Multi-Level Progressive Parameter Optimization Method for the Complex Process Industry

The optimization of process parameters in the process industry usually faces challenges such as computational complexity caused by high-dimensional parameters. It is difficult for existing optimization methods to balance optimization accuracy with efficiency. Therefore, a multi-level progressive parameter optimization method for the complex process industry is proposed. Firstly, the importance evaluation method of parameters is constructed based on correlation analysis. By analyzing the correlation between process parameters and quality indicators, the importance rank of process parameters is realized. Then, a hierarchical method of process parameters based on importance evaluation is proposed. This method uses the sorted process parameters to divide the hierarchy, which lays a foundation for the subsequent multi-level progressive optimization. On this basis, a multi-level progressive modeling strategy is adopted to establish the nonlinear mapping relationship between process parameters and quality indicators layer by layer, and the optimal combination of process parameters is obtained with the improved particle swarm optimization. Finally, the data of a process production line is used for experimental verification. The verification results show that the proposed method can not only ensure the optimization accuracy but also significantly reduce the consumption of computing resources, which provides an efficient parameter optimization solution for the practical application of process industry.