Parameter Optimization of Wet Stirred Media Milling Using an Intelligent Algorithm-Based Stressing Model

Wet stirred media milling (WSMM) is a popular grinding method used to produce important ultrafine-particle materials, such as pigments, pharmaceuticals, and pesticides. Therefore, it is crucial to improve the process capability and quality of WSMM by setting optimal parameters. This study proposes a multi-objective optimization methodology based on an intelligent algorithm to optimize the ultra-fine grinding parameters; this can mitigate the issue whereby grinding parameters are difficult to determine during wet grinding industrial production. A mechanistic model is proposed based on the analysis of energy dissipation mechanisms. The specific energy in the WSMM process is quantified using a stressing model. A shuffled frog leaping algorithm (SFLA)-based stressing model is proposed to maximize the specific stress intensity and specific stress number of the entire system under the constraint of the product particle size and grinding time, which provides the optimal process parameters. The performance of the proposed strategy is validated using two case studies in different industrial optimization scenarios. The result of the first case study illustrates that, in comparison to a quadratic programming-based response surface methodology, the proposed SFLA-based stressing model greatly enhances the wet grinding efficiency (decreasing P80 from 3.28 μm to 2.88 μm). In the second case study, the parameter optimization under different feed particle sizes and different productivities was discussed. The results confirmed that the optimized parameters can achieve the minimum particle size (P50 = 1.78 μm) and maximum solid concentration (Cv = 120 g/L) within the minimum grinding time (tg = 5 min). The contribution of our work lies in the fact that the proposed SFLA-based stressing model can direct multiple-objective decision-making in a more efficient way without requiring costly experimental procedures to acquire the optimized parameters in WSMM. The proposed approach is systematic and robust and can be integrated into WSMM architectures for parameter optimization in other complex wet grinding systems.