G. Unnikrishnan

Oil and Gas Processing Equipment (eBook, ePUB)

Risk Assessment with Bayesian Networks

• Format: ePub

Produktbeschreibung

Oil and gas industries apply several techniques for assessing and mitigating the risks that are inherent in its operations. In this context, the application of Bayesian Networks (BNs) to risk assessment offers a different probabilistic version of causal reasoning. Introducing probabilistic nature of hazards, conditional probability and Bayesian thinking, it discusses how cause and effect of process hazards can be modelled using BNs and development of large BNs from basic building blocks. Focus is on development of BNs for typical equipment in industry including accident case studies and its usage along with other conventional risk assessment methods. Aimed at professionals in oil and gas industry, safety engineering, risk assessment, this book

• Brings together basics of Bayesian theory, Bayesian Networks and applications of the same to process safety hazards and risk assessment in the oil and gas industry
• Presents sequence of steps for setting up the model, populating the model with data and simulating the model for practical cases in a systematic manner
• Includes a comprehensive list on sources of failure data and tips on modelling and simulation of large and complex networks
• Presents modelling and simulation of loss of containment of actual equipment in oil and gas industry such as Separator, Storage tanks, Pipeline, Compressor and risk assessments
• Discusses case studies to demonstrate the practicability of use of Bayesian Network in routine risk assessments

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 152
• Erscheinungstermin: 14. September 2020
• Englisch
• ISBN-13: 9781000174236
• Artikelnr.: 59908510

Autorenporträt

G. Unnikrishnan has over 40 years of experience in oil and gas industry. His experience spans the areas of process design, process safety, engineering & project management. He is currently on assignment as Engineering Specialist with a National Oil Company in the Middle East. He previously worked with engineering consultancy companies in India and abroad. His current work involves review and assessment of Front End Engineering Design and engineering management for upstream oil and gas projects.

He is keenly interested in optimization of process design and how it can be done with the highest process safety. He believes that much needs to be done in process plant design and operations to minimize accidents. He is an active researcher in the area and has presented and published papers on the subject in several international conferences and technical journals. He is a certified Functional Safety Engineer on Safety Instrumented Systems. He holds a degree in Chemical Engineering from Calicut University, MTech from Cochin University of Science & Technology and PhD from University of Petroleum and Energy Studies, Dehradun, India.

Inhaltsangabe

Introduction. Bayes Theorem, Causality and Building Blocks for Bayesian
Networks. Bayesian Network for loss of Containment in Oil & Gas Separator.
Bayesian Network for Loss of Containment in Hydrocarbon Pipelines. Bayesian
Network for Loss of Containment in Hydrocarbon Storage Tank. The Jaipur
Tank Farm Accident. Bayesian Network for Centrifugal Compressor Damage.
Bayesian Network for Loss of Containment in Centrifugal Pump. Other related
topics. References. Index.

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Introduction. Bayes Theorem, Causality and Building Blocks for Bayesian Networks. Bayesian Network for loss of Containment in Oil & Gas Separator. Bayesian Network for Loss of Containment in Hydrocarbon Pipelines. Bayesian Network for Loss of Containment in Hydrocarbon Storage Tank. The Jaipur Tank Farm Accident. Bayesian Network for Centrifugal Compressor Damage. Bayesian Network for Loss of Containment in Centrifugal Pump. Other related topics. References. Index.