Risk Forecasting Modeling for Accidental Events in Domestic Gas Distribution Networks for Indian Metropolitan Cities

Abstract

The Natural gas distribution networks are a fundamental component of the energy newlineinfrastructure of Indian metropolitan cities, safeguarding gas to households, industries, newlineand commercial establishments. However, despite stringent safety protocols these newlinenetworks remain vulnerable to risk such as gas leaks, fires and explosions This thesis newlinepresents a comprehensive Risk Forecasting Model directed at predicting and mitigating newlinerisks within City Gas Distribution networks across Indian cities. The model controls an newlineintegrated approach combining Hazard and Operability Study (HAZOP), Quantitative newlineRisk Assessment (QRA), and advanced tools like ALOHA and PHAST to newlinesystematically assess possible hazards, including fires, explosions, and gas leaks. By newlineanalyzing historical accident data, operational records, and environmental factors, the newlinestudy associates with critical risks, such as equipment faults and exterior interruptions. newlineThe findings from the QRA emphasize persistent challenges in mitigating risks, newlineparticularly in densely populated urban areas highlighting the need for targeted newlineapproaches to enhance the safety and consistency of these essential networks. With newlineminimal risks as low as 1 × 10and#8315;and#8313; annually and maximum risks at 1 × 10and#8315;³ annually, the newlineintegrated hazards are within the ALARP range. In locations where risks surpass newlinereasonable boundaries, further precautions are advised. Case studies from places like newlineSurat, Andheri, Mumbai, and Paschim Vihar, Delhi, highlight the significance of newlineemergency preparedness and strict installation requirements. For instance, a quotJet firequot newlinescenario resulting from a 0.025mtr. gas leak in Paschim Vihar showed possible damage newlineup to 40 meters, highlighting the necessity of preventative safety measures. This newlineresearch is unusual since it combines real-time monitoring systems with a variety of newlinerisk assessment approaches. In contrast to earlier research, this model relates to newlinestatistical analysis, real-world case studies, and simulations to provide a dynamic and newlinexxi newlinepredictive approach to risk manag