kinetic models in

Table 1 employing the Levenberg-

Marquardt double precision method (Gavin,

2013). After solution, the software ranked

the models in order of fit from best to worst

using Coefficient of Multiple Determination

(R2) as criterion. The statistical measures of

interest (Taheri-Garavand et al., 2011) were

given in the Fit information category and

they include Coefficient of Multiple

Determination (R2) and reduced Sum

Squared of Errors (SSE or χ2).

RESULTS

The results are presented by discussing

the effect of sample size on the drying time

of red onions, the regression analysis carried

out to determine the thin-layer mathematical

model that best describes this drying and the

trend of rate of drying against moisture

content variation. Furthermore the quality of

the onions dried in this work is compared

with a dried onion obtained from market.

The plot of Moisture Content Time data

are shown in Figs. 3 and 4. The figures

shows the time taken for the onions of

various sizes to dry close to the equilibrium

moisture content. From the plots it can be

easily observed that size affects the time of

drying of the samples. The 1 mm sample

dried fastest getting to moisture content of

less than 0.2 kg water per kg dry solids

within 40 minutes of drying. Close

observation however shows that during the

first 15 minutes of drying the 1 mm shred

sized onion dries at a slower rate than the 2

mm and 3 mm shred sized onions. This can

be attributed to the fact that sample

preparation (shredding) for sizes as small as

1 mm thickness of onions usually lead to a

paste-like outcome and preparation of 2 mm

size and above results in individual onion

particles. This therefore means that heat

transfer into the onion is faster for the 2 mm

and 3 mm sizes than for the 1 mm size due


55

to more exposed surface area. There is a

sharp transition between 15 and 20 minutes

of drying when the 1 mm sized onion dries

faster than the larger sizes. The 2 mm and 3

mm sized samples took 60 minutes and 70

minutes respectively, to dry below moisture

content of 0.2 kg water per kg dry solids.

Effect of Sample Size on Drying Time of Onions

Fig. 3 Plots of Moisture Content Versus Drying Time

Fig. 4 Plots of Moisture Ratio Versus Drying Time

Fit of Thin-Layer Drying Models

The fit of the models to the data is the

degree to which the mathematical models

represent the drying behaviour of the red

onions using the Refractance Window

drying technique. It must however be noted

that drying data obtained from drying red

onions using other drying techniques may

yield different regression results.

In Table 3 the important regression

results of all the 13 models are shown for

the 3 drying cases. The reported regression

parameters include the Coefficient of

0

1

2

3

4

5

6

7

8

0 20 40 60 80

Mo

istu

re C

on

ten

t (K

g

Wa

ter/K

g D

ry S

oli

d)

Drying Time (min)

Variation of Moisture Content with Time

1 mm Shred Size

2 mm Shred Size

3 mm Shred Size

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80

Mo

istu

re r

atio

Drying Time (Min)

Variation of Moisture ratio with time

MR (Model) 1mm

MR (Model) 2mm

MR (Model) 3mm

MR (Exp) 1mm

MR (Exp) 2mm

MR (Exp) 3mm


56

Multiple Determination (R2) and Sum

Squared of Errors (χ2) and fit of a model is

determined by how close R2 is to 1 and how

close to zero χ2

is. In Table 3 R2 and χ

2

values are given for the three size scenarios,

i.e. 1 mm, 2 mm, 3 mm shredded samples.

For the drying of 1 mm shredded

sample, the highest value (i.e. closest to 1)

of R2 is 0.99196 and the lowest value (i.e.

closest to zero) of χ2

is 0.01265 given by the

Haghi and Ghanadzadeh (2005) model.

Conversely, the lowest value of R2 is

0.00000 (the χ2 value returns with no result)

given by the Aghbashlo et al. (2009) model.

This result makes the Taheri-Garavand)

model the best model for predicting the

drying behaviour of red onions when dried

in 1 mm shred size while the Aghbashlo et

al. (2009) model is most inappropriate.

For the 2 mm and 3 mm sized samples,

the Haghi and Ghanadzadeh (2005) model

also came out with the best fit for predicting

drying behaviour of red onions shredded to

the respective sizes. For the 2 mm shred size

the R2 and χ

2 values for the Haghi and

Ghanadzadeh (2005) model are 0.98570 and

0.01600 respectively while for the 3 mm

sized sample the values are 0.99523 and

0.00489 respectively. The worst fit is given

by Aghbashlo et al. (2009) model for the 2

mm sized sample and Aghbashlo et al.

(2009) and Two Term Exponential models

for the 3 mm sized sample with no values of

R2 and χ

2.

It is important also to compare the best

regression values from all the 3 cases to

conclude as to the drying of which onion

shred size and shape is best understood and

best predictable. The best case therefore is

the case with the highest R2 value and

lowest χ2 value. The 3 mm onion shred size

case is the best having the highest R2 value

of 0.99523 and lowest χ2 value of 0.00489.

|Drying Rate-Moisture Content

Relationship

The drying data are also presented by

plotting Rate of Drying against moisture

content and the resulting plots are called the

Krischer curves (Kemp et al, 2001). They

are used to describe the drying regimes of

drying materials. Krischer curves are plotted

for the drying data obtained from the

experiment and are shown in Fig 5a, 5b and

5c. According to these plots, the start of

drying is indicated by the free end of the

curve and the end of drying is at nearly zero

moisture content.

On inspection of Fig. 5a to 5c the initial

drying rate is high then it decreases as

moisture content decreases. This trend

would look unexpected if compared to a

typical Krischer Curve obtained from a

conventional convective air drying. The

Krischer Curve of a typical drying process

starts with an initialization period (not

experienced for all substances according to

Moyers and Baldwin (1999) followed by a

constant rate period and then a falling rate

period. The deviation in the nature of the

Krischer Curves obtained in this work can

be explained by the working method of the

Refractance Window technique. As wet

onion sample is placed on the transparent

plastic film, a rush of infrared energy occurs

which causes the initial drying rate to be

high. However, as the drying proceeds the

‘window’ closes leading to a gradual

reduction in the infrared energy going

through the plastic film to heat up the


57

onions. This is observed by the decrease in

the rate of evaporation of moisture from the

onions. A second phase of increasing drying

rate is also observed during this period as

moisture content decreases.

Table 2 Results of Regression Analyses of Thin Layer Drying Models

1 mm Onion Shred

Size

2 mm Onion Shred

Size

3 mm Onion Shred

Size

Model Name R2 SSE R

2 SSE R

2 SSE

Newton 0.9299 0.1104 0.9792 0.0233 0.9874 0.0129

Page 0.9901 0.0157 0.9792 0.0233 0.9910 0.0093

Henderson and Pabis 0.9386 0.0966 0.9793 0.0232 0.9886 0.0117

Logarithmic 0.9475 0.0827 0.9803 0.0220 0.9888 0.0115

Two term 0.9386 0.0966 0.9805 0.0186 0.9915 0.0087

Two term exponential 0.2499 1.1807 0.4098 0.6601 0.0000 -

Wang and Singh 0.9353 0.1018 0.8331 0.1867 0.9471 0.0542

Approximation of

diffusion 0.9798 0.0317 0.9792 0.0233 0.9874 0.0129

Modified Henderson

and Pabis 0.9386 0.0966 0.9805 0.0219 0.9915 0.0086

Verma et al 0.7142 0.4499 0.8480 0.1700 0.3517 0.6644

Aghbashlo et al 0.0000 - 0.0000 - 0.0000 -

Midilli et al 0.9917 0.0131 0.9804 0.0219 0.9929 0.0073

Haghi and

Ghanadzadeh 0.9920 0.0127 0.9857 0.0160 0.9952 0.0049

This can be explained by the fact that

after the surface of the drying material in

contact with the plastic film is dried due to

the initial fast drying, the window of

infrared energy is shut and conduction heat

transfer takes over as the predominant from

of energy transfer. As conduction takes over,

the rate of drying increases once more.

Further into the drying, a general sharp

transition is observed, a sharp sloping of the

curve to the origin. This is expected as

compared to a typical Krischer Curve and is

referred to as the falling rate period.


58

Fig. 5 Rate of Drying-Moisture Content Curves

Quality of the Refractance Window Dried Onions

The results of bulk density measurement are presented in Table 3.

Table 3: Bulk Density Measurement Comparison

Volume Occupied

(ml)

Bulk Density

(calculated) (g/ml)

Experimental

Sample

Control

Sample

Experimental

Sample

Control

Sample

1 6.6 6.6 0.4545 0.4545

2 6.8 6.5 0.4412 0.4615

3 6.6 6.4 0.4545 0.4688

Average 0.4501 0.4616

The result in Table 3 shows that the

experimental dried onion is of comparable

quality in terms of bulk density with the

bulk density of the control sample only

marginally higher.

0

0.1

0.2

0.3

0.4

0 2 4 6 8 Dry

ing

Ra

te (

Kg

Wa

ter/K

g

Dry

So

lid

.Min

)

Moisture Content (Kg Water/Kg Dry Solid)

(a)Krischer Curve for 1mm Shred

Size

0

0.1

0.2

0.3

0.4

0.5

0.6

0 2 4 6

Dry

ing

Ra

te (

Kg

Wa

ter/K

g D

ry

So

lid

.Min

)

Moisture Content (Kg Water/Kg Dry

Solid)

(b)Krischer Curve for 2 mm

Shred Size

0

0.1

0.2

0.3

0.4

0 2 4 6 8

Dry

ing

Ra

te (

Kg

Wa

ter/K

g

Dry

So

lid

.Min

)

Moisture Content (Kg Water/Kg Dry

Solid)

(c)Krischer Curve for 3 mm Shred

Size


59

CONCLUSIONS

From the results the following conclusions

can be drawn, with respect to Refractance

Window drying technique:

1. The size of onion sample to be dried

affects the drying time; the time of

drying increases with the size of the

onion shreds to be dried. It is best to

prepare the onions in smaller shreds.

2. Regression analysis revealed that the

best model for predicting the drying

behaviour of 1 mm, 2 mm and 3 mm

sized onion shreds is the Haghi and

Ghanadzadeh model with R2 values of

0.99196, 0.98570 and 0.99523

respectively Given below are the details

of this conclusion.

3. For the 1 mm, 2 mm and 3 mm shred

sizes, Parametric Values in Haghi and

Ghanadzadeh Model:

ftetdtbaMR c ...exp. 2 are

given in Table 4 The 3 mm shred size

onion is best predictable by a

mathematical model (Haghi and

Ghanadzadeh model) having the highest

Coefficient of Determination value of

0.99523 and the lowest χ2 value of

0.00489 of all the best fit models.

4. The Krischer Curves (Drying rate-

Moisture content plots) of the shredded

samples reveal that the working

principle of Refractance Window drying

affects the nature of the curves compared

to Drying rate-Moisture content curves

of conventional convective drying. Also

the drying in the falling rate period

occurred in two phases.

5. The onion powder produced by the

Refractance Window drying in this work

is of comparative quality to that of the

dried onion powder found on the market.

It had a Bulk Density of 0.4501 g/ml

Table 4: Parametric Values for Haghi and Ghanadzadeh Model

1 mm Shred Size 2 mm Shred Size 3 mm Shred Size

a 0.8259 0.8017 0.3695

b 0.0006 0.0394 0.0467

c 2.7160 1.4615 1.4862

d 0.0000 0.0000 0.0001

e -0.0049 -0.0039 -0.0143

f 0.1218 0.1896 0.6273

REFERENCES

Abasi, S., Mousavi, S.M., and Kiani, S.

(2009) Effect of Time and Temperature

on Moisture Content, Shrinkage and

Rehydration of Dried Onions. Iranian

Journal of Chemical Engineering, Vol. 6,

No. 3, Summer 2009, pp. 57-70.

Abdul-Fadl M.M. and Ghanem T.H. (2011).

Effect of Refractance-Window (RW)

Drying Method on Quality Criteria of

Produced Tomato Powder as compared to

the Convection Drying Method. World

Applied Sciences Journal 15; ISSN 1818-

4952, pp 953-965.

Aghbashlo, M., Kianmehr, M. H., and

Arabhosseini, A., (2009), Modeling of

thin-layer drying of potato slices in


60

length of continuous band dryer, Energy

Conversion and Management, 50(5),

1348-1355.

Baron Spices and Seasoning, (2014),

Dehydrated Onion and Garlic Products-

Rehydration. Available from

http://www.baronspices.com/spice_handb

ook/rehydration.html [Accessed 29-09-

2014]

Clarke, P.T. (2004) Refractance Window:

“Down Under”. Drying 2004 –

Proceedings of the 14th International

Drying Symposium (IDS 2004), São

Paulo, Brazil, 22-25 August 2004, vol. B,

pp. 813-820.

Gavin H, (2003), The Levenberg-Marquardt

method for nonlinear least squares curve-

fitting problems. Department of Civil and

Environmental Engineering, Duke

University Durham, NC 27708, USA.

Retrieved January 08, 2015 from

http://people.duke.edu/~hpgavin/ce281/l

m.pdf.

Haghi, A.K. and Ghanadzadeh, H., (2005),

A Study of Thermal Drying Process.

Indian Journal of Chemical Technology,

Vol. 12, November 2005, pp. 654-663

Idah, P.A., Musa, J.J. and Olaleye, S.T.,

(2010) Effect of Temperature and Drying

time on some Nutritional Quality

Parameters of Tomatoes, AU J.T. Vol. 14

No., July 2010, pp. 25-32

Kemp I.C., Fyhr C.B., Laurent S., Roques

M.A., Groenewold C.E., Tsotsas E.,

Sereno A.A., Bonazzi C.B., Bimbenet J-

J., Kind M. (2001). Methods For

Processing Experimental Drying Kinetics

Data. Drying Technology, 19: 15–34.

Mohamed, M.A., Gamea, G.R. and Keshek,

M.H. (2010) Drying Characteristics of

Okra by Different Solar Dryers. Misr J.

Ag. Eng., 27(1): 294 – 312

Midilli A, Kucuk H, Yapar Z (2002) A new

model for single layer drying. Dry

Technol l20 (7):1503-1513.

Moyers, C.G. and Baldwin, G.W. (1999)

Drying Periods (Diagram). In: Perry,

R.H. et al. (eds) Perry’s Chemical

Engineering Handbook. 7th ed. McGraw-

Hill, New York, p. 1184

Mujumdar, A.S. (2006). Principles,

Classification and Selection of Dryers.

In: Mujumdar A.S. (Ed.) Handbook of

Industrial Drying, Taylor and Francis

group LLC.

Nindo, C.I. and Tang, J. (2007). Refractance

Window Dehydration Technology: A

Novel Contact Drying Method, Drying

Technology, 25, pp 37–48

Oakdale Engineering, (2014), Datafit 9.1

Software (Build 9.1.32) developed by

Oakdale Engineering, 23 Tomey Road

Oakdale, PA 15071 USA

Osiberu, A.S. (2014). Inspection of Dried

Products Using a Refractance Window

Drying System; Visit to Plant Site,

Sagamu, Ogun State, Nigeria, September

3, 2014

Sani B. M. and Jaliya M. M. (1993) Onion

Production and Management under

Irrigation, Nation Agricultural Extension

and Research Liason Services, Federal

Ministry of Agriculture and Eater

Resourses, Ahmadu bello University,

Zaria.

Shahari, N.A., (2012), Mathematical

Modelling of Drying of Food Products:

Application to Tropical Fruits, PhD

Thesis, University of Nottingham, United

Kingdom.


61

Taheri-Garavand, A., Rafiee, S. and

Keyhania, A. (2010) Mathematical

Modeling of Thin Layer Drying Kinetics

of Tomato Influence of Air Dryer

Conditions. International Transaction

Journal of Engineering, Management, &

Applied Sciences & Technologies, 2(2):

147-160, 2011

Vega-Mercado, H., Gongora-Nieto, M.M.

and Barbosa-Canovas, G.V., (2001),

Advances in dehydration of foods.

Journal of Food Engineering, 49, pp.

271-28.


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