Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Taoqing Yang; Xia Zheng; Sriram K Vidyarthi; Hongwei Xiao; Xuedong Yao; Yican Li; Yongzhen Zang; Jikai Zhang

doi:10.3390/foods12091897

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Foods. 2023 May 5;12(9):1897. doi: 10.3390/foods12091897.

Authors

Taoqing Yang^{1

2

3}, Xia Zheng^{1

2

3}, Sriram K Vidyarthi⁴, Hongwei Xiao⁵, Xuedong Yao^{1

2

3}, Yican Li^{1

2

3}, Yongzhen Zang^{1

2

3}, Jikai Zhang^{1

2

3}

Affiliations

¹ College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
² Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China.
³ Key Laboratory of Modern Agricultural Machinery Corps, Shihezi 832003, China.
⁴ Department of Biological and Agricultural Engineering, University of California, One Shields Avenue, Davis, CA 95616, USA.
⁵ College of Engineering, China Agricultural University, Beijing 100080, China.

Abstract

This study combined an artificial neural network (ANN) with a genetic algorithm (GA) to obtain the model and optimal process parameters of drying-assisted walnut breaking. Walnuts were dried at different IR temperatures (40 °C, 45 °C, 50 °C, and 55 °C) and air velocities (1, 2, 3, and 4 m/s) to different moisture contents (10%, 15%, 20%, and 25%) by using air-impingement technology. Subsequently, the dried walnuts were broken in different loading directions (sutural, longitudinal, and vertical). The drying time (DT), specific energy consumption (SEC), high kernel rate (HR), whole kernel rate (WR), and shell-breaking rate (SR) were determined as response variables. An ANN optimized by a GA was applied to simulate the influence of IR temperature, air velocity, moisture content, and loading direction on the five response variables, from which the objective functions of DT, SEC, HR, WR, and SR were developed. A GA was applied for the simultaneous maximization of HR, WR, and SR and minimization of DT and SEC to determine the optimized process parameters. The ANN model had a satisfactory prediction ability, with the coefficients of determination of 0.996, 0.998, 0.990, 0.991, and 0.993 for DT, SEC, HR, WR, and SR, respectively. The optimized process parameters were found to be 54.9 °C of IR temperature, 3.66 m/s of air velocity, 10.9% of moisture content, and vertical loading direction. The model combining an ANN and a GA was proven to be an effective method for predicting and optimizing the process parameters of walnut breaking. The predicted values under optimized process parameters fitted the experimental data well, with a low relative error value of 2.51-3.96%. This study can help improve the quality of walnut breaking, processing efficiency, and energy conservation. The ANN modeling and GA multiobjective optimization method developed in this study provide references for the process optimization of walnut and other similar commodities.

Keywords: artificial neural network; drying; genetic algorithm; multi-objective optimization; shell breaking; walnut.

Abstract

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