A fuzzy-HAZOP/ant colony system methodology to identify combined fire, explosion, and toxic release risk in the process industries

Solukloei, Hamid Reza Jamshidi; Nematifard, Salehe; Hesami, Ahmad; Mohammadi, Heidar; Kamalinia, Mojtaba

doi:10.1016/j.eswa.2021.116418

Cited by 14 publications

(2 citation statements)

References 94 publications

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“…Fire represents an event with potential catastrophic consequences-property and environment damage and bodily injury-if not properly contained and the combustion prerequisites are met. This is especially applicable to forest fires (Saha et al [1], Kala [2], Elogne et al [3]), the chemical and oil industry (Lowesmith et al [4], Aydin et al [5], Solukloei et al [6]), various process industries (Park et al [7], Zhou and Reniers [8]), buildings (Østrem and Sommer [9], Ibrahim et al [10]), transportation (Wang et al [11]), and ecosystems and wildlife (Davies et al [12], Lindenmayer et al [13], Liu et al [14], Batista et al [15]).…”

Section: Introductionmentioning

confidence: 99%

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

Diaconu

2023

Fire

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Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning algorithms. In order to understand how an artificial intelligence application penetrates an area of fire detection, a quantitative scientometric analysis was first performed. A literature search process was conducted on the SCOPUS database using terms and Boolean expressions related to fire detection techniques and machine learning areas. A number of 2332 documents were returned upon the bibliometric analysis. Fourteen datasets used in the training of deep learning models were examined, discussing critically the quality parameters, such as the dataset volume, class imbalance, and sample diversity. A separate discussion was dedicated to identifying issues that require further research in order to provide further insights, and faster and more accurate models.. The literature survey identified the main issues the current research should address: class imbalance in datasets, misclassification, and datasets currently used in model training. Recent advances in deep learning models such as transfer learning and (vision) transformers were discussed.

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Section: Introductionmentioning

confidence: 99%

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

Diaconu

2023

Fire

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“…Oil and gas firms utilise a variety of risk assessment approaches, including as hazard and operability (HAZOP) evaluation, fault tree analysis, scenario-based analysis, and indexing methodologies 12 – 14 .…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of interstate pipelines using a fuzzy-clustering approach

Osman

Shehadeh

2022

Sci Rep

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Interstate pipelines are the most efficient and feasible mean of transport for crude oil and gas within boarders. Assessing the risks of these pipelines is challenging despite the evolution of computational fuzzy inference systems (FIS). The computational intricacy increases with the dimensions of the system variables especially in the typical Takagi–Sugeno (T–S) fuzzy-model. Typically, the number of rules rises exponentially as the number of system variables increases and hence, it is unfeasible to specify the rules entirely for pipeline risk assessments. This work proposes the significance of indexing pipeline risk assessment approach that is integrated with subtractive clustering fuzzy logic to address the uncertainty of the real-world circumstances. Hypothetical data is used to setup the subtractive clustering fuzzy-model using the fundamental rules and scores of the pipeline risk assessment indexing method. An interstate crude-oil pipeline in Egypt is used as a case study to demonstrate the proposed approach.

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Identification of critical operational hazards in a biogas upgrading pilot plant through a multi-criteria decision-making and FTOPSIS-HAZOP approach

et al. 2022

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A fuzzy-HAZOP/ant colony system methodology to identify combined fire, explosion, and toxic release risk in the process industries

Cited by 14 publications

References 94 publications

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

Risk assessment of interstate pipelines using a fuzzy-clustering approach

Identification of critical operational hazards in a biogas upgrading pilot plant through a multi-criteria decision-making and FTOPSIS-HAZOP approach

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