A proactive approach to quantitative assessment of disruption risks of petroleum refinery operation

Ishola, Ademola; Matellini, Dante Benjamin; Wang, Jin

doi:10.1016/j.ssci.2020.104666

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…, 2019). Several studies have indicated that the lack of data availability is a challenge when attempting to explore the underlying interdependencies between the factors attributed to accident causation in the petroleum industry (Ishola et al. , 2020; Gangadhari et al.…”

Section: Systematic Review Of Barriers To ML Implementationmentioning

confidence: 99%

“…Further, the reliance The Indian petroleum industry on traditional accident analysis techniques is vulnerable to human bias and human error, due to time constraints and not reporting minor incidents (Rahman et al, 2020;Janssen et al, 2017;Hovden et al, 2010), resulting in the loss of critical information (Ahmed et al, 2019). Several studies have indicated that the lack of data availability is a challenge when attempting to explore the underlying interdependencies between the factors attributed to accident causation in the petroleum industry (Ishola et al, 2020;Gangadhari et al, 2021).…”

Section: Data Collection and Inconsistent Data Formatsmentioning

confidence: 99%

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Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Gangadhari

Khanzode

Murthy

et al. 2022

BIJ

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PurposeThis paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.Design/methodology/approachThe preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.FindingsThe findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.Originality/valueThis is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.

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Section: Systematic Review Of Barriers To ML Implementationmentioning

confidence: 99%

Section: Data Collection and Inconsistent Data Formatsmentioning

confidence: 99%

Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Gangadhari

Khanzode

Murthy

et al. 2022

BIJ

View full text Add to dashboard Cite

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“…Very recently, Jaderi et al 30 applied a traditional RBM and fuzzy RBM method for evaluating failures of facilities in petrochemical plants with consideration of operational impact, operational flexibility, maintenance cost, and impact on safety and environment. Ishola et al 31 proposed a robust fuzzy linguistic assessment methodology to identify and assess risk elements in refinery process units. Although many RBM methods have been developed and applied in different industries, they are generally probability or heuristic based and applied to single facility or small-scale systems.…”

Section: Introductionmentioning

confidence: 99%

“…Very recently, Jaderi et al applied a traditional RBM and fuzzy RBM method for evaluating failures of facilities in petrochemical plants with consideration of operational impact, operational flexibility, maintenance cost, and impact on safety and environment. Ishola et al . proposed a robust fuzzy linguistic assessment methodology to identify and assess risk elements in refinery process units.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Production and Maintenance Scheduling for Refinery Front-End Process with Considerations of Risk Management and Resource Availability

Wang

2022

Ind. Eng. Chem. Res.

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Effective unit maintenance is critically important for the front-end crude transfer process (FCTP) to ensure production reliability and safe operations in refineries. It should be prioritized based on risk ratings of all maintenance tasks and constrained by the availability of various maintenance resources, such as budget, manpower, equipment, and supplies, as well as having minimal impact on the normal production of refineries. In this paper, a new methodology for the simultaneous production and maintenance scheduling for refinery FCTP systems has been developed. It can generate optimal schedules for both crude movement and unit maintenance plans with considerations of dynamic risk management and resource availability constraints. As such, a new multiobjective and mixed-integer nonlinear programming (M-MINLP) model has been developed to minimize the total FCTP operating cost, as well as to minimize the total risk from all maintenance tasks. The scheduling model can handle multiunit maintenance assignments with optimal determination of when to isolate a unit to perform its maintenance and how to resume its service after maintenance under the satisfaction of all strict constraints for crude storage, blending and movements, unit maintenance requirements, and resource limitations. The efficacy of the development has been demonstrated by multiple large-scale case studies.

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Modelling of supply chain disruption analytics using an integrated approach: An emerging economy example

Ali

Paul

Chowdhury

et al. 2021

Expert Systems with Applications

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A proactive approach to quantitative assessment of disruption risks of petroleum refinery operation

Cited by 5 publications

References 38 publications

Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Simultaneous Production and Maintenance Scheduling for Refinery Front-End Process with Considerations of Risk Management and Resource Availability

Modelling of supply chain disruption analytics using an integrated approach: An emerging economy example

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