Risk Mapping of Explosion Zones in Oil and Gas Refineries: A Safety Perspective

Abstract— Emergency withdrawal plans are important in reality mapping of industrial threat areas and reducing the effects of disasters in high -risk areas. The project focuses on oil and gas refinery facilities across India, which suffers from incidents such as chemical leaks, fire and explosion. The proposed web application, ignition watch, integrates geophagical devices such as Leaflet.JS to imagine the risk areas through an interactive map. Live meteorological data - including air speed and UV radiation - is obtained through public API to assess environmental conditions affecting the spread of danger. The system calculates three concentrated danger areas (high, medium, low risk) with dynamic ready, which provides real -time estimates of potential danger areas. The smallest path for clearance is determined using the Dijkstra’s algorithm, which guides the populated population in the nearest predetermined safe areas. The system identifies and displays the nearest fire station or emergency response unit for each refinery. A search feature allows users to detect specific refineries and look at live danger updates and customized escape routes. Unlike traditional systems relying on static predictions, this device optimized for real-time environmental changes, providing accurate, location-specific statusable awareness. It aims to support public to improve preparations for emergency planners, first respondents and public, reduce response time and reduce the loss of life and infrastructure.

Disaster Management, Threat Zone Mapping, Dijkstra’s Algorithm, Leaflet.JS, Oil and Gas Refineries, Real-Time GIS,, Public Safety.

1. M. M. Yari and M. B. Shafii, “Use of GIS for Industrial Hazard Mapping: A Case Study,” Int. J. Environ. Sci. Technol., vol. 15, no. 4, pp. 789–798, 2018. [Google Scholar] [Crossref]

2. S. Gupta and R. Patel, “Real-Time Meteorological Data Integration for Risk Analysis in Oil Refineries,” IEEE Trans. Ind. Inform., vol. 17, no. 6, pp. 4009–4017, 2021. [Google Scholar] [Crossref]

3. K. Elabbasy and H. Hosny, “Blast Impact Simulation and Zoning Around Oil Storage Units,” J. Loss Prev. Process Ind., vol. 62, pp. 103943, 2019. [Google Scholar] [Crossref]

4. D. N. Samarasinghe and L. Silva, “Web-Based Disaster Visualization Using Open-Source GIS Tools,” in Proc. Int. Conf. Geoinformatics, Colombo, Sri Lanka, 2020, pp. 155–160. [Google Scholar] [Crossref]

5. J. R. Fernandez and M. Ghose, “Optimized Evacuation Pathways using Dijkstra’s Algorithm for Urban Hazards,” in Proc. IEEE Int. Conf. Smart City, 2021, pp. 98–104. [Google Scholar] [Crossref]

6. R. Sharma and A. Malik, “An IoT Framework for LPG Gas Leakage Detection in Industrial Environments,” IEEE Sens. J., vol. 21, no. 20, pp. 23167–23174, 2021. [Google Scholar] [Crossref]

7. L. Zeng and X. Yuan, “Machine Learning-Based Hazard Prediction in Petrochemical Plants,” IEEE Access, vol. 9, pp. 100921–100930, 2021. [Google Scholar] [Crossref]

8. M. Krishnamurthy and P. V. Rao, “Optimizing Emergency Response Based on Fire Station Proximity Using GIS,” Int. J. Disaster Risk Reduct., vol. 43, pp. 101389, 2020. [Google Scholar] [Crossref]

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