Dynamic Modelling of Low-Temperature Batch In-Bin Drying of Cobed Seed Maize: an Industrial Case Study
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ORIGINAL ARTICLE
Dynamic Modelling of Low-Temperature Batch In-Bin Drying of Cobed Seed Maize: an Industrial Case Study Augustine B. Makokha 1
&
Samwel C. Melly 2 & Isaiah E. Muchilwa 3
Received: 21 May 2020 / Revised: 11 September 2020 / Accepted: 14 September 2020 # The Korean Society for Agricultural Machinery 2020
Abstract Purpose In this paper, dynamic models have been developed to predict the air temperature, specific humidity and drying rate in an industrial seed grain dryer. Methods An industrial dryer was utilised for experimental measurements. The dyer was modelled as a system of serial cells characterised by heat and mass transfer with air back-mixing. Model equations were solved numerically based on the Levenberg– Marquardt algorithm in MATLAB. The model response was optimised by signal matching technique. The sensitivity of the model parameters on the prediction accuracy was assessed using Monte Carlo simulation tests. The accuracy of the models was statistically checked using the coefficient of determination (R2), the root mean square error (RMSE), and the probability value (p value) approach. Results The results revealed a good agreement between the measured data and model predictions with the highest mean relative deviation (MRD) of 2.37% and p value of 0.0298 at 5% significance level. The model accuracy was highly sensitive to the parameter that defines the air residence time inside the drying bin. The dryer exhibited air back-mixing level of 45.8% and moisture decay rate constant (k) of 0.028 per hour. The convective heat transfer between the air and seed grain was determined as 0.96 kJ/h m2 °C. Conclusions The dynamic models developed here can adequately predict the outlet air temperature, specific humidity and solids temperature and subsequently the drying curve. The models could help to provide real-time insights of drying characteristics, which is necessary for the achievement of a stable and effective control of the drying process. Keywords Drying . Energy . Humidity . Modelling . Moisture . Temperature
Introduction Maize remains an important cereal in the world, with annual global production exceeding that of wheat and rice. In the year
* Augustine B. Makokha [email protected] Samwel C. Melly [email protected] Isaiah E. Muchilwa [email protected] 1
School of Engineering, Department of Mechanical Engineering, Moi University, P.O. Box 3900, Eldoret 30100, Kenya
2
Kenya Seed Company, Ltd., P.O. Box 553, Kitale 30200, Kenya
3
Department of Agricultural Engineering, Universit t Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany
2019, maize production accounted for 41% (418 million tons) of total grain production in the world with seed maize production accounting for 12 million tons (USDA 2019). Drying is one of the primary operations in the production chain of seed maize, and it is known to contribute approximately half of all seed processing costs, largely attributable to energy expenditure (Arinze et al. 1996). The
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