A Nonlinear Industrial Model Predictive Controller Using Integrated PLS and Neural Net State Space Model

摘要

Model predictive control (MPC) technology has been well developed and successfully applied in the refinery and petrochemical process industries over last 20 years. Recent development has been focused on nonlinear MPC and robust MPC technologiesbecause new challenges have been encountered in the polymer and chemical industries where many processes show strong nonlinearity and uncertainty. This paper presents a nonlinear industrial model predictive controller, recently developed by AspenTechnology, Inc. This MPC controller uses a nonlinear, state space, integrated PLS and Neural Net model (Zhao et al., 1998), and a multi-step, constrained, Newton-type optimization algorithm (Oliveira and Biegler, 1995). It results in a robust andcost-effective industrial nonlinear MPC controller. A pH reactor example and a successful industrial application in NOx emission control of a power plant are presented to demonstrate the capability of this controller.