An improved economic-based nonlinear model predictive control strategy for the crude oil distillation process

Improving performance and increasing process profitability represent a priority for the long-term operation of the crude oil distillation process. To increase process profitability and maintain control performance, in this paper, an improved economic-based nonlinear model predictive control (NMPC) strategy is used to optimize the crude oil distillation (CDU) process. Considering the strong coupling and nonlinear dynamics of the CDU process, we make some modifications for the existing NMPC strategy. To reduce the complexity of the nonlinear prediction model, a support vector regression (SVR) based model is employed to describe the dynamical behaviour of the CDU process. Considering the coupling of the CDU process, we propose the concept of the coupling weighting factor to modify the objective function of the NMPC optimization problem. The simulation results demonstrate the effectiveness of the proposed improved NMPC algorithm for the CDU process.