A hybrid multi-criteria decision-making approach for analysing operational hazards in Heavy Fuel Oil-based power plants
Bibliographic record
Abstract
Hazard identification and prioritisation practices are very important for power plants to continue their operations without disruption. Systematic operational hazard analysis is not a very common practice at the Heavy Fuel Oil (HFO) based power plants in Bangladesh. Hence, a structured hazard evaluation framework can greatly benefit them to ensure their operational safety. This study has been conducted to identify and prioritise the operational hazards of the HFO-based power plants through using a hybrid multi-criteria decision-making (MCDM) approach in a fuzzy environment, and then, to explore the appropriate mitigation methods for the top-ranked hazards and to find the interrelationships that exist among the mitigation methods. First, the most common hazards in HFO-based power plants have been identified from the expert feedbacks. Then, a fuzzy analytical hierarchy process (FAHP) method has been used to determine the weights of the evaluation criteria and a fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) method, has been used for the final ranking of the potential hazards. Afterwards, mitigation methods for the top 25 hazards have been identified and interrelationship among those mitigation methods has been explored through using interpretive structural modelling (ISM) and a matriced impacts croisés multiplication appliquée à un classement (MICMAC) analysis. The study finds that 'explosion of high-pressure steam drum of the gas boiler', 'crankcase explosion and fire hazard due to oil pressure rise' and 'explosion of the compressed air reservoir' are the top three hazards in the hazard ranking. 'Standard operating procedure (SOP) and training' have been found to be the most driving mitigation methods for the top-ranked hazards based on the ISM-MICMAC analysis. The findings of this study are expected to provide the managers of power plants with valuable insights, which can help them to prepare sustainable operational strategies to ensure the least hazardous work environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".