AU J.T. 9(3): 187-192 (Jan. 2006)
Temperature Changes in Bulk Stored Maize
B. A. Alabadan
Department of Agricultural Engineering,
Federal University of Technology, Minna. Nigeria
Abstract
A two-dimensional, transient heat conduction model based on finite-difference
method was used to predict the influence of bin diameter, simulation schemes (explicit,
implicit and Crank-Nicolson) and ambient temperature variations within maize bulk
stored in Minna, Nigeria. Temperatures within the silo were predicted at center,
interior, wall, and grain surfaces. Large diameter bins (5.6m and 7.0m) maintained
warmer center temperatures than small diameter bins (1.4m and 2.8m). Predicted
results from explicit, implicit, and Crank-Nicholson schemes were also not significantly
different using F-test at 5% level of significance. The ambient temperatures during the
dry season were higher than the wet season. The grain surface temperatures were
about 1-80C higher than the ambient temperatures.
Keywords: Transient heat conduction, simulation schemes, ambient temperature.
Nigeria by simulating the grain temperatures
using the two-dimensional heat transfer model
of Alabadan (2005) solved by the finite
difference method. The parameters considered
in this study were:
a. bin diameter (1.4, 2.8, 4.2, 5.6 and 7.0 m)
b. simulation schemes (explicit, implicit and
Crank-Nicolson)
c. ambient temperature.
Introduction
Temperature is one of the major factors
affecting the quality of grain during storage
and also the key factor in the regulation of pest
insect populations in stored grain. In the
absence of metabolic heat release by heavy
infestations of insects, weather influences are
the main cause of temperature change in bulk
grain. The effects are transferred through the
bulk by conduction and, where temperature
gradients occur, by convection (Muir 1973).
FAO (1996) reported that metallic silos
promote
moisture
condensation
and
development of hot spots under the humid
climatic conditions of Nigeria. Mijinyawa,
(1989) and Alabadan (2002) concluded that
wooden silos are efficient in reducing the
moisture condensation and development of hot
spots that arise during periods of elevated
temperatures.
Seasonal temperature changes in grain
bulks have been measured and modeled in twodimensional heat transfer to predict temperatures in stored grains (Muir et al. 1980;
Metzger and Muir 1983; Abe and Basunia
1996; and Alabadan 2005).
The objectives of this work were to
predict the effects of various parameters on the
temperatures of stored maize bulk in Minna,
The Simulation Model
The general differential equation of heat
flow in two-dimensional cylindrical coordinate
systems, adapted for the hexagonal bin, was
used for the heat transfer model to simulate
temperatures in the wooden maize bin as
detailed by Alabadan (2005).
The interior, center, and exterior elements
equations were solved together simultaneously
using an iteration method by successive
increments in time (t) by ∆t. The model
assumed that the heat transfer in a grain bulk is
only due to conduction among other
assumptions.
Description of the Silo
The wooden silo was hexagonal in shape,
1.1m in height and 1.4m in diameter. Plywood
187
AU J.T. 9(3): 187-192 (Jan. 2006)
The predicted minimum temperatures at
the center of 1.4, 2.78, 4.2, 5.6 and 7.0m
diameter wooden bins in this study were 27.5,
28.0, 25.4, 28.0 and 29.80C respectively. There
were marked differences in temperatures of this
study when compared with the other
researchers such as Yaciuk et al. (1975) and
Jayas et al. 1994 because temperatures are
higher in the tropical climate prevalent in Nigeria
than in Canada’s temperate climate.
The larger diameter bins maintained
warmer or cooler temperatures at the center
than smaller diameter bins in agreement with
Yaciuk et al. (1975) and Jayas et al. 1994.
Though different bin-wall materials were not
considered, it was observed that plywood with
low thermal conductivity had a great influence
on the pattern of temperatures at the center of
the wooden bin.
The pattern of temperatures at equidistant
from the center and internal wall did not differ
from the pattern at the centers. However,
smaller bins followed the pattern of the
ambient temperatures more closely than the
larger bins. Also, there were close agreements
between the predicted temperatures at the
internal wall of the storage bins since F-test
showed no significant differences. Generally,
there were wide variations in temperatures
within the smaller bins than in the larger bins.
of exterior grade, 3 plies (9 mm) thick, 1440
mm by 2,880 mm in area, and bonded together
using phenol-formaldehyde resin adhesive,e
was used for the walls, roof and floor while the
frames were made from 2 by 2-solid Iroko
(Melicia excelsa) timber. The silo has three
openings: one at the top for loading, and two at
the sides to serve as door and discharge chute
respectively.
Results and Discussion
Study Area
The measured ambient temperature of
the silo environment during the wet season (030 days) was as high as 340C, dropped to about
280C at the beginning of August and rose to as
high as 380C between 150-270 days during the
dry season of November to March. The
relative humidity ranged between 30-100%
(dry to wet season) throughout the storage period.
Effect of Bin Diameters
Figs. 1– 3 show the effect of bin diameters
(1.4, 2.78, 4.2, 5.6, 7.0m) on the temperature
patterns at the center, interior, and wall of the
silos. The temperatures at the center of the
1.4m diameter wooden bin followed closely the
temperatures at the center of the 2.78m
diameter wooden bin. During the 0–30 daystorage period, the larger bins had higher
temperatures than the smaller bins with a
difference of about 0.80C. This pattern changed
at about the 250-day storage period between
January and February when smaller bins
temperatures were higher than the larger bins.
The minimum temperature at the center
of the smallest wooden bin was reached after
75 days of storage while that of the 2.78m
diameter wooden bin was reached after 150
days of storage. This was similar to the report
of Jayas et al. (1994). Larger bins reached their
minimum temperatures after 240 days of
storage. This could be attributed to the fact
that the bins could be considered as small bins
as well as shallow silos based on the fact that
the ratio of diameter to height of bin was
greater than unity.
Effect of Different Simulation Schemes on
the Predicted Temperatures
Figs. 4–6 show the predicted temperatures
at the center, interior and at the internal wall of
the different silos based on Crank-Nicolson,
Explicit and Implicit schemes. There were no
significant differences in the predicted results
based on these schemes.
Effect of Ambient Temperatures on the
Grain Surface Temperatures
In Fig. 7, the grain surface temperatures
were about 1–50C higher than the ambient
temperatures during the wet period between
July to November (150 days of storage). The
dry period from December to March recorded a
marked difference of up to 80C probably due to
accumulated heat during the days and the long
hours of sunshine. This phenomenon was a
188
AU J.T. 9(3): 187-192 (Jan. 2006)
Alabadan, B.A. 2005. Transient temperature
gradients in stored maize bulk. AU J.T. 8:
145-53.
AERLS. 1987. Maize Production in the
Northern States of Nigeria. Extension
Bulletin No. 11, Agricultural Extension and
Research Liaison Services. Ahmadu Bello
University, Samaru-Zaria, Nigeria.
Basunia, M.A.; Abe, T.; and Bala, B.K. 1996.
Application of finite element method for the
simulation of temperature distribution
during storage of rough rice in cylindrical
bin. AMA 27 2): 33-40.
FAO.1996. Technical Cooperation Programme:
Assistance to the Strategic Grain Reserve
Scheme in Nigeria. Terminal Statement
prepared for the Government of Nigeria by
the Food and Agriculture Organization of
the United Nations, Rome, Italy, pp 1-9.
Jayas, D.S.; Alagusundaram, K.; Shunmugam,
G.; Muir, W.E.; and White, N.D.G. 1994.
Simulated temperatures of stored grain bulks.
Canadian Agric. Engin. 36: 239-45.
Metzger, J.F.; and Muir, W.E. 1983. Computer
model of two-dimensional conduction and
forced convection in stored grain. Canadian
Agric. Engin. 25: 119-25.
Muir, W.E. 1973. Temperature and Moisture in
Grain Storage. In: Grain Storage: Part of a
System., R.N. Sinha and W.E. Muir, Eds.
Avi Publ., Westport, Connecticut, USA.
Muir, W.E.; Fraser, B.M.; and Sinha, R.N.
1980. A simulation Model of TwoDimensional Heat Transfer in Controlled
Atmosphere Grains Bins. In:
Controlled
Atmosphere Storage of Grains. J. Shejbel,
Ed. Elsevier Scientific Publ., Amsterdam,
the Netherlands, pp. 385-97.
Mijinyawa, Y. 1989. The Use of Wood
Products in the Design and Construction of
a Grain Silo for the Humid Tropics. Ph.D
Thesis,
Department
of
Agricultural
Engineering, University of Ibadan, Ibadan,
Nigeria.
Yaciuk, G.; Muir, W.E.; and Sinha, R.N.. 1975.
A simulation model of temperatures in
stored grain. J. Agric. Engin. Res. 20: 254-8.
potential source of danger to the grain bulk in
storage. The standard error of estimate was
1.670C with F-test value of 1.504E-06.
Conclusion
Based on the results of this study, the
following conclusions are drawn:
The temperatures decreased from the
walls towards the center and from the top of
grain bulk towards the floor of the wooden silo.
The temperatures within the wooden silo were
generally lower than the ambient temperatures
that show a great potential of the wood product
in reducing the effect of the ambient
temperatures. The temperatures at the grain
headspace (surface) were higher than the
ambient temperatures and, thus, most
conducive for insect pests and also extensive
heat.
The predicted temperatures of the 1.4,
2.78, 4.2, 5.6 and 7.0m diameter silos were not
significantly different from each other at 5%
and 1% levels of significance on statistical
analysis using F-test. Larger diameter bins
maintained warmer grain temperatures during
the wet season and cooler grain temperatures
during the dry season than the smaller diameter
bins.
The results of the three schemes used in
the simulation were not significantly different
at 5% level of significance.
References
Abe, T. and M.A. Basunia. 1996. Simulation of
temperature and moisture changes during
storage of rough rice in cylindrical bins
owing to weather variability. J.Agric. Engin.
Res. 65: 223-33.
Alabadan, B.A. 2002. Modelling the Performance of a Hexagonal Wooden Silo During
Storage of Maize (Zea mays). Ph.D. Thesis
Department of Agricultural Engineering,
University of Ibadan, Ibadan, Nigeria.
189
AU J.T. 9(3): 187-192 (Jan. 2006)
40
Am bie nt
1.4m Dia m e te r
2.8m Dia m e te r
4.2m Dia m e te r
5.6m Dia m e te r
7.0m Dia m e te r
35
Temperature (oC)
30
25
20
15
0
30
60
90
120
150
S torage P eriod
Fig. 1:
180
210
240
270
(Day s )
Am bient and P redicted Temperatures at the Geometric C entre of the W ooden
S ilos.
40
Am bie nt
1.4m Dia m e te r
2.8m Dia m e te r
4.2m Dia m e te r
35
5.6m Dia m e te r
7.0m Dia m e te r
Temperature (oC)
30
25
20
15
0
30
60
90
120
150
S tora ge P e riod
Fig. 2:
180
210
240
(Da ys)
Ambient and Predicted Temperatures at E quidistant from the C entre and internal
Wall of the Wooden Silos.
190
270
AU J.T. 9(3): 187-192 (Jan. 2006)
40
Ambient
1.4m Diameter
2.8m Diameter
4.2m Diameter
5.6m Diameter
7.0m Dameter
Temperature (oC)
35
30
25
20
15
0
30
60
90
120
150
Storage Period
Fig. 3:
180
210
240
270
(Days)
Am bient and Predicted Tem peratures at the internal Wall of the Wooden
Silos.
40
Temperature (oC)
35
30
25
Am bient
Beta=0.5 (Crank Nicolson)
Beta=0 (Explicit)
Beta=1 (Im plicit)
20
15
0
30
60
90
120
150
Storage Period
180
210
240
(Days)
Fig. 4: Am bient and Predicted Tem peratures at the Silo Centre (Radius 0.0m and
Depth 0.3m ) Based on the Crank Nicolson, Explicit and Im plicit Schem es.
191
270
AU J.T. 9(3): 187-192 (Jan. 2006)
40
Temperature (oC)
35
30
25
Am bient
Beta=0.5 (Crank Nicolson)
Beta=0 (Explicit)
Beta=1 (Im plicit)
20
15
0
30
60
90
120
150
Storage Period
180
210
240
270
(Days)
Fig. 5: Am bient and Predicted Tem peratures of the silo (Radius 0.35m and
Depth 0.3m ) Based on the Crank Nicolson, Explicit and Im plicit Schem es.
40
Am bient
Beta=0.5 (Crank Nicolson)
Beta=0 (Explicit)
Beta=1 (Im plicit)
Temperature (oC)
35
30
25
20
15
0
30
60
90
120
150
Storage Period
180
210
240
(Days)
Fig. 6: Am bient and Predicted Tem peratures at the Silo Wall Based on the
Crank Nicolson, Explicit and Im plicit Schem es.
192
270
AU J.T. 9(3): 187-192 (Jan. 2006)
45
Am bient
Temperature (oC)
40
Grain Surface
35
30
25
20
15
0
30
60
90
120
150
Storage Period
180
210
240
(Days)
Fig. 7: Am bient and Grain Surface Tem peratures of the Wooden Silo
193
270