Dynamic modeling of TENORM exposure risk using SMART approach

Khalid Al Nabhani, Faisal I. Khan, Ming Yang

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

Modern industrial processes are highly instrumented with more frequent recording of data. This provides abundant data for safety analysis; however, these data resources have not been well used. This paper presents an integrated dynamic failure prediction analysis approach using principal component analysis (PCA) and the Bayesian network (BN). The key process variables that contribute the most to process performance variations are detected with PCA; while the Bayesian network is adopted to model the interactions among these variables to detect faults and predict the time-dependent probability of system failure. The proposed integrated approach uses big data analysis. The structure of BN is learned using past historical data. The developed BN is used to detect faults and estimate system failure risk. The risk is updated subsequently as new process information is collected. The updated risk is used as a decision-making parameter. The proposed approach is validated through a case of a crude oil distillation unit operation.
Original languageEnglish
Pages (from-to)1-14
JournalJournal of Petroleum Exploration and Production Technology
Volume3
Publication statusPublished - 2017

Fingerprint

Bayesian networks
Principal component analysis
modeling
principal component analysis
distillation
integrated approach
Distillation
crude oil
Crude oil
Decision making
decision making
risk exposure
resource
prediction

Cite this

Dynamic modeling of TENORM exposure risk using SMART approach. / Al Nabhani, Khalid; Khan, Faisal I.; Yang, Ming.

In: Journal of Petroleum Exploration and Production Technology, Vol. 3, 2017, p. 1-14.

Research output: Contribution to journalArticle

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