Experimental and numerical investigation of transport phenomena and kinetics for convective shrimp drying

Experimental and numerical investigation of transport phenomena and kinetics for convective shrimp drying Contents lists available at ScienceDirect Case Studies in Thermal Engineering journal homepage[.]

Contents lists available atScienceDirect Case Studies in Thermal Engineering journal homepage:www.elsevier.com/locate/csite

Experimental and numerical investigation of transport phenomena

and kinetics for convective shrimp drying

aFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology, VNU-HCM, No 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh

City, Viet Nam

bFaculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, No 12 Nguyen Van Bao Street, Go Vap District, Ho Chi Minh City, Viet

Nam

A R T I C L E I N F O

Keywords:

Heat and mass transfer

Numerical simulation

Shrimp drying

Transport properties

A B S T R A C T

In this study, an experimental apparatus was used to produce dried shrimp From experiments with various air velocities and temperatures, the drying constant, moisture transfer coefficient

and moisture diffusivity were obtained by using a Bi-Di correlation An equation for determining

the drying constant was established to determine the drying parameters for different drying conditions To obtain the targeted moisture, i.e., a moisture content of 0.25 (dry basis, d.b.), drying with the highest temperature and air velocity (60 °C, 2 m/s) took 3.6 h, while the lowest temperature and air velocity (50 °C, 1 m/s) required up to 5.8 h The transport parameters were then used to simulate the temperature and moisture content distribution inside the shrimp using ANSYS software for a certain drying condition The results showed that the shrimp temperature increased rapidly, reaching the dry air temperature after approximately 15 min The moisture content in the tail was markedly lower than that at the centre, which is the thickest part of the shrimp The data from this study can be used to optimize the energy consumption in shrimp convective drying technologies

1 Introduction

Vietnam has one of the highest seafood catches in the world Seafood exports also account for a high proportion of the national economy; exports increased from $ 1 billion in 2000 to $ 8.3 billion in 2017, of which a large proportion is wild-caught and farmed shrimp From 2007 to 2017, the proportion of pangasius decreased from 73% to 47%, the proportion of shrimp increased from 11% to 18%, and the proportion of tuna increased from 9% to 30%, according to the 2018 report from the Vietnam Association of Seafood Exporters and Producers (VASEP) Shrimp is stored by freezing or drying Shrimp is commonly stored in coastal areas by sun drying and hot smoke drying from coal burning, manual and seasonal processes However, scholarly research on shrimp drying has been done worldwide, especially in Thailand Prachayawarakorn et al [1] studied the characteristics of shrimp drying using steam and hot air Erdoǧdu et al [2] constructed graphs to optimize the industrial shrimp cooking process to maintain shrimp quality Shrimp drying in a jet-spouted bed has been studied extensively [3–5] In these studies, the hydrodynamic behaviour in the drying chamber, the shrinkage of shrimp during drying, and the quality of dried shrimp were reported Namsanguan et al [6] proposed two-stage drying of shrimp using superheated ste am and a heat pump or hot air The results showed that the two-stage dryer had higher quality dried shrimp than that from a single-stage dryer Information technology has also been thoroughly applied to shrimp drying in recent

https://doi.org/10.1016/j.csite.2019.100465

Received 3 April 2019; Received in revised form 13 May 2019; Accepted 14 May 2019

∗Corresponding author

E-mail address:nmphu@hcmut.edu.vn(M.P Nguyen)

Available online 15 May 2019

2214-157X/ © 2019 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/BY-NC-ND/4.0/)

T

years Hosseinpour et al [7,8] applied computer vision to analyse the colour and shrinkage of shrimp during drying Recently, Akonor et al [9] compared how different drying technologies affect the amount of protein and fat in shrimp, and the overall quality

of dried shrimp More recently, Cheng et al [10] used non-destructive quality control techniques to monitor the dynamic states of water in shrimp during drying They proved that water vaporization in shrimp was mainly due to the trapped water

In the present study, we performed forced convection drying with an experimental apparatus to provide data on shrimp drying properties, such as mass transfer coefficient, drying constant, moisture diffusivity, and drying curves A few previous studies have provided some kinetic and transport properties for shrimp drying However, the data did not span a wide range of drying conditions, and did not illustrate how to apply the moisture transfer parameters Complete information is useful for the optimization and design

of experiments with various drying technologies, such as heat pump drying, solar energy, hot smoke and so on Therefore, the drying kinetics, mass transfer parameters and application to the distribution simulation of temperature and moisture content in the dried

shrimp are the main objectives of this paper The Bi-Di correlation is a mathematical model to calculate the heat and mass transfer

coefficients and the moisture diffusivity This is a widely used mathematical model in food drying studies [11–16] From the obtained data, the governing equations, initial and boundary conditions were established to simulate the distribution of temperature and moisture in dried shrimp

2 Materials and methods

2.1 Sample preparation

The shrimp size used in the experiment was 130 shrimp kg-1 The shrimp was boiled in a salt solution (2% w/v) with a boiling time of 7 min, and then preliminarily dehydrated in a centrifugal machine before drying The moisture content of the preliminarily

dehydrated shrimp was M i= 1.86 (dry basis, d.b.)

2.2 Experimental description

The experimental apparatus for forced convection shrimp drying is shown inFig 1 The model consisted of a 38 W fan and nine

130 W resistors The fan speed and resistor power were controlled by variable resistors The pipe diameter in the experimental apparatus was 200 mm A thin layer of shrimp was placed on a stainless-steel drying tray The weight of the dry matter in shrimp was determined by oven drying (Binder dryer) at 105 °C for 24 h The drying air temperature was measured by a TESTO 735 thermometer ( ± 0.1 °C) The air velocity was measured using a PCE-007 air flow metre ( ± 3%) The shrimp weight was measured on an electronic balance ( ± 0.01 g)

2.3 Moisture content and drying

The moisture content was estimated using the simplified moisture ratio equation [13]:

Fig 1 Experimental apparatus.

Y M

To determine the moisture transport mechanism, a semi-logarithmic plot (lnY-t) was constructed and fitted with a linear function.

This is the same model used by Henderson and Pabis, which was a good fit for the experimental drying data to describe the thin-layer drying of shrimp [17]:

Y St G or Y G St

where t is the drying time, S is the drying coefficient and G is the lag factor.

The moisture diffusivity was calculated by using Dincer and Hussain's equation [18]:

=

D SL

µ

2

where L is the shrimp half-thickness, D is the moisture diffusivity, and µ1is a root of the transcendental characteristic equation, as follows:

The moisture transfer coefficient can be obtained from the definition of the Biot number, as follows:

=

h D Bi

L

The Biot number was calculated from relationship between the Biot and Dincer numbers This correlation was adopted for several types of foodproducts subjected to drying [18]:

=

where Di is the Dincer number, developed as follows:

=

Di v

where v is the flow velocity of drying air.

3 Experimental results and discussion

Fig 2a presents the change in moisture ratio with drying time for a drying condition of 1.5 m/s and 50 °C, as a typical illustration

A curve was fit to the experimental data to determine the lag factor G and drying coefficient S (R2= 96.89%) For the presented

drying condition, the values of G and S were 0.958087 and 1.2444 ×10-4s-1, respectively

The moisture diffusivity, moisture transfer coefficient, and the Biot number were then calculated by using Eqs.(3)–(7), as follows:

D = 6.67 ×10-9m2/s

h m= 1.432×10-7m/s

Bi = 0.0965

Similarly, experiments with different drying conditions were performed, and the results are tabulated inTable 1

Table 1shows the lag factor G was almost independent of the drying conditions and that its value was approximately unity The drying constant S denotes the drying capacity of a solid per unit of time Compared to other types of food (fruits or vegetables), the S

for shrimp drying is relatively small, which indicates the shrimp drying time is longer than that of other foods in the same drying condition [11–13] The parameters D and h mplay important roles in determining the moisture content distribution in a product The obtained values were all in the range 0.1 ≤ Bi ≤ 100, indicating that during shrimp drying, there were finite internal and surface

resistances Among these parameters, the drying coefficient S is relatively significant for computing the mass transfer parameters Therefore, an equation of S as a function of drying conditions T and v was developed as Eq.(8)with R2= 99%:

S = -2.40374 ×10-4+5.79192×10-6T-3.00167 ×10-8T2+1.62786×10-4v-4.17417 ×10-5v2

(8) Fig 2b shows nine drying curves from the model = Y Gexp( St)at the drying conditions given inTable 1 The curves were built from the initial moisture content of 1.86 d.b to the final moisture content of 0.25 d.b [4,5], i.e., the moisture ratio Y = 1 to 0.134.

Judging fromFig 2b, to obtain the final moisture, the drying condition with the highest temperature and air velocity (60 °C, 2 m/s) took 3.6 h, while the drying condition with the lowest temperature and air velocity (50 °C, 1 m/s) required up to 5.8 h

The uncertainty analyses for lag factor and drying constant were performed from the measured data The analysis method is presented in the literature [19] which was proposed by Kline and McClintock The uncertainty in the calculated result was estimated

as follows:

=

x U

R

i

n

i i

1

2

(9)

where U R is the uncertainty in the calculated result R, and U i is the uncertainty in the measured quantity x i

The error calculation was performed using EES software [20] The maximum errors in lag factor and drying constant were found

to be 4.35% and 6.96%, respectively

Fig 2 a) Changes in moisture ratio with drying time at 1.5 m/s and 50 °C b) Changes in moisture ratio with time at various drying conditions Table 1

Regression model coefficients and the calculated mass transfer parameters

Drying conditions Model = Y Gexp( St) Mass transfer parameters

4 Numerical simulation of moisture and temperature distributions

A mathematical model was developed to predict the temperature and moisture content inside the shrimp during the convective drying process Heat transfer within shrimp is described by the conduction equation, with conditions as follows:

=

T

t

k

The initial and boundary conditions were:

t = 0; T = T i

=

k T h T t( s T )

Fick's second law equation was applied to simulate the moisture transfer:

=

M

The initial and boundary conditions were:

t = 0; M = M i

=

D M h M m( s M e)

where M eis the equilibrium moisture content, which can be evaluated by Oswin's equation [21] as follows:

=

RH

0.103

1

e

0.645

(12)

where RH is the relative humidity of the drying air The range of relative humidity was from 0.21 to 0.28 in the simulations The thermal conductivity k, specific heat capacity c pand density sof shrimp were taken from the study of Niamnuy et al [4] The heat

transfer coefficient, h t , was calculated from Pohllhausen's equation [22,23]

The distribution of moisture and temperature in the shrimp was simulated by using commercial software [24] based on finite volume discretization The 3D model of the shrimp and the mesh are shown inFig 3 A hexahedral mesh was used in the current study Double precision was used to improve the accuracy of the results A convergence criterion of 10-6was set for the governing equations

The input parameters for the simulation were conditions corresponding to a drying speed of 1.5 m/s and a drying temperature of

50 °C.Figs 4 and 5show the temperature distribution after 1000 s and the moisture content after 6.5 h The shrimp temperature at this point was fairly uniform and approximately the temperature of the drying air; the moisture of the shrimp tail reached

Fig 3 3D model of shrimp and mesh.

approximately 0.12, while the body moisture was 0.3.

Fig 6presents the moisture ratio at different positions in the shrimp There is a difference in moisture content between the thickest position in the shrimp and the shrimp tail After approximately 6.5 h, the moisture ratio at the shrimp tail was 0.07, while at the thickest position it was 0.2.Fig 7 represents the temperature distribution in the shrimp After a short time (approximately

15 min), the temperature of the shrimp approaches the drying temperature, due to the small shrimp size

The simulation results show a moderate deviation in the moisture ratio compared to the experimental results in the previous section The experimental result (Fig 2a) and the simulation result (Fig 6) indicated that moisture ratios are approximately 0.08 and

0.14 at a drying time of 22000 s, respectively This can be explained by the size difference of the simulated shrimp, Bi-Di correlation

errors (the correlation coefficient of Eq.(6)is approximately 0.8), numerical solution errors and experimental process variations The simulation does not consider shrimp shrinkage or the latent heat of vaporization

5 Conclusions

Experiments with convective drying for several drying conditions (50–60 °C; 1–2 m/s) were conducted to dry shrimp The shrimp

drying behaviour was described by the Bi-Di correlation An equation for determining the drying constant was established to

de-termine the drying parameters for different drying conditions A 3-dimensional numerical simulation was performed to dede-termine the moisture and the temperature distribution in a shrimp The main findings are as follows:

Fig 4 Temperature distribution after 1000 s.

Fig 5 Moisture content distribution inside shrimp after 6.5 h of drying.

1 The drying coefficient, moisture diffusivity, and the moisture transfer coefficient were in the ranges of 9.522×10-5- 1.5768×10-4

s-1, 4.34×10-9- 2.127×10-8m2/s, and 9.81×10-8- 4.478×10-7m/s, respectively

2 The shrimp temperature increased rapidly, approaching the dry air temperature during the initial 15 min

3 The moisture at the shrimp tail is quite low compared to that at its centre (the thickest point)

The current research results can enable the energy consumption optimization of drying processes for respective applications, and suggest solutions to enhance temperature uniformity and moisture content distributions in shrimp during drying At a certain drying condition, the power of a heater (heat pump, solar collector, superheated steam dryer, etc.) and a fan can be determined Drying time

is also predicted fromFig 2b, if the final moisture content of shrimp is given A better drying condition yields shorter drying time, but the power consumption is higher, and vice-versa The minimum energy consumption can be found from this trade-off

Conflicts of interest

The authors declare that there is no conflict of interests regarding the publication of this paper

Nomenclature

Bi Biot number (dimensionless)

cp Specific heat capacity of shrimp (J/kg-K)

Fig 6 Moisture ratio at the body and tail of shrimp with respect to drying time.

Fig 7 Temperature at different positions.

D Moisture diffusivity (m2/s)

Di Dincer number (dimensionless)

G Lag factor (dimensionless)

hm Moisture transfer coefficient (m/s)

ht Heat transfer coefficient (W/m2-K)

k Thermal conductivity of shrimp (W/m-K)

L Shrimp half-thickness (m)

M Moisture content (kg/kg, dry basis)

RH Relative humidity of drying air (decimal)

S Drying coefficient (s-1)

T Temperature (°C)

v Flow velocity of drying air (m/s)

Y Moisture ratio (dimensionless)

ρs Density of shrimp

Subscripts

Appendix A Supplementary data

Supplementary data to this article can be found online athttps://doi.org/10.1016/j.csite.2019.100465

Reference

[1] S Prachayawarakorn, et al., Desorption isotherms and drying characteristics of shrimp in superheated steam and hot air, Dry Technol 20 (3) (2002) 669–684 [2] F Erdoǧdu, M.O Balaban, W.S Otwell, Construction of shrimp cooking charts using previously developed mathematical models for heat transfer and yield loss predictions, J Food Eng 60 (1) (2003) 107–110

[3] S Devahastin, R Tapaneyasin, A Tansakul, Hydrodynamic behavior of a jet spouted bed of shrimp, J Food Eng 74 (3) (2006) 345–351

[4] C Niamnuy, et al., Modeling coupled transport phenomena and mechanical deformation of shrimp during drying in a jet spouted bed dryer, Chem Eng Sci 63 (22) (2008) 5503–5512

[5] C Niamnuy, S Devahastin, S Soponronnarit, Effects of process parameters on quality changes of shrimp during drying in a jet-spouted bed dryer, J Food Sci 72 (9) (2007) E553–E563

[6] Y Namsanguan, W Tia, et al., Drying kinetics and quality of shrimp undergoing different two-stage drying processes, Dry Technol 22 (4) (2004) 759–778 [7] S Hosseinpour, et al., Application of computer vision technique for on-line monitoring of shrimp color changes during drying, J Food Eng 115 (1) (2013) 99–114

[8] S Hosseinpour, S Rafiee, S.S Mohtasebi, Application of image processing to analyze shrinkage and shape changes of shrimp batch during drying, Dry Technol.

29 (12) (2011) 1416–1438

[9] P.T Akonor, et al., Drying characteristics and physical and nutritional properties of shrimp meat as affected by different traditional drying techniques, Int J Food Sci (2016) 1–5 7879097

[10] S Cheng, Y Tang, et al., Approach for monitoring the dynamic states of water in shrimp during drying process with LF-NMR and MRI, Dry Technol 36 (7) (2018) 841–848

[11] X Liu, H Hou, J Chen, Applicability of moisture transfer parameters estimated by correlation between Biot number and lag factor (Bi–G correlation) for convective drying of eggplant slices, Heat Mass Transf 49 (11) (2013) 1595–1601

[12] M Beigi, Influence of drying air parameters on mass transfer characteristics of apple slices, Heat Mass Transf 52 (10) (2016) 2213–2221

[13] V Mrkić, M Ukrainczyk, B Tripalo, Applicability of moisture transfer Bi–Di correlation for convective drying of broccoli, J Food Eng 79 (2) (2007) 640–646 [14] D Ghanbarian, M Baraani Dastjerdi, M Torki-Harchegani, Mass transfer characteristics of bisporus mushroom (Agaricus bisporus) slices during convective hot air drying, Heat Mass Transf 52 (5) (2016) 1081–1088

[15] A Kaya, O Aydin, M Akgun, Drying kinetics and moisture transfer parameters of hazelnut, J Food Process Preserv 35 (5) (2011) 714–721

[16] H.Y Ju, H.M El-Mashad, et al., Drying characteristics and modeling of yam slices under different relative humidity conditions, Dry Technol 34 (3) (2016) 296–306

[17] H Darvishi, A Farhang, E Hazbavi, Mathematical modeling of thin-layer drying of shrimp, Glob J Sci Front Res (GJSFR) 12 (3) (2012) 82–89 [18] I Dincer, M.M Hussain, Development of a new Bi–Di correlation for solids drying, Int J Heat Mass Transf 45 (15) (2002) 3065–3069

[19] J Holman, Experimental Methods for Engineers, eighth ed., McGraw-Hill, 2012

[20] S.A Klein, Engineering Equation Solver, F-Chart Software, 2013

[21] S Tirawanichakul, W NA Phatthalung, Y Tirawanichakul, Drying strategy of shrimp using hot air convection and hybrid infrared radiation/hot air convection, Walailak J Sci Technol 5 (1) (2011) 77–100 2011

[22] D Velić, et al., Influence of airflow velocity on kinetics of convection apple drying, J Food Eng 64 (1) (2004) 97–102

[23] E.K Akpinar, Evaluation of convective heat transfer coefficient of various crops in cyclone type dryer, Energy Convers Manag 46 (15) (2005) 2439–2454 [24] ANSYS FLUENT, Theory Guide, ANSYS Inc., Canonsburg, PA, USA, 2013