Application of a multi-objective optimization framework in cement clinker calcination system: An adaptive evolutionary algorithm with robust selection technique

Abstract

Optimizing the cement clinker calcination (CCC) system is crucial for sustainability, but studies on enhancing coal and electricity efficiency and clinker quality under dynamic conditions are limited. Therefore, this study introduces a multi-objective optimization framework using the self-adaptive adjusted evolutionary operators-differential evolution (SaAEO-DE) algorithm for the enhancement of the system performance. Firstly, a three-objective optimization framework is established, selecting seven process operational parameters as decision variables. Then, the SaAEO-DE algorithm, which adjusts evolutionary operators based on the population’s evolutionary state during the population iteration process, is used to obtain a Pareto optimal solution set. By selecting the most robust solution, the robust optimal solution technique (ROST) further ensures the stability of system performance in complex environments. Finally, the effectiveness and superiority of the multi-objective optimization method were validated by comparing the SaAEO-DE algorithm with classical multi-objective optimization algorithms, using actual production data from a cement plant in North China.