Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67950
Volume:
10 |
10 |
Oct 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
FIRE TECHNOLOGY & SAFETY ENGINEERING
VIKRANT UNIVERSITY, GWALIOR
FIRE TECHNOLOGY & SAFETY ENGINEERING
VIKRANT UNIVERSITY, GWALIOR
Industrial operations involving hazardous materials, complex machinery, high temperatures, pressurised systems, and flammable substances present significant risks to workers, equipment, the environment, and surrounding communities. Conventional risk assessment approaches often rely on qualitative risk matrices, individual hazard analysis techniques, and historical accident data. Although these methods are valuable, their effectiveness may be limited when multiple hazards interact, operational conditions change, or safety resources must be allocated under financial and technical constraints. This paper proposes an integrated risk assessment and optimisation framework for industrial safety that combines systematic hazard identification, quantitative risk assessment, probabilistic modelling, consequence analysis, and multi-objective optimisation. The framework incorporates hazard identification techniques, including Hazard and Operability Studies (HAZOP), Failure Mode and Effects Analysis (FMEA), and Fault Tree Analysis (FTA), alongside likelihood–severity evaluation and computational decision-support methods. A mathematical optimisation model is proposed to prioritise safety interventions while considering risk reduction, implementation costs, and operational constraints. Python-based computational analysis can support data processing, uncertainty evaluation, risk ranking, and optimisation. The proposed framework is intended to improve the consistency, transparency, and efficiency of industrial safety decisions. Its practical effectiveness should be established through case-study implementation, sensitivity analysis, and comparison with existing risk assessment practices.
KEYWORDS
This paper proposes an integrated risk assessment and optimisation framework for industrial safety that connects hazard identification, scenario development, quantitative risk estimation, uncertainty analysis, and the selection of preventive interventions. The framework combines established methods such as HAZOP, FMEA, and FTA with mathematical risk models and constrained optimisation. Its central principle is that risk assessment should not end with the identification or ranking of hazards; it should provide a transparent basis for selecting and evaluating appropriate risk-control measures. The proposed approach may improve the consistency of hazard prioritisation, support evidence-based safety investment, and make uncertainty more visible in industrial decision-making. However, these benefits remain to be demonstrated through case-study implementation, data-based validation, and comparison with existing practices. The framework offers a foundation for further research in computational industrial safety, quantitative risk assessment, and safety intervention optimisation. Its practical value will ultimately depend on the quality of the underlying data, the validity of its assumptions, and the effectiveness of the controls implemented in the workplace.
1. International Electrotechnical Commission. (2019). IEC 31010:2019 Risk management — Risk assessment techniques. IEC. https://www.iso.org/standard/72140.html 2. Occupational Safety and Health Administration. Process Safety Management. United States Department of Labor. https://www.osha.gov/process-safety-management 3. Occupational Safety and Health Administration. Process Safety Management — Hazard Recognition. United States Department of Labor. https://www.osha.gov/process-safety-management/hazards 4. Occupational Safety and Health Administration. Safety Management — Hazard Identification and Assessment. United States Department of Labor. https://www.osha.gov/safety-management/hazard-identification 5. International Organization for Standardization. (2018). ISO 31000:2018 Risk management — Guidelines. ISO. https://www.iso.org/standard/65694.html
Publication Date
10/14/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
2
Total views since publication
PDF Downloads
7
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...