Smart cooling-aeration guided by aeration window model for paddy stored in concrete silos in a depot of Guangzhou, China

Abstract A smart cooling-aeration (SCA) system was developed and tested for cooling bulk paddy stored in concrete silos in a south-subtropical depot of Guangzhou, China. Besides lowering the grain temperature with natural cold air during winter months, the cooling-aeration operation was also required to minimize grain moisture content loss, as well as the fan noise nuisance to nearby residents. Based on the grain and ambient air condition, the SCA system used the collected information in Aeration Window Model (AWM) for optimal control of aeration fans. The AWM was based on the CAE adsorptive equation and equilibrium absolute humidity curve. The SCA system was tested in two concrete silos of 650 tonnes in Guangzhou (22°44′N, 113°30′E) from 7 December 2018 to 5 March 2019. The system was set to turn on the fans when the temperature of grain bulk was 3 °C higher than the ambient air temperature and the equilibrium absolute humidity of grain for adsorption was above the absolute humidity of the ambient air. The grain temperature in one silo decreased by 10.3 °C after 97.8 h of aeration. The unit energy consumption by one 5.5-kW centrifugal fan was 0.063 k W h t ∙ ° C - 1 . The grain temperature was lowered by 9.2 °C in 106.5 h in the second silo with the unit energy consumption of 0.031 k W h t ∙ ° C - 1 by a 2.2-kW centrifugal fan. The unit energy consumption for both silos were 52−77% lower than the lowest “typical” value (0.132 k W h t ∙ ° C - 1 ) in similar type of silos in the region when cooling aeration is manually controlled. Compared to the 650 tonne paddy rice in the control silo with a 15 kW aeration fan, the grain moisture content, the total milled rice yield and grain processing quality was unchanged by the smart cooling aeration with smaller kW aeration fans.

[1]  Chiachung Chen,et al.  FACTORS WHICH EFFECT EQUILIBRIUM RELATIVE HUMIDITY OF AGRICULTURAL PRODUCTS , 2000 .

[2]  S. M. Henderson,et al.  Desorption Isotherms of Rough Rice from 10 C to 40 C , 1979 .

[3]  Da-Wen Sun,et al.  Comparison and selection of EMC/ERH isotherm equations for rice , 1999 .

[4]  M. A. Basunia,et al.  Moisture desorption isotherms of medium-grain rough rice. , 2001, Journal of stored products research.

[5]  W. E. Muir,et al.  Comparison of mathematical models to simulate aeration of wheat stored in Brazil , 1996 .

[6]  G. H. Foster,et al.  Mathematical Simulation of Corn Drying — A New Model , 1968 .

[7]  Dirk E. Maier,et al.  AERATION STRATEGY SIMULATIONS FOR WHEAT STORAGE IN THE SUB-TROPICAL REGION OF NORTH INDIA , 2011 .

[8]  G. Mazza,et al.  EQUILIBRIUM MOISTURE CHARACTERISTICS OF SAFFLOWER SEEDS , 1991 .

[9]  Thomas L. Thompson,et al.  Temporary Storage of High-Moisture Shelled Corn Using Continuous Aeration , 1972 .

[10]  P. Vírseda,et al.  Moisture desorption isotherms of rough rice at high temperatures , 2007 .

[11]  Juan M. Corchado,et al.  Energy Optimization Using a Case-Based Reasoning Strategy , 2018, Sensors.

[12]  T. Siebenmorgen,et al.  Equilibrium Moisture Contents of Pureline, Hybrid, and Parboiled Rice Kernel Fractions , 2012 .

[13]  V. K. Jindal,et al.  Effects of Moisture Adsorption on the Head Rice Yields of Long-Grain Rough Rice , 1986 .

[14]  A. Singh,et al.  Influence of Milling Parameters on Head Rice Recovery: A Review , 2017 .

[15]  Terry J. Siebenmorgen,et al.  Effects of Variety and Harvest Moisture Content on Equilibrium Moisture Contents of Rice , 2000 .

[16]  T. Kameoka.  Equilibrium Moisture Content of Rice , 1988 .

[17]  J. L. Woods,et al.  Deep-bed simulation of the cooling of stored grain with ambient air: a test bed for ventilation control strategies , 1997 .

[18]  N. J. Burrell,et al.  Cooling bulk grain in the British climate to control storage insects and to improve keeping quality , 1964 .

[19]  Daniela de Carvalho Lopes,et al.  Aeration simulation of stored grain under variable air ambient conditions , 2006 .

[20]  Juan M. Corchado,et al.  SiloMAS: A MAS for Smart Silos to Optimize Food and Water Consumption on Livestock Holdings , 2018, DCAI.