Ulteriori informazioni
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.
Sommario
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.
Info autore
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.
Riassunto
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.
