Fuzzy logic for process safety analysis

Markowski, A.; Mannan, M. Sam; Bigoszewska, Agata

doi:10.1016/j.jlp.2008.11.011

Cited by 178 publications

(64 citation statements)

References 13 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…According to the FMECA analysis report, in a petrochemical production system, the system or equipment contains several important functional items, including subsystems, components, and parts, and each has one or more failure modes [15]. In their risk evaluation, a failure mode corresponds to a variety of risk types, including safety risk, environmental risk, economic loss risk, and maintenance cost risk.…”

Section: Establishing a Hierarchical Structure Treementioning

confidence: 99%

See 1 more Smart Citation

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

et al. 2017

View full text Add to dashboard Cite

Abstract:The high hazard mechanical system (HHMS) has three characteristics in the petroleum and petrochemical industry (PPI): high risk, high cost, and high technology requirements. For a HHMS, part, component, and subsystem failures will result in varying degrees and various types of risk consequences, including unexpected downtime, production losses, economic costs, safety accidents, and environmental pollution. Thus, obtaining the quantitative risk level and distribution in a HHMS to control major risk accidents and ensure safe production is of vital importance. However, the structure of the HHMS is more complex than some other systems, making the quantitative risk analysis process more difficult. Additionally, a variety of uncertain risk data hinder the realization of quantitative risk analysis. A few quantitative risk analysis techniques and studies for HHMS exist, especially in the PPI. Therefore, a study on the quantitative risk analysis method for HHMS was completed to obtain the risk level and distribution of high-risk objects. Firstly, Fuzzy Set Theory (FST) was applied to address the uncertain risk data for the occurrence probability (OP) and consequence severity (CS) in the risk analysis process. Secondly, a fuzzy fault tree analysis (FFTA) and a fuzzy event tree analysis (FETA) were used to achieve quantitative risk analysis and calculation. Thirdly, a fuzzy bow-tie model (FBTM) was established to obtain a quantitative risk assessment result according to the analysis results of the FFTA and FETA. Finally, the feasibility and practicability of the method were verified with a case study on the quantitative risk analysis of one reciprocating pump system (RPS). The quantitative risk analysis method for HHMS can provide more accurate and scientific data support for the development of Asset Integrity Management (AIM) systems in the PPI.

show abstract

Section: Establishing a Hierarchical Structure Treementioning

confidence: 99%

“…The probability of the result event at one event node is defined by crisp numbers and calculated with the conventional rule, like Equation (15). Then, jump to Step 7.…”

Section: Making Fuzzification Processingmentioning

confidence: 99%

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

et al. 2017

View full text Add to dashboard Cite

show abstract

“…They used possibility-probability distribution as a new approach for analyzing risks. Mannan (2008, 2009a) and Markowski et al (2009b) developed a risk matrix based on fuzzy thinking and described fuzzification of the frequency and severity of the consequences of an incident scenario as basic inputs for fuzzy risk assessment. In addition, many attempts were made in the models at developing possibility-based risk assessments in environmental issues since there were many uncertainties and lack of information in environmental risk analysis.…”

Section: Dnvmentioning

confidence: 99%

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Karimpour

Zarghami

Moosavian

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

-A new risk assessment methodology by using fuzzy logic is proposed in this paper. The new Fuzzy Inference System (FIS) was established by the Mamdani algorithm based on different consequences of an incident. A combination of two FIS formed the proposed fuzzy method. Human knowledge and brainstorming were the devices for making the rules and interdependencies between variables in the new model. Different types of consequences and effective parameters were considered as inputs for the first fuzzy inference system. The final consequence was the preliminary result of the first inference model. It added to the probability of failures, as inputs of the second inference model. The result of the second inference model was the risk factor, which was considered as the final output of the proposed new fuzzy model. This model makes risk assessment more convenient in the absence of suitable data. In addition, decision-making will be easier, since its results are more understandable than the results of classical methods. A case study and a comparison between the classic method and the new fuzzy model illustrated that the results of the proposed model are more accurate, reliable and convenient for use in decision-making.Résumé -Nouveau modèle à logique floue pour une évaluation de risque basée sur différents types de conséquences -Une nouvelle méthode d'évaluation des risques en utilisant une logique floue est proposée dans le présent article. Le nouveau système d'inférence flou a été établi en utilisant l'algorithme de Mamdani basé sur les différentes conséquences d'un incident. Une combinaison de deux systèmes d'inférence flous a donné naissance à la méthode floue proposée. Les connaissances humaines et un brainstorming ont été les outils qui ont permis d'établir les règles et interdépendances entre des variables du nouveau modèle. Différents types de conséquences et de paramètres effectifs ont été pris en considération comme entrées pour le premier système d'inférence flou. La conséquence finale qui est le résultat préliminaire du premier modèle d'inférence, est combinée avec la probabilité de risques, en tant qu'entrées du second modèle d'inférence. Cette combinaison conduit à considérer le facteur de risque comme le résultat final du nouveau modèle flou proposé. Ce modèle rend l'évaluation des risques plus facile, en l'absence de données adaptées. En outre, la prise de décision sera plus simple, puisque ses résultats sont plus compréhensibles que les résultats des méthodes classiques. Une étude de cas et une comparaison entre la méthode classique et le nouveau modèle flou ont démontré que les résultats du modèle proposé étaient plus précis, plus fiables et plus faciles à utiliser dans la prise de décision.

show abstract

“…The application of fuzzy sets may improve data acquisition process. of course, The success of this method depends on quality of failure data collection of process components as well as on the cooperation with plant operation staff [6].…”

Section: Safety Analysismentioning

confidence: 99%

Fuzzy Logic Applications In Chemical Processes

Emami¹

2010

J. Math. Computer Sci.

View full text Add to dashboard Cite

Abstract:Fuzzy logic a new approach was intended to emulate human reasoning using calculations and operations with fuzzy groups and linguistic variables. Fuzzy variables describe qualitative expressions such as very slow, slow, fast, very fast, and so on. The application of fuzzy logic techniques has been increasing rapidly in the last few years. Fuzzy logic is used in target tracking, pattern recognition, robotics, power systems, controller design, chemical engineering, biomedical engineering, vehicular technology, economy management and decision making, aerospace applications, communications and networking, electronic engineering, and civil engineering. In many chemical engineering systems, the classification of product quality characteristics is performed by human experts, due to the absence of measuring devices. The development of mathematical models for such systems is a rather difficult task, since no equations based on first principles can be written. Chemical engineering has employed fuzzy logic in the detection of chemical agents as well as gas recognition. It has also been applied to processes control, batch distillation column, separation process, and kinetics. In this research, we investigate these applications in more detail.

show abstract

Fuzzy logic for process safety analysis

Cited by 178 publications

References 13 publications

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Fuzzy Logic Applications In Chemical Processes

Contact Info

Product

Resources

About