Implementing Artificial Intelligence in Process Safety Studies

AI can enhance risk assessments — starting with process hazard analysis (PHA) revalidation and expanding to other studies — provided that proper digitalization initiatives are in place.

Artificial Intelligence (AI) has been advancing rapidly, and its capabilities can be leveraged for effective and optimal utilization of resources in the chemical process industries (CPI), especially the oil and gas industry. These industries need to conduct several process safety management (PSM) studies as part of their risk management strategy, which requires significant resources, including competent personnel, time, and money.

This article explores how different PSM studies can benefit from AI. The successful implementation of AI depends on the availability of quality labeled data and plant-specific training of the AI model. In the CPI today, most plant data is not available in a labeled format and cannot be used directly in machine learning algorithms. Therefore, after carefully reviewing the data availability and resource requirements for PSM studies, this article suggests starting with rule-based AI implementation as an initial step, followed later by the application of machine learning and deep learning concepts.

Implementing AI in PSM studies can be a game-changer, and this article discusses the necessity and limitations of such an undertaking. This article also proposes a roadmap to help the CPI integrate AI into its PSM studies with ease and efficiency.