A data-centric predictive control approach for nonlinear chemical processes

Abstract

Many nonlinear chemical processes (e.g., aluminum smelter cells) are operated under simple logic or constraint-based control strategies, which often do not achieve a high level of performance. A challenge for implementing model-based process control (e.g., model predictive control) is the cost, time, and effort associated with building an accurate dynamic model of the physical system. By integrating machine learning and control theory, this work presents a novel big data-centric predictive control (DPC) approach for nonlinear chemical processes. During the offline training period, the proposed approach utilizes a learning algorithm – Random Forests with linear model trees (RF-LMT) to learn black-box models from the process operation data. For online implementation, the DPC controller uses input-output trajectories to retrieve local linear models from the linear model trees, which are then applied to predictive control for process constraints handling and performance optimization in a receding horizon. The closed-loop stability of the proposed DPC approach is ensured by an incremental dissipativity based condition. Furthermore, a systematic method is developed to derive such an offline stability constraint from the data-centric model learned from RF-LMT. The proposed DPC approach is demonstrated by a case study on control of the aluminum smelter cell, which is under logic control in current industrial practice. The study shows that the proposed approach can significantly improve the system performance by using the existing operational data.