Uncertainty handling in safety instrumented systems according to IEC 61508 and new proposal based on coupling Monte Carlo analysis and fuzzy sets

Innal, Fares; Chebila, Mourad; Dutuit, Yves

doi:10.1016/j.jlp.2016.07.028

Cited by 18 publications

(3 citation statements)

References 28 publications

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“…To perform these calculations, IEC 61508 provides simplified analytical formulas that are valid under some assumptions [14]. Furthermore, several simplified formulas can be found in the literature [15][16][17].…”

Section: Real Safety Integrity Level Of the Sis (Real Sil)mentioning

confidence: 99%

Evaluation of Safety Instrumented System in a Natural Gas Facility According to IEC 61508 Standard

Boudjoghra,

Innal

2023

IJSSE

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The work presented was aimed primarily at contributing to the control of risks relating to an industrial installation by means of a safety instrumented system (SIS), through a global approach defined by the IEC 61508 standard which goes from risk analysis to the evaluation of SIS. The IEC 61508 approach consists of industrial risk analysis which is a process used to identify the risks inherent in an industrial system and estimate the levels of these risks using several methods, tools and software. Then, the reduction of risks deemed unacceptable in the risk analysis phase to an acceptable level using SIS. The extent of the reduction to be achieved determines the level of performance that the SIS must have to achieve this reduction assigned to it (required SIL). The real performance of SIS (real SIL) must meet the corresponding requirements. Otherwise, technical modifications must be made to the SIS. The application of the IEC 61508 approach addresses the risk analysis of butane product overflow stored in a tank and the verification of the ability of an SIS installed at this tank to control this risk. And if necessary, propose technical modifications to optimize the performance of the SIS. Risk analysis conducted using the HAZOP (HAZard and OPerability) method to identify critical accident scenarios, PHAST (Process Hazard Analysis Software Tool) software and risk matrix to estimate risk level of a given accident. Verification of the ability of an SIS installed at the tank to control the risk deemed unacceptable in the risk analysis phase is carried out by The LOPA (Layer Of Protection Analysis) method for the allocation of the required SIL for the SIS and SIL module of the GRIF (GRaphical Interface for reliability Forecasting) software to calculated real SIL of SIS. Depending on the results obtained (required SIL > real SIL), recommendations likely to strengthen the reliability of SIS are proposed (modification of the test policy and the architecture of the constituent elements of the SIS) in order to achieve the required SIL. And thus secure our system.

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Section: Real Safety Integrity Level Of the Sis (Real Sil)mentioning

confidence: 99%

Evaluation of Safety Instrumented System in a Natural Gas Facility According to IEC 61508 Standard

Boudjoghra,

Innal

2023

IJSSE

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“…Therefore, probability distributions and fuzzy sets with their application are implemented jointly in risk assessment. Some of the related studies are as follows: Guyonnet et al (1999, 2003) discussed hybrid uncertainty modelling; Kentel and Aral (2004) studied the probabilistic-fuzzy health risk modelling; Baudrit et al (2006, 2008) studied probability-fuzzy-based uncertainty modelling; Anoop et al (2008) studied the safety assessment of austenitic steel nuclear power plant pipelines against stress corrosion cracking in the presence of hybrid uncertainties; Baraldi and Zio (2008) combined Monte Carlo and possibilistic approach to uncertainty propagation in event tree analysis; Limbourg and de Rocquigny (2010) studied uncertainty analysis using evidence theory—confronting level 1and level 2 approaches with data availability and computational constraints; Chen et al (2010) proposed a hybrid fuzzy-stochastic modelling approach in environmental risk assessment of offshore produced water discharges; Flage et al (2013) studied the probabilistic and possibilistic treatment of epistemic uncertainties; Karami et al (2013) studied the fuzzy logic and adaptive neuro-fuzzy inference system for characterisation of contaminant exposure; Pedroni et al (2012, 2013) studied the propagation of aleatory and epistemic uncertainties; Arunraj et al (2013) proposed an integrated approach with fuzzy set theory and Monte Carlo simulation for uncertainty modelling in risk assessment; Pastoor et al (2014) studied the road map for human health risk assessment in the twenty-first century; Santillana Farakos et al (2013, 2014, 2016) studied the risk assessment for Salmonella in tree nuts, Salmonella in low-water activity foods and Salmonella in low-moisture foods; Zwietering (2015) studied the uncertainty modelling for risk assessment and risk management for safe foods; Rębiasz et al (2017) studied the joint treatment of imprecision and randomness in the appraisal of the effectiveness and risk of investment projects; Innal et al (2016) studied the uncertainty handling in safety instrumented systems according to International Electrotechnical Commission (IEC) and the new proposal based on coupling Monte Carlo analysis and fuzzy sets; Abdo and Flaus (2016) proposed a new approach with randomness and fuzzy theory for uncertainty quantification in dynamic system risk assessment; Zhang et al (2016) studied the risk assessment of shallow groundwater contamination under irrigation and fertilisation conditions; Dutta (2017) devised a technique for joint propagation of aleatory and epistemic uncertainties; Liu et al (2017) studied the pos...…”

Section: Related Workmentioning

confidence: 99%

A General Amalgamate Technique to Evaluate Human Health Risk Under Uncertain Circumstances

Dutta

Akhtari

2022

Journal of Health Management

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Evaluation of humans’ health risk is an essential and most demanding aid in relevance to the process of decision-making. Accumulation of quality knowledge on the attributes of each and every available data, information and model parameters, involved in risk assessment, plays a crucial role in the process of evaluation. It is important to note that, most frequently, model parameters are imprecise due to the availability of limited data and knowledge. Under such circumstances, probability theory (PT) and the theory of fuzzy sets can be brought forth to deal with the emerging uncertainties. There is also a need to devise an amalgamate technique to perform health risk assessment under uncertainty. Although some different approaches are available in this regard, all approaches are situation or problem dependent and fail to address some specific issues. Therefore, this article presents a general amalgamate technique to address all the concerned issues, and, finally, health risk is carried out using this approach.

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“…Elite work has been done in the past to enhance the production and performance of various industrial systems (Panchal and Kumar, 2016;Arora and Kumar, 1997;Khanduja et al, 2011;Kumar, 2019, 2020), including thermal power plant (Gupta and Tewari, 2011;Kumar and Ram, 2013), nuclear power plant, sugar mill plant, marine power plant (Kumar and Ram, 2015) and paper mill plant (Ram and Kumar, 2015) by obtaining several performance measures, including reliability, availability, mean time to failure (MTTF), etc. For the modeling of a multi-state system in which level of redundancies varies with time due to component failure and repair, Markov approach, Petri Net are recommended by the authors (Guo and Yang, 2007;Rouvroye and Brombacher, 1999;Innal, 2008;Kumar and Ram, 2019;Li, 2016;Ram and Manglik, 2016). Kumar et al (1996) investigated the shell gasification and carbon recovery process in a urea fertilizer plant (UFP) and find the steady state availability of the plant.…”

Section: Introductionmentioning

confidence: 99%

Process modeling for decomposition unit of a UFP for reliability indices subject to fail-back mode and degradation

Kumar

Ram

2023

JQME

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PurposeEnsuring safe operation of a urea fertilizer plant (UFP) is a vital aspect for its functioning and production. Clearly the safe operation of such systems can only be archived with proper and effective maintenance scheduling and through controlling its failures as well as repairs of the components. Also for this, the concern plant management must have the information regarding the failures that affects the system's performance most/least. The objective of this study is to analyze mathematically the factors that are responsible for the failure/degradation of the decomposition unit of UFP.Design/methodology/approachThe considered system has been modeled by the aid of Markov's birth–death process with two types of failures for its components: variable (which are very similar in practical situations) and constant. The mathematical model is solved by the help of Laplace transform and supplementary variable technique.FindingsIn the present paper, the availability, reliability and mean time to failure (MTTF) are computed for the decomposition unit of the UFP. The critical components that affect the reliability and MTTF of the decomposition unit are identified through sensitivity analysis.Originality/valueIn this paper, a mathematical model based on the working of the decomposition unit of a UFP has been developed by considering two types of failure, namely, variable failures rates and constant failure rates (which has not been done in the literature for the decomposition unit). Conclusions in this paper are good references for the improvement of the same.

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Uncertainty handling in safety instrumented systems according to IEC 61508 and new proposal based on coupling Monte Carlo analysis and fuzzy sets

Cited by 18 publications

References 28 publications

Evaluation of Safety Instrumented System in a Natural Gas Facility According to IEC 61508 Standard

Evaluation of Safety Instrumented System in a Natural Gas Facility According to IEC 61508 Standard

A General Amalgamate Technique to Evaluate Human Health Risk Under Uncertain Circumstances

Process modeling for decomposition unit of a UFP for reliability indices subject to fail-back mode and degradation

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