An integrated approach for real-time hazard mitigation in complex industrial processes

•Optimization of safety-threshold for complex industrial processes.•Consider joint probabilities of multiple process variables leading to an accident.•Enables dynamic risk assessment based on multiple real-time process variables. Modern engineering systems give paramount importance to safety in orde...

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Vydáno v:	Reliability engineering & system safety Ročník 188; s. 297 - 309
Hlavní autoři:	Rebello, Sinda, Yu, Hongyang, Ma, Lin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Barking Elsevier Ltd 01.08.2019 Elsevier BV
Témata:	Accidents Bayesian analysis Bayesian network Contamination Economic impact Genetic algorithm Genetic algorithms Hazard mitigation Industrial hazard Markov chains Optimization Organic chemistry Process monitoring Process variables Real time Real variables Reliability engineering Risk assessment Safety Safety-threshold optimization System safety Process monitoring System safety Safety-threshold optimization Genetic algorithm Bayesian network Industrial hazard
ISSN:	0951-8320, 1879-0836
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Abstract	•Optimization of safety-threshold for complex industrial processes.•Consider joint probabilities of multiple process variables leading to an accident.•Enables dynamic risk assessment based on multiple real-time process variables. Modern engineering systems give paramount importance to safety in order to avoid or mitigate hazardous accidents which can lead to huge economic losses, environmental contamination, and human injuries. This paper proposes an integrated approach that uses both Hidden Markov Model and Bayesian Network to estimate an optimum safety-threshold for complex industrial processes. In order to estimate the safety threshold, the proposed approach considers different cost factors and the joint probabilities of multiple process variables leading to an accident. In addition to the system level threshold, it also estimates the safety-threshold for components. This helps in identifying the component that needs maintenance to enhance system performance and safety. Furthermore, it proposes a dynamic risk assessment methodology based on multiple real-time process variables. The optimum safety-thresholds are estimated using Genetic Algorithm which aims at minimizing the system running cost over a finite time horizon. A case study on Tennessee Eastman Chemical Process is presented to demonstrate the proposed methodology for optimizing process safety-threshold.
AbstractList	Modern engineering systems give paramount importance to safety in order to avoid or mitigate hazardous accidents which can lead to huge economic losses, environmental contamination, and human injuries. This paper proposes an integrated approach that uses both Hidden Markov Model and Bayesian Network to estimate an optimum safety-threshold for complex industrial processes. In order to estimate the safety threshold, the proposed approach considers different cost factors and the joint probabilities of multiple process variables leading to an accident. In addition to the system level threshold, it also estimates the safety-threshold for components. This helps in identifying the component that needs maintenance to enhance system performance and safety. Furthermore, it proposes a dynamic risk assessment methodology based on multiple real-time process variables. The optimum safety-thresholds are estimated using Genetic Algorithm which aims at minimizing the system running cost over a finite time horizon. A case study on Tennessee Eastman Chemical Process is presented to demonstrate the proposed methodology for optimizing process safety-threshold. •Optimization of safety-threshold for complex industrial processes.•Consider joint probabilities of multiple process variables leading to an accident.•Enables dynamic risk assessment based on multiple real-time process variables. Modern engineering systems give paramount importance to safety in order to avoid or mitigate hazardous accidents which can lead to huge economic losses, environmental contamination, and human injuries. This paper proposes an integrated approach that uses both Hidden Markov Model and Bayesian Network to estimate an optimum safety-threshold for complex industrial processes. In order to estimate the safety threshold, the proposed approach considers different cost factors and the joint probabilities of multiple process variables leading to an accident. In addition to the system level threshold, it also estimates the safety-threshold for components. This helps in identifying the component that needs maintenance to enhance system performance and safety. Furthermore, it proposes a dynamic risk assessment methodology based on multiple real-time process variables. The optimum safety-thresholds are estimated using Genetic Algorithm which aims at minimizing the system running cost over a finite time horizon. A case study on Tennessee Eastman Chemical Process is presented to demonstrate the proposed methodology for optimizing process safety-threshold.
Author	Ma, Lin Rebello, Sinda Yu, Hongyang
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Snippet	•Optimization of safety-threshold for complex industrial processes.•Consider joint probabilities of multiple process variables leading to an accident.•Enables... Modern engineering systems give paramount importance to safety in order to avoid or mitigate hazardous accidents which can lead to huge economic losses,...
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SubjectTerms	Accidents Bayesian analysis Bayesian network Contamination Economic impact Genetic algorithm Genetic algorithms Hazard mitigation Industrial hazard Markov chains Optimization Organic chemistry Process monitoring Process variables Real time Real variables Reliability engineering Risk assessment Safety Safety-threshold optimization System safety
Title	An integrated approach for real-time hazard mitigation in complex industrial processes
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