Simulation Analysis and Multiobjective Optimization of Pulverization Process of Seed-Used Watermelon Peel Pulverizer Based on EDEM

Xiaobin Mou, Fangxin Wan, Jinfeng Wu, Qi Luo, Shanglong Xin, Guojun Ma, Xiaoliang Zhou, Xiaopeng Huang () and Lizeng Peng ()
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Xiaobin Mou: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Fangxin Wan: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Jinfeng Wu: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Qi Luo: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Shanglong Xin: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Guojun Ma: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Xiaoliang Zhou: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Xiaopeng Huang: College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China
Lizeng Peng: Key Laboratory of Agro-Products Processing Technology of Shandong Province, Key Laboratory of Novel Food Resources Processing Ministry of Agriculture, Institute of Food and Nutrition Science and Technology, Shandong Academy of Agricultural Sciences, Jinan 250100, China

Agriculture, 2024, vol. 14, issue 2, 1-23

Abstract: To enhance the utilization of seed-used watermelon peel and mitigate environmental pollution, a hammer-blade seed-used watermelon peel crusher was designed and manufactured, and its structure and working parameters were optimized. Initially, the seed-used watermelon peel crusher and seed-used watermelon peel model were constructed, and the model’s parameters were calibrated. Subsequently, the discrete element method (EDEM2022) was employed to investigate the effects of spindle speed (MSS), the number of hammers (NCB), and feeding volume (FQ) on the pulverizing process. Multivariate nonlinear regression prediction models were developed for the percentage of pulverized particle size less than 8 mm ( P sv ), pulverizing efficiency ( G e ), and power density ( P pd ), followed by the analysis of influencing factors and prediction models using ANOVA. The multiobjective optimization of the prediction model utilizing the improved hybrid metacellular genetic algorithm CellDE resulted in solutions of 90.02%, 89.57%, and 8.35 × 10 −3 t/(h-kw) for P sv-opt , G e-opt , and P pd-opt , respectively. The corresponding optimal interaction values of MSS, NCB, and FQ were determined to be 1500 r/min, 108, and 150 kg/min. Finally, a prototype test was conducted by combining the optimal factor interaction values, yielding statistically calculated values of 96.63%, 92.37%, and 7.76 × 10 −3 t/(h-kw) for P sv-pr , G e-pr , and P pd-pr , respectively. The results indicate that the optimized values of P sv-opt , G e-opt , and P pd-opt models have an error of less than 8% compared to the statistically calculated values of the prototype test and outperform the values of P sv-ori , G e-ori , and P pd-ori obtained under the original parameters.

Keywords: hammer grinder; EDEM2022; multiobjective optimization; power density; prototype experiment (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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