Research on robust optimization of cement calcination process based on RMODE algorithm

Abstract

In the process of cement clinker calcination, the working conditions fluctuate dynamically, and multiple operational indices are interdependent. The inability to monitor key indicators, such as clinker quality and energy consumption, in real time, along with the absence of coordination mechanisms among various operational indicators, results in issues such as product instability, low energy efficiency, and insufficient robustness of the production system. To tackle these challenges under dynamic conditions, this paper proposes a robust optimization method for the cement calcination process (CCP). First, a prediction model for coal consumption and free calcium oxide (f-CaO) content is developed using a Time Series-Based Convolutional Neural Network (TS-CNN), incorporating the multi-time-scale characteristics and significant delays inherent in cement calcination data. Second, a multi-objective optimization model for the CCP is formulated by examining the relationships between process parameters and production indices. Subsequently, the mean effective function of the prediction model is defined as the fitness function, and a robust multi-objective difference algorithm (RMODE) is developed to solve the optimization model, yielding a robust optimal solution with high resistance to disturbances. Finally, comparative experiments are performed using real-world CCP data. The experimental results indicate that, compared to the baseline algorithm, the proposed method enhances system robustness while maintaining product quality and reducing coal consumption.