2019 AIChE Annual Meeting

(740g) Operational Safety of an Ammonia Process Network Via Model Predictive Control

Authors

Zhihao Zhang - Presenter, University of California, Los Angeles
David Rincon, University of California, Los Angeles
Zhe Wu, University of California Los Angeles
Carlos Garcia, University of California, Los Angeles
Panagiotis Christofides, University of California, Los Angeles
Safety is a critical issue in chemical process operation. Despite efforts to prevent incidents in the chemical process industries [1], the continued occurrence of accidents is motivating research focused on enhancing process operational safety to protect human lives and the environment. Recent advances in chemical process safety utilizing a system engineering perspective have sought to incorporate safety considerations within model predictive control (MPC) [2].

Motivated by the above considerations, this work presents a practical application simulating multiple model predictive controllers within a multi-unit ammonia process network, all of which integrate safety constraints of the process within their design. Specifically, catalytic deactivation in the shift reactor is a common and problematic disturbance that may trigger reaction thermal runaway in the methanator. Two controllers designed with the objective of improving operational safety are implemented on the ammonia plant under catalyst deactivation. Aspen Plus is a commercial process simulation software which is integrated with Matlab to run a closed-loop simulation for the ammonia process network. The results demonstrate that under suitable design of the controllers, desirable closed-loop performance is achieved with high temperature extremities avoided during operation in the presence of significant catalytic deactivation in the shift reactor.

[1] Center for Chemical Process Safety. Guidelines for Hazard Evaluation Procedures. John Wiley & Sons, Inc., Hoboken, New Jersey, third edition, 2008.

[2] Albalawi, F., Durand, H., Alanqar, A., Christofides, P. D. Achieving operational process safety via model predictive control. Journal of Loss Prevention in the Process Industries, 53: 74-88, 2018.