Statistical analysis of emergency response optimization for hazardous chemical processes in industrial facilities
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Babu Rao C. Gnanasekara
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Rajesh Pachimatla
Abstract
This research explored the state of emergency preparedness at high-risk chemical manufacturing facilities across Tamil Nadu, India. Data were gathered through on-site inspections and collaboration with plant operators, focusing on responses to 100 key questions about readiness protocols. Using advanced statistical software, we analyzed the binary responses to evaluate the effectiveness of current emergency measures and identify critical shortcomings. The investigation emphasized several vital aspects, including the robustness of fire protection systems, funding for safety initiatives, uninterrupted power for communication networks, regular equipment testing, sound system clarity, certification compliance, hazard identification (HAZID) evaluations, fire load assessments, backup assembly areas, and reliable fire door operations. Regulatory oversight highlighted the importance of maintaining certified emergency protocols, ensuring ambulance availability, and establishing workplace health facilities. Our statistical findings revealed significant deficiencies in preparedness within Tamil Nadu’s chemical industry, offering actionable insights for improvement. With a confidence interval supporting broader applicability, the study underscores the urgent need for enhanced emergency planning to elevate safety performance across the region’s chemical sector.
Acknowledgments
No external funding.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None Declared.
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Conflict of interest: All other authors state no conflict of interest.
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Research funding: No funding.
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Data availability: Not applicable.
References
1. Bellasio, R, Bianconi, R. Online simulation system for industrial accidents. Environ Model Softw 2005;20:329–42. https://doi.org/10.1016/j.envsoft.2003.12.011.Search in Google Scholar
2. Amin, MT, Khan, F, Amyotte, P. A bibliometric review of process safety and risk analysis. Process Saf Environ Prot 2019;126:366–81. https://doi.org/10.1016/j.psep.2019.04.015.Search in Google Scholar
3. Bragatto, PA, Ansaldi, SM, Agnello, P. Small enterprises and major hazards: how to develop an appropriate safety management system. J Loss Prev Process Ind 2015;33:232–44. https://doi.org/10.1016/j.jlp.2014.12.016.Search in Google Scholar
4. Abdolhamidzadeh, B, Bab, V, Rashtchian, D, Reniers, G. Fire dynamics simulation of multiple ethanol pool fires. Res J Chem Environ 2013;17:3–9. Available from: https://repository.uantwerpen.be/desktop/irua.Search in Google Scholar
5. Lim, HY, Um, KS, Jung, SH. A study on effective mitigation system for accidental toxic gas releases. J Loss Prev Process Ind 2017;49:636–44. https://doi.org/10.1016/j.jlp.2017.05.017.Search in Google Scholar
6. Ogle, R, Ramirez, JC, Hetrick, TM. Domino effect in a catastrophic solid oxidizer fire. Process Saf Prog 2014;34:167–71. https://doi.org/10.1002/prs.11701.Search in Google Scholar
7. Li, G, Yang, HX, Yuan, CM, Eckhoff, R. A catastrophic aluminium-alloy dust explosion in China. J Loss Prev Process Ind 2016;39:121–30. https://doi.org/10.1016/j.jlp.2015.11.013.Search in Google Scholar
8. Lina, CP, Chang, HK, Chang, YM, Chen, SW, Shu, CM. Emergency response study for chemical releases in the high-tech industry in Taiwan–A semiconductor plant example. Process Saf Environ Prot 2009;87:353–60. https://doi.org/10.1016/j.psep.2009.07.005.Search in Google Scholar
9. Jo, YD, Park, KD. Emergency response plane of toxic gas releases with considering ventilation ratio and meteorological conditions. Int J Saf Secur Eng 2016;6:229–37. Available from: https://www.witpress.com/elibrary/sse-volumes/6/2/1166.10.2495/SAFE-V6-N2-229-237Search in Google Scholar
10. Baybutt, P. The treatment of domino effects in process hazard analysis. Process Saf Prog 2014;34:220–7. https://doi.org/10.1002/prs.11687.Search in Google Scholar
11. Lees, FP. Loss prevention in the process industries: hazard identification, assessment and control, 2nd ed. Oxford: Butterworth Heinemann; 1996. Available from: https://www.sciencedirect.com/book/9780750675550/lees-loss-prevention-in-the-process-industries.Search in Google Scholar
12. Shen, Y, Wang, Q, Yan, W, Sun, IH. An evacuation model coupling with toxic effect for chemical industrial park. J Loss Prev Process Ind 2015;33:258–65. https://doi.org/10.1016/j.jlp.2015.01.002.Search in Google Scholar
13. Cozzani, V, Bandini, R, Basta, C, Christou, MD. Application of land-use planning criteria for the control of major accident hazards: a case-study. J Hazard Mater 2006;136:170–80. https://doi.org/10.1016/j.jhazmat.2005.12.031.Search in Google Scholar PubMed
14. Yu, Q, Zhang, Y, Wang, X, Ma, WC, Chen, LM. Safety distance assessment of industrial toxic releases based on frequency and consequence: a case study in Shanghai, China. J Hazard Mater 2009;168:955–61. https://doi.org/10.1016/j.jhazmat.2009.02.123.Search in Google Scholar PubMed
15. Murphy, JF. Emergency management: it starts with a question in the design stages “what can go wrong? Process Saf Prog 2017;36:325. https://doi.org/10.1002/prs.11938.Search in Google Scholar
16. Fu, G, Fan, Y, Tong, R, Gong, Y. A universal methodology for the causation analysis of accidents. J Accid Prev 2016;2:7–12. Available from: http://www.safetysciencepublishing.org/home/PaperDtl.aspx?paperID=189.Search in Google Scholar
17. Leveson, NG. Applying systems thinking to analyze and learn from events. Saf Sci 2011;49:55–64. https://doi.org/10.1016/j.ssci.2009.12.021.Search in Google Scholar
18. The factories act, 1948 [Online]. Available from: https://labour.gov.in/sites/default/files/TheFactoriesAct1948.pdf.Search in Google Scholar
19. Hazardous chemical rules [Online]. Available from: https://dish.tn.gov.in/assets/pdf/HAZARDOUS%20CHEMICAL%20RULES.pdf.Search in Google Scholar
20. Risk assessment [Online]. Available from: http://environmentclearance.nic.in/writereaddata/online/RiskAssessment/300620160QSAUYNJRiskAssessment.pdf.Search in Google Scholar
21. Baburao, G, Srinivas, T, Anitha, A, Elangovan, RK, Govindarajan, R. Statistical data analysis in emergency management elements of Indian state of Tamil Nadu manufacturing industries utilising LPG. Int J Occup Saf Health 2024;14:88–97. https://doi.org/10.3126/ijosh.v14i1.48606.Search in Google Scholar
22. Mohamad, MM, Sulaiman, NL, Sern, LC, Salleh, KM. Measuring the validity and reliability of research instruments. Proced Soc Behav Sci 2015;204:164–71. https://doi.org/10.1016/j.sbspro.2015.08.129.Search in Google Scholar
23. Lin, SC, Mufidah, I, Persada, SF. Safety-culture exploration in Taiwan’s metal factories: identifying the workers’ background influence on safety climate. Sustainability 2017;9:1965. https://doi.org/10.3390/su9111965.Search in Google Scholar
24. Balanda, KP, MacGillivray, HL. Kurtosis: a critical review. Am Stat 1988;42:111–9. https://doi.org/10.2307/2684482.Search in Google Scholar
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