Data-driven real-time control method for process equipment in flow shop towards product quality improvement

Abstract

Real-time control of complex process equipment is a crucial aspect for product quality assurance in the flow shop. However, there are two bottleneck problems, including the inaccurate quality prediction and the unstable process control, which would lead to poor product quality. To solve these problems, a data-driven real-time product quality prediction and control method for the process equipment is elaborated in this paper. First, to improve the prediction accuracy, a delay-mean rule is designed to eliminate the negative effects of time lag focused on the modeling data. Second, a variable optimization method based on the improved simulated annealing algorithm and a time window is proposed to enhance the stability of control. Finally, a case study regarding the real-time control for a dryer equipment is used to verify the proposed method.