An Adaptive, Advanced Control Strategy for KPI-Based Optimization of Industrial Processes

The need to deal with rapid change in an environmentally and economically friendly manner has led to renewed interest in data-driven, online process optimization. Although various methods, such as economic model predictive control (EMPC), are available to achieve this goal, they require that the process model be available and relatively accurate and that there be no process changes. Recently, the focus has shifted to using economic key performance indices (KPIs) to design supervisory controllers to regulate the process. In order to accomplish this, accurate models of the highly nonlinear KPIs are needed. A solution to this problem is to develop a two-step control strategy consisting of a static, offline component and a dynamic, online component. This paper proposes the use of a linear, BILIMOD method combined with a self-partitioning algorithm for the static component and gradient-based optimization method for the dynamic component. In order to deal with process changes, the static model parameters are updated. The proposed new controller strategy is tested on the wastewater treatment process. It is shown that the proposed method can quickly and effectively achieve the desired optimal point with minimal disturbance to the overall process.