Nonlinear Control of Fouling in Polyethylene Reactors

Fouling formation in reactor vessels poses a serious threat to the safe operation of the industrial low-density polyethylene (LDPE) polymerization. Fouling also degrades the polymer quality and causes productivity loss to some extent. In this work, neural Wiener model predictive control (NWMPC) is introduced to address the fouling concern. In addition, a soft sensor model is used to activate the fouling-defouling (F-D) mechanism when the fouling surpasses the thickness limit to prevent vessel overheating. NWMPC is proven to be fast, stable, and robust under various control scenarios. The use of a soft sensor model in conjunction with NWMPC enables the online monitoring and controlling of the F-D processes. When comparison is made with a state space (SSMPC) utilizing only the linear block, NWMPC is found to be able to control the LDPE grade with quicker grade transition and lower resource consumption.

Automated summary

The study introduces the neural Wiener model predictive control (NWMPC) to address fouling formation in low-density polyethylene (LDPE) polymerization, and utilizes a soft sensor model to monitor and control the fouling-defouling process. Compared to state space model predictive control (SSMPC), NWMPC proves to be faster in controlling the transition of LDPE grade and consumes fewer resources.