Integration of resilience engineering and reinforcement learning in chemical process safety

Exothermic reactions carried out in batch reactors need a lot of attention to operate because any insufficient condition can lead to thermal runaway causing an explosion in the worst case. Therefore, a well-designed intervention action is necessary to avoid non-desired events. For this problem, we propose to use resiliencebased reinforcement learning, where the artificial agent can decide whether to intervene or not based on the current state of the system. One of our goals is to design resilient systems, which means designing systems that can recover after a disruption. Therefore, we developed the resilience calculation method for reactors, where we suggest the use of dynamic predictive time to failure and recover to better resilience evaluation. Moreover, if the process state is out of the design parameters, then we do not suggest calculating with the adaptation and recovery phase. We suggest using Deep Q-learning to learn when to intervene in the system to avoid catastrophic events, where we propose to use the resilience metric as a reward function for the learning process. The results show that the proposed methodology is applicable to develop resilient-based mitigation systems, and the agent can effectively distinguish between normal and hazardous states.

Automated summary

In this article, a resilience-based reinforcement learning approach is proposed to address the potential thermal runaway issue in batch reactors. By calculating the resilience metric for reactors and utilizing Deep Q-learning to decide when to intervene in the system, resilient-based mitigation systems can be effectively developed.