Optimal Sensor Placement for Gas Leak Monitoring in Chemical Parks Using Graph Convolutional Networks and Evolutionary Multi-Objective Optimization

This study establishes a GCN–NSGA-III-based framework for determining gas-leak sensor locations. Candidate layouts are optimized simultaneously with respect to installation expenditure, spatial coverage, leak-identification performance, and time to alarm. In the proposed framework, the GCN extracts spatial correlations and leakage-risk features among candidate monitoring locations, whereas NSGA-III optimizes the network weights and thresholds to support the selection of improved sensor placement schemes. By combining the image-based spatial feature extraction capability of CNNs, the graph-structured feature learning capability of GCNs, and the multi-objective optimization strength of NSGA-III, the model achieves significant improvements in detection accuracy and risk assessment efficiency. The model was validated using a hybrid gas-leak dataset comprising 758 training samples, including 189 real-world monitoring samples and 569 simulated samples, and 229 test samples, including 73 real-world monitoring samples and 156 simulated samples. Its engineering applicability was further evaluated using a simulated chlorine leakage scenario at a chemical plant in Changzhou, China, with a leakage rate of 2 kg/s and an ambient easterly wind speed of 1 m/s. Experimental results confirm its reliability and practical applicability in real-world engineering contexts. Compared with the original pre-optimization sensor layout under the same leakage and environmental conditions, the proposed optimization strategy reduces deployment costs by 19%, increases monitoring coverage by 8.2%, improves detection accuracy by 14.1%, and shortens alarm response time by approximately 15%.