An integrated computational intelligence technique based operating parameters optimization scheme for quality improvement oriented process-manufacturing system

The analysis and improvement of product quality for process industry is an increasing concern for academia and industry. As the outputs of a manufacturing system mainly depend on corresponding input conditions, so it is of high significance to develop an optimization scheme to actively and accurately determine operating parameters to obtain desired quality. However, the widely employed single-model modeling mode for whole production process neglects the natural characteristics within process manufacturing system such as multistage manufacturing and hysteresis. Additionally, the popular data-driven modeling techniques in current works, especially black-box machine learning models have been restricted to satisfying the requirements regarding excellent approximation capability and explicit mathematical expression simultaneously. To fill up above research gap, it is meaningful to develop a new data-driven optimization scheme in this work to effectively and accurately determine the optimum operating parameters considering the abovementioned characteristics and requirements. Firstly, two different connecting strategies are discussed to determine the more accurate and feasible quality propagation mode between adjacent stages. Then, two computational intelligence (CI) techniques, i.e., Multi-Gene Genetic Programming (MGGP) and Multi-objective Particle Swarm Optimization (MOPSO) algorithm are exploited to construct correlation model with explicit mathematical expression and derive the optimal operating parameters, respectively. Afterwards, the fuzzy Multi-criteria Decision Making (FMCDM) method is further proposed to select the optimal solution from the obtained Pareto solutions sets. The application of the proposed scheme in a coal preparation process indicates that the proposed scheme is promising and competitive on prediction accuracy and optimization efficiency over baseline methods, and can significantly improve the final product quality comparing with initial parameters setting. Moreover, the feasible quality specification for intermediate product can also be obtained by our proposed scheme which is beneficial for early detection of quality abnormality and timely parameters adjustment.