Parameters optimization of a walnut shell-kernel separation device using response surface methodology and ANN coupled genetic algorithm - Liu, Hualong; Mao, Biqi; Zhang, Hong; Chen, Peiyu; Tang, Yurong; Fan, Xiuwen; Zhang, Yongcheng; Wang, Jianping; Zeng, Yong (International Journal of Agricultural and Biological Engineering, 2025)
Walnut shell-kernel separation after cracking is crucial for providing raw materials for further processing. However, impurities and losses during separation limit complete separation. To address this, a two-stage tandem separation device was designed and optimized. Computational fluid dynamics (CFD) was used to analyze the effects of four bent duct structures on the flow field. Response surface methodology (RSM) and artificial neural networks (ANN) were employed to predict separation performance under various conditions. Both models accurately predicted performance, with ANN showing superior predictive ability. The optimal design was determined using non-dominated sorting genetic algorithm-II (NSGA-II) and technique for order preference by similarity to an ideal solution (TOPSIS): the inclination of the first stage deflector plate (x1) was 39°,the inclination of the second stage deflector plate (x2) was 36°, the wind speed of the first stage fan (x3) was 21 m/s, and the wind speed of the second stage fan (x4) was 13.5 m/s; impurity rate (y1) was 4.51%, and loss rate (y2) was 6.62%. Compared with traditional single-stage devices, the optimized device reduced impurity rate by 73.98% to 77.55% and loss rate by 9.44% to 53.96%, significantly improving separation efficiency and quality. This study provides theoretical guidance for designing and optimizing shell-kernel separation devices. Keywords: walnut, two-stage separation device, multi-objective optimization, response surface methodology, artificial neural network DOI: 10.25165/j.ijabe.20251805.9375 Citation: Liu H L, Mao B Q, Zhang H, Chen P Y, Tang Y R, Fan X W, et al. Parameters optimization of a walnut shell-kernel separation device using response surface methodology and ANN coupled genetic algorithm. Int J Agric & Biol Eng, 2025; 18(5): 306–316.