Title: Integration of artificial neural network with genetic algorithm for an optimum performance of a Chironji (Buchanania lanzan) nut decorticator
Authors: Dileswar Pradhan; Rama Chandra Pradhan
Addresses: Department of Food Process Engineering, National Institute of Technology Rourkela, Rourkela – 769008, Odisha, India ' Department of Food Process Engineering, National Institute of Technology Rourkela, Rourkela – 769008, Odisha, India
Abstract: This study has explored the integrated artificial neural network - genetic algorithm (ANN-GA) technique for the optimum performance of a Chironji (Buchanania lanzan) nut decorticator. To have desired outcomes, the performance of the decorticator was evaluated at 54 different combinations of independent variables. Those variables are six levels of moisture content of the nuts (3%, 6%, 9%, 12%, 15%, and 18% d.b.), three levels of the roller speed (400, 450, and 500 rpm), and three levels of clearance between the roller and plate (6.44, 6.95, and 7.46 mm). The optimum values found were the moisture content of 12.06% (d.b.), speed of 413 rpm, and clearance of 6.64 mm. With the optimised values, 14.69% of whole kernels, 3.45% of broken kernels, and 5.20% of un-decorticated nuts were recovered. A decorticating efficiency of 94.8% and a machine efficiency of 76.77% was recorded when the decorticator was operated at optimum conditions. The outcomes of this study will be useful for nut processing and elsewhere.
Keywords: nut processing; decorticator; decortication; machine performance; decorticating efficiency; machine efficiency; modelling; artificial neural network; ANN; optimisation; genetic algorithm.
International Journal of Postharvest Technology and Innovation, 2020 Vol.7 No.2, pp.87 - 108
Received: 04 Dec 2019
Accepted: 13 Feb 2020
Published online: 10 Sep 2020 *