Biogas plants accidents: Analyzing occurrence, severity, and associations between 1990 and 2023
Bibliographic record
Abstract
Biogas plants numbers are increasing worldwide, but their safety record is rarely investigated. This paper analyzes 75 occurrences of various types of accidents in biogas plants worldwide between 1990 and 2023. The study comprehensively reviewed accident reports and research literature with input from plant operators and safety experts. We aim to identify the common causes and consequences of accidents (occurrences) and suggest preventive measures to improve safety. The occurrences’ primary causes were component failure > maintenance error > natural and technological disasters (NaTech) > equipment failure > operational error > no personal protective equipment (PPE). The most common occurrences were gas explosions 69.3%, toxic gas releases (biohazard) 21.3%, asphyxia (biohazard) 4%, malfunctioning (electric and mechanical hazard) 2.7%, and fires 2.7%. The accident consequences ranged from minor injuries (76) to fatalities (51) and extensive property damage. Lack of PPE and gas pipelines (mechanical and biohazards) correlated positively and significantly (R2 = 0.70), while operational errors and asphyxia (biohazard) scenarios correlated positively and moderately (R2 = 0.55). The plant design, operating procedures, and maintenance practices strongly influence the occurrences’ likelihood and severity. This study provides valuable insights for stakeholders, researchers, and policymakers interested in promoting biogas’ safe and sustainable development. Future studies should investigate the relationship between plant size and accident frequency and assess the effectiveness of safety management and risk assessment methodologies in mitigating such occurrences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".