Seed melon pulping is a critical process in the full utilization of seed melon. However, controlling the performance during the pulping process presents several challenges, particularly the unclear relationship between pulping performance and process parameters. This study proposes an optimization of seed melon pulping process parameters based on the optimal Latin hypercube sampling (OLHS) method. The seed melon pulping rate and the large-particle ratio after pulping were selected as performance indicators, with process parameters including the feeding rate of rind–flesh, the rotational speed of first-channel pulping knife roller, and the rotational speed of second-channel pulping knife roller. The OLHS method was combined with the discrete element method (DEM) of pulping to derive the input parameters required for training the radial basis function neural network (RBFNN). Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) was employed to find the optimal solution for the pulping performance approximation model, followed by validation through comparison experiments. The multi-objective optimization results showed that the optimal process parameters were rind–flesh feeding rate of 175.69 kg/min−1, first-channel pulping knife roller rotational speed of 797.71 r/min−1, and second-channel pulping knife roller rotational speed of 708.34 r/min−1. Under these parameters, the seed melon pulping rate reached 92.81%, and the large-particle ratio after pulping was 2.19%. Furthermore, the RBFNN-trained approximation model demonstrated a high degree of model fit for the process parameters and performance indicators, as well as strong predictive ability for the macroscopic behavior of the pulping process parameters. Further verification through seed melon pulping experiments showed consistent results with the simulation outcomes, indicating that the optimization results can effectively improve seed melon pulping performance and further confirm the reliability of the method.
Luo et al. (Sun,) studied this question.