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

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 (Psv), pulverizing efficiency (Ge), and power density (Ppd), 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 Psv-opt, Ge-opt, and Ppd-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 Psv-pr, Ge-pr, and Ppd-pr, respectively. The results indicate that the optimized values of Psv-opt, Ge-opt, and Ppd-opt models have an error of less than 8% compared to the statistically calculated values of the prototype test and outperform the values of Psv-ori, Ge-ori, and Ppd-ori obtained under the original parameters.

Parameter Calibration of Discrete Element Model of Wine Lees Particles

“…Currently, in the field of agricultural engineering, many scholars have conducted a lot of research on the calibration of discrete element model parameters of particles. Yunxia Wang, Fanyi Liu, and Tao Wu calibrated the discrete elemental model parameters of corn seeds, wheat, and clayey soil, respectively, based on angle of repose simulation experiments [6][7][8]; Xu Bing established a non-spherical particle model of buckwheat seeds using the auto-filling method and parameterized it using a discrete element model [9]. Wang Long designed a device to simultaneously determine the angle of repose and angle of accumulation of materials and calibrated the contact parameters of cotton seeds through physical and simulation tests [10].…”