Food and Bioprocess Technology: An International Journal CFD based analysis of 1-MCP distribution in commercial cool store rooms: porous medium model application --Manuscript Draft-Manuscript Number:
FABT-D-13-00528
Full Title:
CFD based analysis of 1-MCP distribution in commercial cool store rooms: porous medium model application
Article Type:
Original Research
Keywords:
Fruit-storage, Computational fluid dynamics, Diffusion-adsorption, Porous-medium, Simulation
Corresponding Author:
Alemayehu Ambaw Tsige Katholieke Universiteit Leuven Leuven, Leuven BELGIUM
Corresponding Author Secondary Information: Corresponding Author's Institution:
Katholieke Universiteit Leuven
Corresponding Author's Secondary Institution: First Author:
Alemayehu Ambaw Tsige
First Author Secondary Information: Order of Authors:
Alemayehu Ambaw Tsige Pieter Verboven Mulugeta A Delele Thijs Defraeye Engelbert Tijskens Ann Schenk Bert E Verlinden Umezuruike L Opara Bart M Nicolai
Order of Authors Secondary Information: Abstract:
1-methylcyclopropene (1-MCP) is a synthetic plant growth regulator used commercially to delay ripening of fruits. 1-MCP (SmartFreshTM) is applied in gaseous form (as a fumigant) in the cool store room. This paper uses a porous medium CFD model to numerically analyze the distribution of 1-MCP in cool store rooms for apple fruit. The effects of air circulation, room shape and bin material were investigated. Dose calculation based on filling density was explored. The 1-MCP distribution in commercial cool stores, under room cooling condition, was uniform irrespective of room shape. Rooms filled with fruit in wooden bins deplete 25% more of the active substance than rooms filled with fruit in plastic bins. The calculated dose increases linearly with the amount of fruit in the cool store (filling density). Hence, this study suggests that filling density based dose prescription is feasible.
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Manuscript Click here to download Manuscript: MANUSCRIPT_TSIGE.docx Click here to view linked References 1 2 3 4 5 1 6 7 2 8 9 3 10 11 4 Alemayehu Ambaw(a), Pieter Verboven(a), Mulugeta A. Delele(b), Thijs Defraeye(a), Engelbert 12 13 5 Tijskens(a), Ann Schenk(d), Bert E. Verlinden(d), Umezuruike Linus Opara(c), Bart M. Nicolai(a, d *). 14 6 15 a 7 BIOSYST-MeBioS, Katholieke Universiteit Leuven, Willem de Croylaan 42, B-3001 Leuven, 16 8 Belgium 17 18 b 19 9 Leibniz institute of Agricultural Engineering. Potsdam, Germany 20 21 c 10 Postharvest Technology Research Laboratory, South African Chair in Postharvest Technology, 22 11 Faculty of AgriSciences, Stellenbosch University, Private Bag X1, Stellenbosch 7602, South 23 Africa 24 12 25 13 d 26 14 Flanders Centre of Postharvest Technology, Willem de Croylaan 42, B-3001 Leuven, Belgium 27 28 *Corresponding author; email:
[email protected]. 29 15 30 31 16 32 17 33 18 Keywords: 34 Fruit-storage, Computational fluid dynamics, Diffusion-adsorption, Porous-medium, Simulation 35 19 36 37 38 20 39 1-methylcyclopropene (1-MCP) is a synthetic plant growth regulator used commercially to delay 40 21 41 42 22 ripening of fruits. 1-MCP (SmartFreshTM) is applied in gaseous form (as a fumigant) in the cool 43 44 store room. This paper uses a porous medium CFD model to numerically analyze the distribution 45 23 46 47 24 of 1-MCP in cool store rooms for apple fruit. The effects of air circulation, room shape and bin 48 49 material were investigated. Dose calculation based on filling density was explored. The 1-MCP 50 25 51 52 26 distribution in commercial cool stores, under room cooling condition, was uniform irrespective of 53 54 room shape. Rooms filled with fruit in wooden bins deplete 25% more of the active substance 55 27 56 than rooms filled with fruit in plastic bins. The calculated dose increases linearly with the amount 57 28 58 59 60 61 62 63 64 65
CFD based analysis of 1-MCP distribution in commercial cool store rooms: porous medium model application
Abstract
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29
of fruit in the cool store (filling density). Hence, this study suggests that filling density based
30
dose prescription is feasible.
31
1 Introduction
32
Ethylene (C2H4) is a natural plant hormone which plays a vital role in triggering and coordinating
33
ripening of different types of fruit (Gorny and Kader, 1997). Unless purposely added to the
34
storage environment to elicit a specifically required response, C2H4 is undesirable and exposure
35
should be minimized. Hence, controlling its action in plants is of great economic importance to
36
producers, wholesalers, retailers and consumers of fresh fruits, vegetables, and ornamentals.
37
Application of 1-methylcyclopropene (1-MCP, SmartFreshTM) is one technique used to suppress
38
the normal ripening process by blocking the receptor sites that otherwise are occupied by C2H4
39
(Blankenship and Dole, 2003; Huber, 2008; Sisler and Serek, 1997).
40
Plant responses to a given dose of the gas at various temperatures and duration of treatment have
41
been explored (Blankenship and Dole, 2003). On the other hand, little is known of the fate of 1-
42
MCP in the storage environment. Since 1-MCP is applied as a gas, the airflow in the storage
43
room may be important. Non-target solid materials including wooden boxes, cardlinings and
44
other plant based porous materials used in bins have 1-MCP sorption capacity (Ambaw et al.,
45
2011; Vallejo and Beaudry, 2006). The box design, placement and arrangement in the cool room
46
affect the airflow distribution and, hence, may affect the 1-MCP distribution. Studying the effect
47
of these factors by mathematical modeling helps to understand and optimize the process.
48
Numerical approaches, specifically, computational fluid dynamics (CFD) have become
49
increasingly successful in recent years to generate flow and scalar simulations with the help of
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computers (Ambaw et al., 2013a; Alvarez & Flick, 2007; Defraeye et al., 2013; Ferrua & Singh,
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2009; Hoang et al., 2003; 2004; Nahor et al., 2005; Verboven et al., 2006, Zou et al., 2006a,b).
52
Previously, we obtained the basic parameters describing the convection, diffusion and adsorption
53
of 1-MCP in air, in apple fruit and in several non-target solid materials (Ambaw et al., 2011). The
54
calculated parameter values were implemented to develop direct CFD models of small scale 1-
55
MCP application units in storage containers (Ambaw et al., 2012). The influence of air
56
circulation on the uniformity of gas distribution and level of depletion of the active substance
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from the treatment atmosphere by wooden boxes as well as the prospect of dose reduction were
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studied. Although direct CFD models provide detailed information on the gas distribution and
59
adsorption, they are, however, computationally intensive, and it is currently impossible to apply
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this method to full scale commercial cool stores. To overcome this limitation, we developed an
61
equivalent porous medium approach that simplifies the calculations while preserving the main
62
features of the distribution and adsorption process (Ambaw et al., 2013b). The porous medium
63
model agreed well with experimental data as well as with the direct CFD model, confirming its
64
applicability to investigate the gas distribution in large cool stores as presented in the current
65
study.
66
The objective of the present work was to use the porous medium approach to model 1-MCP
67
distribution and adsorption in full scale commercial cool stores. The distribution was compared to
68
experimental measurements in a pilot scale cool room with different fruit loading. In a large cool
69
store, experiments were conducted to assess the firmness of apples at different positions of the
70
room for different doses and to compare plastic to wooden bins. The dose response curves were
71
calculated and compared for different configurations and related to the distribution and firmness
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measurements. Effects of filling density, bin material and air circulation were investigated.
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Finally, using the new knowledge, dose calculations based on filling density were proposed.
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2 Materials and methods
75
2.1 Fruit
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For the pilot cool room experiments, ‘Jonagold’ apples (Malus × domestica Borkh, cv. Jonagold)
77
were purchased directly after harvest from a local grower in Belgium in September, 2011. All the
78
fruit used for the test were free of visual defects. Fruit were stored at 1 °C in normal atmospheric
79
air before and during the experiment that took place within a few days. The mean ± standard
80
deviation (n = 10) of the mass and volume of fruit were 235g ± 32 g and 281 mL ± 36 mL,
81
respectively.
82
The efficacy analysis was accomplished in large scale firmness tests on ‘Jonagold’ apples
83
obtained from local growers in Belgium in autumn, 2007.
84
2.2 Pilot cool room validation
85
Previously the porous medium model was developed and validated using experiments in a 500 L
86
storage container and by comparing it with the direct CFD model (Ambaw et. al, 2013b). In the
87
present study, additional validation experiments were performed using larger stacks in the pilot
88
cool room facility at the Flanders Centre of Postharvest Technology (Leuven, Belgium). The
89
room has a total volume of 48 m3 (Fig. 1). It is equipped with two axial flow fans with diameter
90
of 40 cm and capacity of 2140 m3 h−1 each. The control system of the room was equipped with
91
Pt100 and RH meters (Siemens Building Technologies Inc., Illinois, USA) with accuracy of ± 0.5
92
°C and ± 5%, for measuring temperature and humidity, respectively.
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The experiment was designed to collect spatio-temporal 1-MCP concentration data at several
94
room loadings. For this, the room was filled with 400 or 800 or 1200 kg Jonagold apple fruit as
95
shown in Fig. 1a, b and c, respectively. Fruit were put in high-density polyethylene (HDPE)
96
plastic bins (Euro Pool System International B.V., Rijswijk, The Netherlands) with dimensions of
97
0.57× 0.37× 0.23 m each holding 15 kg fruit. In the loaded room, the 1-MCP treatment was
98
applied by placing a proprietary 1-MCP generation system (SmartFresh TechnologyTM) at a
99
target dose of 0.625 µL/L. After starting the generator the room was closed for 24 h at 1 °C. .
100
The applies for 24h or 24 h.
101
Gas samples were taken from five different positions in the room: (1) located centrally between
102
the stack and the door, (2) located centrally between the stack and the back wall, (3) top of the
103
stack, (4) between fruit in one of the boxes in the bottom raw and, (5) between fruit in one of the
104
boxes in the top raw. At sampling points, ends of tubes of equal length (3.5m) were fixed. The
105
other end of each sampling tube extended outside of the cool room and attached to vacuumed
106
glass jars of size 1.8 L to draw gas samples. The dilution effect due to the sampling was
107
calibrated by drawing known concentrations of gas samples from separately prepared standards.
108
At specific times over a 24 h period (2 h, 4 h, 6 h, 8 h, 10 h, and 24 h) gas samples from the five
109
points were drawn simultaneously for analysis. The concentration of 1-MCP was measured by
110
capillary type gas chromatography (Compact GC, Interscience, Louvain-la-Neuve, Belgium). The
111
repeatability of the measurement was assessed using three independent and identical tests on the
112
400 kg loading case.
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113
2.3 Commercial cool store treatments
114
Efficacy of the 1-MCP treatment was assessed through fruit firmness measurements in a
115
commercial cool store (Fig. 2a). The experiments were designed to investigate the effect of the
116
bin (wooden bins vs. plastic bins of dimensions 124 × 124 × 74 cm) and dose reduction (0.6250
117
vs. 0.3125 µL L-1) in cool stores with 184 m3 volume. For each experiment, the room was filled
118
with 90 bins (each holding 380 kg fruit). In the loaded room, the 1-MCP treatment was applied
119
by placing a proprietary 1-MCP generation system (SmartFresh TechnologyTM) at a target dose of
120
0.625 or 0.3125 µL/L. After starting the generator the room was closed for 24h at 1 °C. The
121
initial state of fruit in each experiment was assessed by measuring the firmness of 50 randomly
122
selected fruit. For each configuration, 16 samples of 30 apples were prepared. 15 samples were
123
placed at 15 different positions in the cool store room and 1 sample was set aside as a control
124
with no 1-MCP treatment. Fig. 3 shows the placement of the 15 samples in the cool store. At the
125
end of the treatment, the 16 samples (15 treated and 1 untreated) were subjected to a shelf life of
126
14 days at 18 °C, after which the firmness was measured. Fruit firmness was taken as the
127
maximal force needed to penetrate the apple over a distance of 8 mm with a cylindrical self
128
cutting probe (diameter 11 mm) moving at constant speed of 8 mm/s using a TA-XT2 Texture
129
Analyser (Stable Micro Systems Ltd., Godalming, Surrey, UK). Firmness was measured at the
130
equator at two opposite positions of the fruit without removing the peel. The measurement data
131
was analyzed by a one-way ANOVA and a Tukey multiple comparison analysis using the
132
statistics toolbox of Matlab 7.6.0 (R2008a).
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2.4 Porous medium CFD model
134
2.4.1 Governing equations
135
We build on our previous work (Ambaw et al., 2013b) that described the governing equations,
136
accompanying assumptions and validation (using small scale experimental set up and a direct
137
CFD model) of the porous medium model. The present study extends the validation by using
138
pilot cool room experimental data and applies the porous medium model to investigate the
139
distribution of 1-MCP in commercial cool stores.
140
2.4.2 Model parameters and conditions
141
The porous medium modeling approach involves a volume-averaged version of the Reynolds
142
Averaged Navier-Stokes equations. This procedure transforms the porous media that consisted of
143
stacked apples and air gaps between apples into a continuous and homogeneous medium,
144
characterized by porous medium properties such as porosity, tortuosity and interface transfer
145
coefficients. Once this operator is applied, the small details of the porous channel geometry are
146
avoided in order to obtain average solutions of airflow, temperature and gas distribution.
147
Turbulence is modeled using the SST-k-ω approach, not only at the clear fluid region (outside the
148
porous domain) but also inside the porous medium. For wooden bins, the model integrates the
149
effect of the wooden materials as well. This was accomplished by using average material
150
properties based on volume proportion of wood-to-fruit. This approach was validated using
151
experimental measurements and an equivalent direct CFD model of the 1-MCP distribution in
152
500 L container loaded with 80 kg Jonagold apple fruit in four wooden boxes each holding 20 kg
153
fruit. Hence, the sorption capacity, effective diffusivity and adsorption rate constants of the
154
porous domain were computed from the corresponding parameter values of Jonagold apple fruit
155
and wooden bin (oak wood) using Eqn. (1):
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156
Pi , pm pi , fruit (1 wp ) pi ,wood wp
157
where Pi,pm is value of parameter i of the porous domain, pi,fruit is value of parameter i of
158
Jonagold apple fruit, pi,wood is value of parameter i of wooden material (oak wood) and wp is the
159
proportion of the porous domain which is wood in volume. The model parameters and their
160
corresponding values are given in Table 1.
161
2.4.3 Characterizing the flow resistance of the porous domain
162
The porous medium model incorporates flow resistance terms in the region of the model defined
163
as “porous” (the fruit stacked in boxes or bins). This was obtained from simulations made on a
164
DE-CFD discrete element-computational fluid dynamic (DE-CFD) model of the airflow across a
165
fruit loaded bin. The resistance coefficients corresponding to a stack in the small boxes used in
166
the pilot cool room were presented by Ambaw et al. (2013b). For the larger bins used in
167
commercial cool stores (Fig. 4), the corresponding resistance coefficients were determined in the
168
current study. For the wooden bin (Fig. 4a,b), a separate DE-CFD model was developed for a
169
geometric model of 1620 apples (assuming a spherical fruit shape) with a diameter randomly
170
distributed between 80 to 85 mm in a wooden bin of dimension 1.24 × 1.24 × 0.74 m (Fig. 5a).
171
The DE method was used to create the geometric model as outlined in previous studies (Ambaw
172
et al., 2013b; Delele et al., 2009; 2008). Assuming symmetry, only half of the stack was modeled
173
to reduce computational load (see Fig.5b). The model accounts for all geometric details of the
174
stacked spheres, the void space between them and the bin. The discretization of half of the
175
geometric model consists of 10,646,348 cells. Fig. 5c and d depict the contour of pressure
176
distribution of airflow along +x and +y directions, respectively. The calculated viscous and
177
inertial resistance coefficients are summarized in Table 2. The geometric detail of the plastic bin
178
is complex with numerous small vent holes (see Fig. 4c) that makes the mesh generation
(1)
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179
extremely difficult. To this end, for the plastic bin the coefficients were determined from
180
empirical correlations proposed by Reichelt (1972) with value of constants obtained from Delele
181
et al. (2008).
182
2.4.4 Boundary and initial conditions
183
The cool store room is assumed to be a completely isolated system with the door, walls, ceiling
184
and floor as its boundaries. Inside these boundaries we have the porous domain consisting of all
185
fruit loaded boxes, the cooling unit as fluid domain 1 and the remaining region which is the free
186
air stream is defined as fluid domain 2. Accordingly, fluid-fluid (between fluid domain 1 and
187
fluid domain 2) and fluid-porous (between the porous domain and fluid domain 2) interfaces
188
were explicitly defined and set to a conservative interface flux boundary for the momentum and
189
scalar conservation equations. The airflow inside the cooling unit was modeled by adding a
190
momentum source term in fluid domain 1. 1-MCP gas entering fluid domain 1 was described by
191
adding a source term at the boundary of the device (surface representing the outlet of the gas
192
generator). The source is defined by a stepwise decreasing 1-MCP flux term in such a way that
193
80%, 18% and 2% of the full dose enters the domain within the first, second and third hours,
194
respectively, and zero during the remaining period. The 1-MCP release profile from the generator
195
was approximated from a separate experiment by placing the generator in an empty container. In
196
all the models, internal room walls, external surfaces of the circulation fan and external surface of
197
the 1-MCP generator were set to no slip walls. The initial concentration of 1-MCP in every
198
domain in the model was equal to zero.
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199
2.4.5 Simulation setup
200
The domains of each model were discretized using hexahedral elements for the porous region and
201
tetrahedral hybrid mesh for the region outside the porous domain. For every model, the level of
202
grid independence was evaluated using a form of Richardson extrapolation (Franke et al., 2007;
203
Roache, 1994) whereby, the average discretization error in estimating the 1-MCP fluxes across
204
selected faces of the porous domain was maximally 2 %. The optimum grids consisted of 800,000
205
up to 4,000,000 computational cells for the different configurations in this study. Several time
206
step sizes (360, 72 and 36 s) were assessed. Based on the computational time required and
207
improved accuracy obtained by decreasing the time step, 36 s with 15 iterations was selected as
208
the optimum for all the computations.
209
The transport equations were numerically solved using the finite volume method in ANSYS-
210
CFX-13 (ANSYS CFX, 2010). The advection scheme of the numerical solution uses the ANSYS-
211
CFX high resolution method, which is a blend between central differencing and upwind
212
differencing locally. The transient scheme uses a second order backward Euler method. For each
213
porous medium model, a steady state calculation was performed to obtain a converged solution of
214
the airflow. The steady state solutions were used as initial value to the subsequent transient scalar
215
transport calculation. Under the selected optimum solver format, a single full simulation took 40
216
to 60 h on a 64-bit, Intel (R) Core (TM) 2 Quad CPU, 3 GHz, 8 Gb RAM, Windows 7 PC.
217
2.5 Simulation study
218
2.5.1 Cases
219
The porous medium CFD model was used to compute the 1-MCP distribution for the following
220
cool stores:
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221
The 48 m3 pilot scale room filled with different loadings of Jonagold apples in plastic
222
crates (400, 800 and 1200 kg) and a dose of 0.625 µL L-1. The dimensions of the pilot
223
room are 2.8 m × 4.25 m × 3.6 m (Fig. 1).
224
The commercial cool store (CS1, Veiling Borgloon, Belgium) of 184 m3 volume filled
225
with wooden or plastic bins and for different doses (0.625 and 0.3125 µL L-1) where the
226
firmness measurements were performed. The dimensions of the cool store were 9.45 m
227
depth, 3.37 m width and 5.79 m height. The room was loaded with a total of 90 bins
228
(wooden or plastic) each holding 380 kg Jonagold apple fruits.
229
A 434 m3 commercial cool store (CS2) at Proefcentrum Fruitteelt v.z.w. (Sint-Truiden,
230
Belgium) with 9.73 m depth, 6.03 m width and 7.4 m height, loaded with 230 bins
231
(wooden or plastic) each holding 380 kg Jonagold apple fruit (Fig. 2b). Different airflow
232
rates were compared for this room. Also the time profiles of the 1-MCP distribution for
233
different 1-MCP doses were compared.
234
2.5.2 Analysis procedure
235
The distribution of 1-MCP as free in air, bound in fruit and unbound in fruit was calculated for
236
the three pilot cool room experiments using their corresponding porous medium model. The
237
calculated time profiles of the free in air concentrations were then compared with experimental
238
data to validate the porous medium model.
239
Subsequently, the validated porous medium model was used to study the effect of room
240
dimension on the uniformity of gas distribution. For this, CS1 and CS2 with depth-to-width ratios
241
of 2.8 and 1.6, respectively, were compared. Depth is measured in the same direction as airflow
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
242
from the evaporator. In this analysis, wooden bins were assumed in both cool stores. The air
243
exchange rate in both rooms was 50 h-1.
244
Then the relationship between airflow and gas distribution was further assessed using the Design
245
Exploration tool of ANSYS Workbench. The examination was done by varying the airflow
246
velocity (from the cooling unit) from 0.01 to 10 m s-1, equivalent to an air exchange rate of 0.2 to
247
160 h-1 in CS2 with wooden bins. Uniformity of gas distribution and degree of saturation at 2h
248
were used as output of interest. Uniformity was calculated as coefficient of variance (CV) of
249
local unbound 1-MCP concentration to the volume average in the porous domain. Hence, higher
250
CV means lower uniformity. Degree of saturation was calculated as the ratio of average bound 1-
251
MCP at 2h to the maximum sorption capacity of Jonagold apple fruit at equilibrium, which is
252
5×10-6 kg m-3(Ambaw et al. 2011).
253
In order to study the relationship between dose, bin material of construction and treatment
254
duration, simulations were performed for 1-MCP dose of: 0.3, 0.5, 0.6 and 1 µL L-1 in CS1 with
255
wooden or plastic bins.
256
2.6 Dose calculation
257
To investigate dose requirement per room filling density, we performed a 1-MCP balance
258
calculation. Filling density is defined as the amount of fruit per unit volume of the cool store. The
259
1-MCP balance calculation is based on the amount of 1-MCP binding sites. For a room with
260
plastic bins, the fruit is the only sorbent since a sorption of 1-MCP by plastic materials is
261
negligible (Ambaw et al., 2011). On the other hand, in cool store room with wooden bins, both
262
fruit and wood materials contribute in the sorption. The 1-MCP dose calculation is given by Eqn.
263
(2).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
264
Dose Cmax , fruit Cmax ,wood wp room fruit
265
where Cmax,fruit and Cmax,wood are the sorption capacities (kg m-3) of Jonagold apple fruit and Oak
266
wood respectively as given in Table 1. wp is the proportion of wood which is calculated from the
267
wood to fruit volume ratio (= 1/(1+8)). ρroom is the room filling-density (kg m-3) and ρfruit the
268
density of Jonagold apple fruit (= 830 kg m-3).
269
3 Results and discussion
270 271
3.1 1-MCP distribution in pilot cool room with different loadings
272
3.1.1 Validation of the 1-MCP concentration
273
The performance of the porous medium model in predicting the airflow was assessed by velocity
274
measurements in the same room as previously reported (Delele et al., 2009). Hence, only, the 1-
275
MCP distribution is validated in the present study.
276
The measured and calculated time history of the 1-MCP concentrations in the pilot cool room are
277
shown in Fig. 6a, b and c for the 400, 800 and 1200 kg loading, respectively. The model
278
simulations of the 1-MCP concentration in the air are spatially uniform at any specific time and
279
shown as a single curve for each model in Fig. 6. This is confirmed by the measurements. The
280
observed spatial variations were random and within measurement errors (Fig. 6a). The 1-MCP
281
distributions in air of the three cases are well reproduced by the model. Hence, the porous
282
medium model is applicable to model airflow and 1-MCP distributions in such cool stores
283
adequately.
(2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
284
3.1.2 1-MCP adsorption by fruit for different loadings
285
Simulated contours of unbound and bound 1-MCP profiles of the 1200 kg loading are shown in
286
Fig. 7. The contours of the 400 and 800 kg loading cases follow the same trend and are not shown
287
here. As can be seen from the contours, the distribution of 1-MCP as free in air, unbound and
288
bound in the porous domain are uniform at every time during the treatment. Complete treatment
289
was attained well before the prescribed 24 h (within 6h). The amount of 1-MCP that remained in
290
the treatment atmosphere after completions of the treatment was equal to 0.59, 0.57 and 0.56 ppm
291
for the 400, 800 and 1200kg loading, respectively. The model predictions of remaining 1-MCPs
292
agree well with the corresponding experimental values. The rather small depletion of the active
293
substance from the treatment atmosphere (target dose of 0.625 ppm) is plausible. This is typical
294
of a room with low fruit mass per unit volume of storage (low filling density). The porous
295
medium model captured the apparent small difference between the three cases.
296
3.2 1-MCP distribution in a commercial cool store
297
3.2.1 Efficacy assessed by fruit firmness
298
Fig. 8 shows the firmness results corresponding to the effect of dose and bin material of
299
construction. Fruit treated with dose of 0.625 µL L-1 in CS1 show uniform fruit firmness over the
300
entire room regardless of the type of bin material and position of fruit in the room. After the 14
301
day shelf life at 18 °C, firmness of the treated apples were not different from the firmness at the
302
start of the treatment, while the non-treated apples had decreased significantly in firmness.
303
Similarly, at the 0.3125 µL L-1 dose (half dose), fruit firmness was uniform irrespective of
304
position in the room (Fig. 8). However, the firmness of treated fruit was not different from the
305
untreated fruit. Firmness of treated and untreated apples significantly decreased at the half dose.
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3.2.2 1-MCP profiles for different bin materials and doses
307
Simulations were performed for 1-MCP doses of 0.3, 0.5, 0.6 and 1 µL L-1 in CS1 with wooden
308
or plastic bins, for which experimental efficacy data is available for comparison. Fig. 9a and b
309
show the calculated time profile of the average bound and unbound 1-MCP concentrations,
310
respectively. For 1 and 0.6 µL L-1 dose, complete treatment of fruit was attained within 24 h,
311
irrespective of the used bin material of construction. At these two doses, in the room atmosphere,
312
0.043 and 0.052 ppm 1-MCP remained at the end of the treatment for wooden bins and plastic
313
bins, respectively. At a dose of 0.5 µL L-1 the active substance in the room is nearly depleted at
314
24 h for both bin materials and a complete treatment is only attained if plastic bins are used and
315
the treatment duration is increased to 30 h. The complete treatment at a dose of 0.625 µL L-1 and
316
the complete lack of efficacy at the 0.3125 µL L-1 dose observed in the firmness experimental
317
data agree with the model simulation.
318
However, this assessment is specific to the room size and amount of fruit in the room (room
319
filling density) considered in the analysis. Filling density of CS1 in the calculation was 185 kg m-
320
3
321
amount of 1-MCP for complete treatment should increase (see discussion below).
322
3.2.3 Effect of room dimensions on 1-MCP distribution
323
The porous medium model of the gas distribution in CS1 and CS2 with wooden bins was used to
324
assessthe effect of room dimensions. For both rooms an air exchange rate of 50 h-1 was used.
325
Simulated contours of unbound and bound 1-MCP are shown in Fig. 10a and b for CS1 and CS2
326
respectively. The 1-MCP distribution in the free air was perfectly uniform in both rooms (see the
327
portion of contours on the section planes outside the porous domain in Fig. 10 top row). The
. Larger commercial cool stores usually operate at higher filling density. Accordingly, the
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328
unbound 1-MCP in the porous domain was also quite uniform. It was within 1% variation in both
329
room and was only captured by the narrow color scale (see Fig. 10 top row). The bound 1-MCP
330
had a 2 to 10 % variation for CS1 and CS2 (Fig. 10 bottom row). The higher non uniformity in
331
CS2 is, in part, due to the lower depth-to-width ratio of this room. Notice also that in CS2 the
332
variation was higher along x axis. The contours are plotted at 2h after start of the treatment, when
333
non-uniformity, if any, would be in its maximum noticeable range. To this end, one can safely
334
assume uniform gas distribution irrespective of shape as far as a room operates at sufficient air
335
circulation. However, under insufficient air circulation one can expect a more pronounced non-
336
uniformity in a room with low depth-to-width ratio. A more comprehensive assessment on the
337
effect of air exchange rate is presented below.
338
3.2.4 Effect of air circulation rate
339
Fig. 11 shows the response curves of the simulation as a result of using different air circulation
340
rates in CS2 loaded with fruits in wooden bins. The increase in uniformity is very rapid up to,
341
say, 20 h-1. Afterwards increasing the air exchange rate has negligible incremental value on
342
uniformity of gas distribution. As shown in the zoomed view of Fig. 11a, maximum uniformity is
343
reached at 80 h-1. Interestingly, a higher air exchange rate may even lead to a slightly reduced
344
uniformity. At higher air exchange rate fast moving air in front of the cooling unit creates a low
345
pressure region in that vicinity. This causes higher recirculation and due to this less air returned
346
to the air circulation unit and across the 1-MCP generator. The rate of 1-MCP adsorption also
347
rapidly increases up to 20 h-1. Thereafter, the rate of adsorption increases gradually but only
348
slightly (Fig. 11b).
349
Fig. 12 depicts air stream diagrams in CS2 at 0.2 h-1 and 20-1 air exchange rates with wooden bins
350
to contrast a worst case and marginally satisfactory scenarios. The very low air penetration into
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351
the stack of the 0.2 h-1 is apparent. The airflow inside the porous domain at this air exchange rate
352
is negligibly small such that the gas transport is mainly by diffusion. This scenario nearly
353
represents a room without air circulation. At 20 h-1, the air stream from the circulation fan clearly
354
reached the back wall and a major part of the returning air passes through the stack. The
355
corresponding contours of bound and unbound 1-MCP concentrations are shown in Fig. 13.
356
Notice that at the lower air exchange rate, there are stacks totally untreated even after 18 h. On
357
the other hand the rather uniform distribution of both unbound and bound 1-MCP concentrations
358
of the 20 h-1 is noticeable.
359
The recommended air exchange rate of commercial cool stores, under room cooling condition, is
360
about 77 m3 h-1 per bin (Hellickson, 2005) or 170 m3 h-1 ton-1 (Guillou, 1960). This amounts to an
361
air exchange rate of 40 h-1 for the particular loading conditions of CS2. Our model demonstrated
362
that the 1-MCP distribution is uniform even at 20 h-1, which is half of the normal cool store room
363
operating condition. To this end, one can expect essentially uniform 1-MCP distribution in
364
commercial cool store room applications. The uniform distribution of fruit firmness data in both
365
tests go along the uniform 1-MCP distribution simulated by the porous medium model. Also, it is
366
noteworthy that the tests were performed in CS1, at filling density of 183 kg m-3 for which the
367
critical doses are respectively, 0.5 and 0.4 µL L-1 for wooden and plastic bins (Fig. 10).
368
Accordingly, the full efficacy at the higher dose and lack of efficacy at the lower dose follow the
369
critical dose calculation.
370
3.2.5 Effect of room filling density
371
The calculated doses per filling densities from Eqn. (1) are shown in Fig. 14. A critical dose in
372
the chart corresponds to the amount of 1-MCP required to completely saturate binding sites in all
373
fruit and wooden bins in the cool store. Dose ranges from 0.30 to 0.91 µL L-1 as filling density
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increases from 100 to 300 kg m-3 for rooms with wooden bins. For room with plastic bins, the
375
dose ranges from 0.24 to 0.72 µL L-1 for the same range of filling density. It is interesting to point
376
out that the prescribed doses in commercial 1-MCP application differ from country to country. In
377
the USA and Canada, the labeled treatment dosage for apple is 1.0 and 0.6 μL L-1, respectively
378
(Pest Management Regulatory Agency, Health Canada, 2004). In Europe, the minimum use rate
379
is 0.545, and 1 μL L−1 is prescribed as critical use rate (European Food Safety Authority, 2005).
380
The reason behind the inconsistency of the dose prescription is not clearly reported. We suspect
381
the variation has to do with the way the efficacy trials were done during dose prescriptions which
382
in turn is partly affected by the room filling density. Hence, we propose that filling density based
383
dose prescription could be used rather than the fixed value prescription that is commonly used.
384
4 Conclusions
385
A previously developed and validated (in small scale) porous medium model was further
386
validated in this study using pilot cool room experimental data. Here again, the model agreed
387
well with measurements, which strengthens our confidence to use it for design studies of
388
postharvest treatments. Subsequently, porous medium models of two different sized large cool
389
store rooms were developed and used for numerical assessment of effects of room shape, air
390
circulation rate, material of construction of bins and dose on the 1-MCP treatment. The study
391
showed that 1-MCP distribution in commercial cool stores under room cooling conditions is
392
sufficiently uniform irrespective of the room shape. Generally, the air circulation in a commercial
393
cool store is designed to meet the more stringent requirement of temperature and moisture
394
uniformity. We have shown that under these working conditions 1-MCP uniformity is assured for
395
different room designs and use of bin materials. The study presented time-concentration profiles
396
of bound and unbound 1-MCP concentrations in room with wooden bin or plastic bins at range of
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
397
initial 1-MCP doses. Wooden bins account for up to 25% extra depletion compared to plastic
398
bins. Dose prescriptions based on room filling-density rather than the currently practiced solitary
399
prescription is suggested in this study.
400
5 Acknowledgments
401
The financial support of the Institute for the Promotion of Innovation by Science and Technology
402
in Flanders (project IWT 060720) is kindly appreciated. These projects received co-funding by
403
the Belgian Association of Fruit and Vegetable Co-operatives (VBT). SmartFreshTM was kindly
404
supplied for the experimental trials by AgroFresh Inc. Prof. Opara's contributions were supported
405
by the South African Research Chairs Initiative of the Department of Science and Technology
406
and National Research Foundation, and the South African Postharvest Innovation Programme
407
(PHI).
408
6 References
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Ambaw A, Delele M, Defraeye T, Ho Q, Opara LU, and Nicolai BM (2013a) The use of CFD to characterize and design post-harvest storage facilities: Past, present and future. Computers and Electronics in Agriculture, 93, 184-194.
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Ambaw A, Verboven P, Delele MA, Defraey T, Tijskens E, Schenk A, Opara UL, Nicolai BN (2013b) Porous medium modeling and parameter sensitivity analysis of 1-MCP distribution in boxes with apple fruit. Journal of Food Engineering, under review.
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Ambaw A, Verboven P, Delele MA, Defraey T, Tijskens E, Schenk A, Nicolai BN (2012) CFD modeling of the 3D spatial and temporal distribution of 1-methylcyclopropene in fruit storage container. Food and Bioprocess Technology, DOI: 10.1007/s11947-012-0913-7 Online First™
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Ambaw A, Beaudry R, Bulens I, Delele MA, Ho QT, Schenk A, Nicolai BN, Verboven P (2011) Modeling the diffusion-adsorption kinetics of 1-methylcyclopropene (1-MCP) in apple fruit and non- target materials in storage rooms. Journal of Food Engineering, 102, 257-265.
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Blankenship, S.M., Dole, J.M. (2003) 1-Methylcyclopropene: a review. Postharvest Biology and Technology, 28, 1–25.
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Defraeye T, Lambrecht R, Tsige AA, Delele MA, Opara UL, Cronjé P, Verboven P, Nicolai BM (2013) Forced-convective cooling of citrus fruit: package design. Journal of Food Engineering , doi: http://dx.doi.org/10.1016/j.jfoodeng.2013.03.026
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Delele MA, Tijskens E, Atalay YT, Ho QT, Ramon H, Nicolai BM, Verboven P (2008) Combined Discrete Element and CFD Modelling of airflow through random stacking of horticultural products in vented boxes. Journal of Food Engineering, 89 (1), 33 – 41.
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Ferrua MJ, Singh RP (2009) Modeling the forced-air cooling process of fresh strawberry packages, Part I: Numerical model. International Journal of Refrigeration, 32, 335-348.
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Guillou R (1960) Coolers for fruits and vegetables. Calif. Agric. Exp. Stn. Bul. No. 773.
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Hoang M, Verboven P, Baelmans M, Nicolai BM (2003) A continuum model for airflow, heat and mass transfer in bulk of chicory roots. Transactions of the ASAE, 46, 1603-1611.
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Vallejo F & Beaudry R (2006). Depletion of 1-MCP by ‘non-target’ materials from fruit storage facilities. Postharvest Biology and Technology, 40, 177–182.
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Verboven P, Flick D, Nicolai BM, Alvarez G (2006). Modelling transport phenomena in refrigerated food bulks, packages and stacks: basics and advances. International journal of refrigeration, 29 (6), 985-997.
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Zou Q, Opara LU, McKibbin R (2006a). A CFD modeling system for airflow and heat transfer in ventilated packaging for fresh foods I: Initial analysis and development of mathematical models. Journal of Food Engineering 77 (4), 1037-1047.
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Zou Q, Opara LU, McKibbin R (2006b). A CFD modeling system for airflow and heat transfer in ventilated packaging for fresh foods II: Computational solution, software development and model testing. Journal of Food Engineering 77 (4), 1048-1058.
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FIGURE CAPTIONS
485 486
Fig. 1: Schematic diagram of the setup of the validation experiments in the pilot cool room. (a) 400 kg loading (b), 800 kg loading (c) 1200 kg loading.
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Fig. 2: Schematic diagrams of the commercial cool stores. (a) CS1 (Veiling Borgloon Belgium) of 184m3 volume with 90 plastic or wooden bins (b) CS2 (Proefcentrum Fruitteelt v.z.w., Sint Truiden, Belgium) of 434 m3 volume filled with 232 plastic or wooden bins. Bins hold 380 kg Jonagold apple fruit each.
491 492 493 494 495
Fig. 3: Positions of the 15 samples in CS1 during the efficacy experiment. (a) The three vertical planes along which samples were placed, (b) position of samples S1 to S5, (c) position of samples S6 to S10 and (d) samples S11 to S15. Each sample contains 30 fruits in plastic net bag and placed inside the fruit loaded bins (left and right raw) or suspended in position in the free space between the left and right raw.
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Fig. 4: The bins used to hold fruit in large cool store rooms. Sides of loaded wooden bin (a), inside view of empty wooden bin (b) and plastic bin (c).
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Fig. 5: The implementation of the DE-CFD method to obtain the viscous and inertial loss coefficients of the porous domain. Wooden box loaded with 1620 spheres with diameters ranging randomly from 80 to 85 mm (a), half of the stack used in the modeling assuming symmetry, velocity contours are shown on the symmetry plane (b), simulated contour of pressure distribution while air flows laterally (c) and vertically (d). The pressure and velocity contours shown correspond to a superficial velocity of 4 m s-1.
504 505 506
Fig. 6: Measured and calculated time-concentration profile of 1-MCP in air in the pilot cool room with 400kg fruit (a), 800 kg fruit (b) and 1200 kg fruit (c). Tests were undertaken at 1°C and 1-MCP dose of 0.625 ppm. Vertical lines on data points of (a), represent standard errors.
507 508 509
Fig. 7: Contour of unbound (top row) and bound (bottom row) of 1-MCP concentrations on vertical bisection plane through columns second from the left wall of the room (see Fig. 1) in the pilot cool room loaded with 1200 kg Jonagold apple fruit at 1°C and 1-MCP dose of 0.625 ppm.
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Fig. 8: Result of efficacy measurement using fruit firmness measurement. The test took place in CS1 filled with 34200 kg Jonagold apple fruit in 90 bins.
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Fig. 9: Calculated time profile of volume averaged bound (a) and unbound (b) 1-MCP concentrations in with plastic bins (full curves) and wooden bins (broken curves).
514 515
Fig. 10: Simulated contour of unbound (top row) and bound (bottom row) 1-MCP at 2h in CS1 (a) and CS2 (b). Simulations correspond to treatment at 1°C and 1-MCP dose of 0.625 ppm.
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Fig. 11: Measure of uniformity of unbound gas distribution (a) and degree of saturation (b) vs. air exchange rates at 2h in CS2 at 1°C and 1-MCP dose of 0.625 ppm. The internal chart in (a) shows the lower end of the coefficient of variance (CV).
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Fig. 12: Simulated stream lines of the airflow in CS2 filled with 88160 kg Jonagold apple fruit in 232 wooden bins each holding 380 kg fruit at air exchange rates of 0.2 h-1 (a) and 20 h-1 (b).
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Fig. 13: Simulated contour of unbound and bound 1-MCP concentrations in CS2 at 1°C and 1MCP dose of 0.625 ppm. The contours are shown on a vertical section plane bisecting the column of stacks second from the left wall of room.
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Fig. 14: Calculated critical 1-MCP dose per filling density of cool store room with wooden bins (dashed curve) and plastic bins (full curve).
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Table Click here to download Table: TABLES.docx
Table 1: Model parameters and their values Parameter
Value
Adsorption rate constant per binding site in fruit, ka,f [m3 mol-1s-1]
3.62
Adsorption rate constant per binding site in wood, ka,w [m3 mol-1 s-1]
6.50
Dry air density, ρ [kg m-3]
1.29
Kinematic Diffusivity of 1-MCP gas in air Da [m2 s-1]
8.67 × 10-6
Effective Diffusion coefficient of 1-MCP in fruit, Ds,f [m2 s-1]
2.41 × 10-8
Effective diffusion coefficient 1-MCP in wood, Ds,w [m2 s-1]
2.00 × 10-8
Molecular weight of 1-MCP [kg/mol]
0.054
Turbulence Schmidt number, Sct
0.9
1-MCP binding capacity of apple fruit, Cmax,fruit [ kg m-3 ]
4.87 ×10-6
1-MCP binding capacity of wooden bin, Cmax,wood [ kg m-3 ]
1.2×10-5
Table 2: Viscous and inertial coefficients of the airflow resistance model of the porous medium, obtained from DE-CFD simulation for fruit in wooden bin and from Reichlet (1972) correlation for fruit in plastic bin. Fruit in wooden bin
Fruit in plastic bin
Inertial loss
viscous loss
coefficient
coefficient
Inertial loss coefficient
[kg m-3 s-1 ]
[kg m-4]
[kg m-3 s-1 ]
[kg m-4]
X or Z
68
276
2.51
163
Y
2
269
2.35
160
viscous loss coefficient Direction
Abstract
ABSTRACT 1-methylcyclopropene (1-MCP) is a synthetic plant growth regulator used commercially to delay ripening of fruits. 1-MCP (SmartFreshTM) is applied in gaseous form (as a fumigant) in the cool store room. This paper uses a porous medium CFD model to numerically analyze the distribution of 1-MCP in cool store rooms for apple fruit. The effects of air circulation, room shape and bin material were investigated. Dose calculation based on filling density was explored. The 1-MCP distribution in commercial cool stores, under room cooling condition, was uniform irrespective of room shape. Rooms filled with fruit in wooden bins deplete 25% more of the active substance than rooms filled with fruit in plastic bins. The calculated dose increases linearly with the amount of fruit in the cool store (filling density). Hence, this study suggests that filling density based dose prescription is feasible. Keywords: Fruit-storage, Computational fluid dynamics, Diffusion-adsorption, Porous-medium, Simulation
