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Vileiniskis, Marius, Remenyte-Prescott, Rasa, Rama, Dovile and Andrews, John (2016) Fault detection and diagnostics of a three-phase separator. Journal of Loss Prevention in the Process Industries, 41 . pp. 215-230. ISSN 0950-4230

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Abstract

A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.

A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator.

Item Type:	Article
RIS ID:	https://nottingham-repository.worktribe.com/output/977122
Keywords:	Three-phase separator ; fault detection ; fault diagnostics ; Bayesian Belief Networks ; BBN
Schools/Departments:	University of Nottingham, UK > Faculty of Engineering
Identification Number:	https://doi.org/10.1016/j.jlp.2016.03.021
Depositing User:	Eprints, Support
Date Deposited:	24 Mar 2016 12:05
Last Modified:	04 May 2020 20:03
URI:	https://eprints.nottingham.ac.uk/id/eprint/32502

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