Neural network modeling of drying of rice in BAU-STR dryer
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The experimental performance and artificial neural network modeling of rice drying in BAU-STR dryer is presented in this paper. The dryer consists of a biomass stove as a heat source, a perforated inner bin and a perforated outer bin with annular space for grains, and a blower (1 hp) to supply heated air. The dryer capacity was 500 kg of freshly harvested rice. Twenty experimental runs were conducted to investigate the experimental performance of the dryer for drying of rice. An independent multilayer neural network approach was used to predict the performance of the BAU-STR dryer for drying of rice. Ten sets of experimental data were used for training using back propagation algorithm and another ten sets of data were used for testing the artificial neural network model. The prediction of the performance of the dryer was found to be excellent after it was adequately trained. The statistical analysis showed that the errors (MSE and RMSE) were within and acceptable range of ±5% with a coefficient of determination (R2) of 99%. The model can be used to predict the potential of the dryer for different locations, and can also be used in a predictive optimal control algorithm.
Temperature at the center line of inner bin, °C.
Temperature in top layer of grain bin, °C.
Temperature in bottom layer of grain bin, °C.
Temperature in middle layer of grain bin at 0.26 m distance from the center line, °C.
Temperature in middle layer of grain bin at 0.32 m distance from the center line, °C.
Temperature in middle layer of grain bin at 0.38 m distance from the center line, °C.
Temperature in middle layer of grain bin at 0.44 m distance from the center line, °C.
Temperature in middle layer of grain bin at 0.50 m distance from the center line, °C.
moisture in top layer of grain bin, %.
moisture in bottom layer of grain bin, %.
moisture in middle layer of grain bin at 0.27 m distance from the center line, %.
moisture in middle layer of grain bin at 0.38 m distance from the center line, %.
moisture in middle layer of grain bin at 0.47 m distance from the center line, %.
meter per second.
This study is made possible by the support of the American People provided to the Feed the Future Innovation Lab for the Reduction Post-Harvest Loss through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by the United States Agency for International Development (USAID) under Cooperative Agreement No. (Grant Number: UReRA 2015-05296-01-00).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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