Edge Computing-Enabled Real-Time Leak Detection System for Hazardous Chemical Pipeline Networks

Abstract

The transportation of hazardous chemicals through industrial pipeline networks presents significant safety and environmental risks due to potential leakage incidents, equipment failures, and delayed response mechanisms. This study proposes an edge computing-enabled real-time leak detection system for hazardous chemical pipeline networks to enhance operational safety, monitoring accuracy, and rapid incident response capability. The developed framework integrates distributed sensor networks, edge computing architecture, and intelligent anomaly detection algorithms to enable localized real-time data processing and continuous pipeline condition assessment. Key operational parameters, including pressure variation, flow rate imbalance, temperature fluctuation, acoustic signals, and chemical concentration levels, were continuously monitored using strategically deployed sensing units along the pipeline infrastructure. Edge computing nodes were utilized to process sensor data near the source, minimizing communication latency and enabling rapid detection of abnormal operating conditions without reliance on centralized cloud processing. Machine learning-based analytical models were incorporated to improve leak identification accuracy, distinguish false alarms, and predict leak severity under varying operational scenarios. Experimental and simulation analyses demonstrated that the proposed system significantly reduced leak detection time and improved localization accuracy compared to conventional centralized monitoring approaches. Furthermore, the edge-enabled architecture enhanced network reliability, reduced bandwidth utilization, and improved cybersecurity resilience within industrial pipeline monitoring systems. Comparative evaluation revealed substantial improvements in operational responsiveness, environmental risk mitigation, and preventive maintenance capability through intelligent real-time monitoring and decentralized decision-making.