Research Article Open Access

Parameters Optimization of Adaptive Cashew Shelling Cutter Based on BP Neural Network and Genetic Algorithm

Yun-Fei Fu1, Jie Gong1, Hui Huang1, Yi-Jun Liu1, De-ming Zhu1 and Peng-Fei Zhao1
  • 1 Chinese Agricultural Ministry Key Laboratory of Tropical Crop Products Processing, China
American Journal of Engineering and Applied Sciences
Volume 8 No. 4, 2015, 648-658

DOI: https://doi.org/10.3844/ajeassp.2015.648.658

Submitted On: 28 May 2015 Published On: 1 November 2015

How to Cite: Fu, Y., Gong, J., Huang, H., Liu, Y., Zhu, D. & Zhao, P. (2015). Parameters Optimization of Adaptive Cashew Shelling Cutter Based on BP Neural Network and Genetic Algorithm. American Journal of Engineering and Applied Sciences, 8(4), 648-658. https://doi.org/10.3844/ajeassp.2015.648.658

Abstract

The aim of this study is to determine the optimal parameters of the adaptive cashew shelling cutter. To meet the requirements of cashew nut processing enterprises, this study takes the whole-kernel rate as the optimization objective. Let the three main parameters of the adaptive cashew shelling cutter: The distance between upper and lower cutters, the pre-pressure of the spring and the velocity of the scraper be design variables. The BP neural network and genetic algorithm is used to find out the optimal parameters based on the limited shelling test data of cashew nuts. The optimal test result and its corresponding parameters are obtained by executing the BP neural network and genetic algorithm. The optimal whole-kernel rate is 0.0533 kg min-1 and the optimal parameters are the distance between upper and lower cutters of 8.37 mm, the pre-pressure of the spring of 141.56 N and the velocity of the scraper of 0.57 m sec-1. To evaluate the accuracy of the predicted value, the shelling tests under the optimal parameters are carried out 5 times. The test results show that the error between the predicted value and actual value is in the range of 2.25 to 13.7%.

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Keywords

  • Adaptive Cashew Shelling Cutter
  • BP Neural Network and Genetic Algorithm
  • Whole-Kernel Rate
  • Shelling Test