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         安全与防护协同创新中心

报告题目：Dynamic Risk Management of Hazardous Operations

报告人： Prof.Faisal Khan

Memorial University

时间：2020年7月3日上午09:00

报告人简介：

Dr. Faisal Khan is Professor and Canada Research Chair (Tier I) of Offshore Safety and Risk Engineering Risk at Memorial University, St John's, Canada. He is the founder of the Centre for Risk Integrity and Safety and Engineering (C-RISE), which have over research 90 research members. His areas of research interest include safety and risk engineering, inherent safety, and risk-based integrity assessment and management. He is recipient of President Outstanding Research Award of 2012-13, CSChE National Award on Process Safety Management of 2014, President Outstanding Research Supervision Award of 2013-14, and Society of Petroleum Engineer award for his contribution in Health, Safety and Risk Engineering (2015). He has authored over 500 research articles in peer-reviewed journals on safety, risk and reliability engineering and seven books on the subject area. He is Editor-in-Chief to the Journal of Process Safety and Environmental Protection and Safety in Harsh Environments, and subject Editor/Editorial Board Members to other safety and risk journals. He regularly offers training program/workshop on safety and risk engineering in different places including St John’s, Chennai, Dubai, Beijing, Aberdeen, Cape Town, Doha and Kuala Lumpur.

报告摘要：

Safety is a continuous activity with a constancy of purpose that must be controlled in real-time. As the process operates and generates incidents and near misses, the accident occurrence probability is predicted using accident precursors. The concept of developing a dynamic risk profile for a processing system, which encompasses the likelihood and consequences of a given abnormal event, is presented here. Dynamic risk estimation uses Bayesian theory to update the probability of an event occurrence and a generalized consequence algorithm to obtain the relative consequences of the given event. This approach results in a risk function, which has predictive capabilities and the ability to be updated with time. A dynamic overall loss modelling approach is recommended along with Bayesian Network to calculate the probability of occurrence of different undesirable events. These probabilities are updated utilizing real-time measurements. Different applications of the proposed approach to oil and gas operators are discussed here. This talk also touches system advances from a digitalization perspective and the challenges its present from a safety perspective. It concludes by suggesting a potential way forward that may help to address emerging safety challenges.