Machine learning in process safety and asset integrity management

M. Yang*, H. Sun, Rustam Abubakirov

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review


Artificial Intelligence (AI) is a scientific subject investigating and developing theories, methods, technologies, and application systems to simulate, extend, and expand human intelligence. Research in AI includes robotics, language recognition, image recognition, natural language processing, and expert systems. As a comprehensive frontier technology, machine Learning (ML), an essential part of AI, has drawn widespread attention. This chapter discusses the application of ML in process safety and asset integrity management (AIM). It gives a brief literature review of the state-of-the-art of AI in process safety and AIM and describes the use of ML approaches in probabilistic risk assessment. The chapter also presents a conceptual model for big-data-driven AIM. Failure mode and effect analysis is used for damage mode identification and cause and effect characterization. Random forest regressor is an ensemble algorithm that comprises a set of decision trees built independently and with a different structure.
Original languageEnglish
Title of host publicationMachine Learning in Chemical Safety and Health
Subtitle of host publicationFundamentals with Applications
EditorsQingsheng Wang, Changjie Cai
PublisherJohn Wiley & Sons
Number of pages20
ISBN (Electronic)9781119817512
ISBN (Print)9781119817482
Publication statusPublished - 2022


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