Improved crude oil processing using second-order Volterra models and nonlinear Model Predictive Control

The petroleum industry operates a wide variety of chemical processes that can benefit from advanced modeling and control methods. Traditional linear control methods can be applied to these systems, but this often results in sub-optimal closed-loop performance. The current work presents modeling and control of a refinery facility simulation using second order Volterra series models and a nonlinear model predictive control formulation. Realistic process data were generated using a dynamic refinery simulation model. The data set from the crude oil separation facility simulation was used to determine an empirical model for use with nonlinear model predictive control (MPC). Results show that a second-order Volterra model can be used to represent the multivariable chemical plant which exhibits both nonlinear gains and nonlinear dynamics. It is demonstrated that the proposed nonlinear MPC formulation tracks setpoints and rejects disturbances better than traditional linear control methods.