Machine learning in process safety and asset integrity management

Book chapter (2022)

Authors

M. Yang The Australian Maritime College (AMC), Safety and Security Science - TPM
H. Sun China University of Petroleum (East China), Safety and Security Science - TPM
Rustam Abubakirov University of Bologna
Research Group
Safety and Security Science (TPM) (TU Delft)
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

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.