(657f) Operation Safety of Fluid Catalytic Cracking Process Via Model Predictive Control

Safety in chemical processes is an important corner stone for the well-being of the process and most important the individuals around them. However, there are many investigated accidents that show a recurrent gap between the usual safety analysis and the actual practices in the industry [1]. Particularly, it has been noted that the usual safety analysis in the industry (e.g., Operability Analysis (HAZOP), and Layer of Protection Analysis (LOPA)) relies on methodologies that do not account for real time operations [2]. More concretely, there is no direct link between the know-how extracted from these methodologies (i.e., HAZOP and LOPA) with the existing control system structure of the process. Model predictive control (MPC) is a solution that can be designed to link safety know-how into a constraint of the control structure in real time fashion [3].

Motivated by the above, this work focuses on simulating a major accident that occurred in a refinery in Torrance, CA [1], and tackling the accident with a Safeness Index-based MPC. For this, the key units of the plant, the Fluid Catalytic Cracking (FCC) unit and the main column, are simulated in Aspen Plus Dynamics. Then the Aspen model is partially validated with data obtained from the final report submitted by U.S. Chemical Safety and Hazard Investigation Board [1] for this specific Torrance accident. Subsequently, a data-driven model is developed based on open-loop simulation data generated using Aspen large-scale simulator. The Safeness Index-based MPC is then implemented in Matlab and co-simulated with the accident-involved refinery units in Aspen. In order to further investigate the accident, several magnitudes of the main disturbance are tested with the proposed controller using safety considerations and comparing with other methodologies.

[1] ExxonMobil Refinery Explosion, US Chemical Safety and Hazard Investigation Board, Washington, DC, 2017.

[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.

[3] Zhang, Z., Wu, Z., Rincon, D., Garcia, C., Christofides, P. D. (2019). Operational Safety of Chemical Processes via Safeness-Index Based MPC: Two Large-Scale Case Studies. Computers & Chemical Engineering, 125:204-215, 2019.