Application of Big Data Analytics in Process Safety and Risk Management
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© 2017 IEEE. In recent years, there has been an increasing interest in the field of big data analytics. It has been established that there exist large amounts of data in the energy industry1. However, there is a need to develop methods combining domain knowledge to transform this data into meaningful information to return business intelligence. The existing literature on big data analytics focuses on applications in various fields such as healthcare, aviation industry, finance, energy industry, and supply chain. However, within the energy industry, the application of big data analytics in process safety and risk management is in the nascent stages. The objective of this study is to discuss the potential of big data analytics in the area of process safety and risk management in the energy industry. The paper outlines the systemic framework with different stakeholders, data sources, challenges, and discusses the benefits of big data analytics in process safety. Four case studies with different applications ranging from incident database analysis, predictive modeling for pump failures, dynamic risk mapping of operating plant, and image analysis to gain insights are demonstrated. It is concluded that the application of big data analytics would provide valuable insights for more informed policy, strategic, and operational risk decision-making leading to a safer and more reliable industry.
author list (cited authors)
Goel, P., Datta, A., & Mannan, M. S.