Enhancing safety and risk management in biodiesel production: a machine learning approach using k-means clustering to address operational challenges and standardization issues

• Bow Tie analysis identifies and manages risks in industrial biodiesel production, enhancing safety and sustainability. • Key hazards include chemical spills, equipment failure, fire disasters, and occupational health issues. • Preventive measures like equipment maintenance, proper training, and safety protocols are crucial. • Mitigative measures include emergency response plans, containment systems, and fire suppression systems. Industrial-scale biodiesel production plays a critical role in advancing sustainable energy but involves significant operational risks due to complex processes and hazardous materials. Traditional risk assessments often fall short in addressing these specific challenges comprehensively. The study conducted a detailed risk assessment for industrial-scale biodiesel production using Bow Tie analysis to identify hazards, causal factors, and mitigation strategies. Bow Tie analysis effectively mapped top events such as equipment failure and fire incidents, tracing their root causes and potential consequences across key stages like feedstock handling and chemical processing. Integration of preventive and mitigated barriers provided a clear framework for risk containment. The priority weightage analysis identified uncertainty Level (0.5) as the most variable factor, followed by Risk Score (0.25), Incident Probability (0.16), and Safety Alert Frequency (0.09). Correlation analysis shows strong negative correlations between safety outcomes and factors like process automation ( r = -0.98658), regulatory compliance ( r = -0.95542), and safety policy implementation ( r = -0.97946). Dataset 38 shows optimal safety performance with the highest automation level (84), top compliance (0.93), lowest risk score (13), low uncertainty (5 %), and minimal incident probability (0.02), making it the most efficient and secure configuration. This integrated approach supports the sustainable growth and resilience of biodiesel production facilities.

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Publication Details

Journal
Results in Engineering
Published
2025-05-01
DOI
https://doi.org/10.1016/j.rineng.2025.105175
Citations
9
Primary Topic
Biodiesel Production and Applications
Type
article
Field-Weighted Citation Impact
6.07

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article

Enhancing safety and risk management in biodiesel production: a machine learning approach using k-means clustering to address operational challenges and standardization issues

Osama Khan, Ibrahim Alsaduni, Faisal Khan, Mohd Parvez
9 citations
Results in Engineering
Biodiesel Production and Applications
6.07
article

Enhancing safety and risk management in biodiesel production: a machine learning approach using k-means clustering to address operational challenges and standardization issues

Osama Khan, Ibrahim Alsaduni, Faisal Khan, Mohd Parvez
article en
9 citations

Abstract

• Bow Tie analysis identifies and manages risks in industrial biodiesel production, enhancing safety and sustainability. • Key hazards include chemical spills, equipment failure, fire disasters, and occupational health issues. • Preventive measures like equipment maintenance, proper training, and safety protocols are crucial. • Mitigative measures include emergency response plans, containment systems, and fire suppression systems. Industrial-scale biodiesel production plays a critical role in advancing sustainable energy but involves significant operational risks due to complex processes and hazardous materials. Traditional risk assessments often fall short in addressing these specific challenges comprehensively. The study conducted a detailed risk assessment for industrial-scale biodiesel production using Bow Tie analysis to identify hazards, causal factors, and mitigation strategies. Bow Tie analysis effectively mapped top events such as equipment failure and fire incidents, tracing their root causes and potential consequences across key stages like feedstock handling and chemical processing. Integration of preventive and mitigated barriers provided a clear framework for risk containment. The priority weightage analysis identified uncertainty Level (0.5) as the most variable factor, followed by Risk Score (0.25), Incident Probability (0.16), and Safety Alert Frequency (0.09). Correlation analysis shows strong negative correlations between safety outcomes and factors like process automation ( r = -0.98658), regulatory compliance ( r = -0.95542), and safety policy implementation ( r = -0.97946). Dataset 38 shows optimal safety performance with the highest automation level (84), top compliance (0.93), lowest risk score (13), low uncertainty (5 %), and minimal incident probability (0.02), making it the most efficient and secure configuration. This integrated approach supports the sustainable growth and resilience of biodiesel production facilities.

Results in EngineeringVol. 26
Majmaah University (SA), Jamia Millia Islamia (IN)
Majmaah University
Openalex Percentile: Top 4%
Biodiesel Production and Applications
6.07
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