Dynamic multi-objective optimization method for production index of cement clinker firing process based on collaborative prediction strategy

The cement clinker firing system is complex, with interdependent indicators, making it challenging to optimize decision-making. Furthermore, the traditional static single-objective or multi-objective optimization methods are inadequate in adapting to the dynamic changes of complex working conditions. To address these issues, this paper proposes a dynamic multi-objective optimization method for the production index of the cement clinker firing process. The way first establishes a data-driven dynamic multi-objective optimization model, the content of free calcium oxide (f-CaO) in clinker and the coal consumption of the sintering system as optimization objectives, numerous operational indicators as decision variables, considers dynamic factors and constraints during manufacturing. Then a dynamic multi-objective evolutionary algorithm based on a collaborative prediction strategy (CPS-DMOEA) is designed to solve the model. Experimental results for some benchmark test problems show a significant improvement in dynamic optimization performance with CPS-DMOEA. Furthermore, experiments using actual cement production data show that the proposed method outperforms traditional static multi-objective optimization methods.