Chlorine Safety in Water Treatment: A Study of Causes and Preventive Measures Through Fault Tree and Event Tree Analysis
Bibliographic record
Abstract
Accidents in process industries, especially involving hazardous substances like chlorine, pose significant risks to humans, the environment, and property.This paper examines chlorine-related incidents in water treatment plants (WTPs), where chlorine gas is used for disinfection.Typically, WTPs store over 20 chlorine drums, each containing around 930 kg, increasing inherent risks due to chlorine's reactivity and health consequences upon exposure.Incidents such as the Bhopal 2022 case, which hospitalized 15 people, and the Kota Belud 2017 case highlight the importance of safe operation.This study aims to identify the root causes of chlorine-related incidents, assess their impacts, and recommend effective preventive measures to enhance safety.Using Fault Tree Analysis (FTA) to identify root causes and Event Tree Analysis (ETA) to evaluate preventive measures.Major failure causes included equipment damage, corrosion, non-compliance with design specifications, and mishandling.Preventive measures like leak detectors, scrubber systems, and Emergency Shutdown Systems (ESD) significantly reduce risks.If scrubbers fail but ESD works with trained personnel, or if scrubbers work but ESD fails without personnel, the impact remains medium.However, chlorine releases become catastrophic when all measures fail or only leak detectors work without effective mitigation systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".