Sensor Layout Optimization and Natural Gas Leakage Source Term Estimation Based on Non-Dominated Sorting Genetic Algorithm

For gas leakage monitoring in obstacle environments such as oil and gas stations, the layout of fixed sensors directly affects the validity of monitoring data and the accuracy of subsequent leakage source localization. To achieve effective coverage of high-risk areas with a limited number of sensors and reduce deployment costs, this paper proposes a multi-objective optimization method for sensor layout based on the non-dominated sorting genetic algorithm-II (NSGA-II). Based on multi-scenario computational fluid dynamics simulation data, the peak concentration, hazardous concentration duration, and leakage probability at each monitoring point are extracted as risk characteristic indicators. The NSGA-II analytic hierarchy process is employed to determine the weight of each indicator, and a comprehensive risk classification model for the monitored area is established. This is adopted for solution seeking. Through non-dominated sorting and crowding distance calculation, the Pareto optimal front is searched in the solution space. The optimized layout scheme is applied to the leakage source term estimation based on particle filter, and the performance of different layout schemes is compared and analyzed with the source localization error as the evaluation index. Case studies show that the sensor layout optimized by the non-dominated sorting genetic algorithm achieves effective coverage of high-risk areas. With the same number of sensors, its high-risk area coverage rate outperforms that of the multi-objective particle swarm optimization algorithm (MPSOA). Following the application of the optimized layout, the localization accuracy of leakage source term estimation is significantly improved. Compared with the traditional grid and circular layouts, the source localization error is reduced by approximately 44%. Compared with the layouts optimized by the MPSOA and genetic algorithm (GA), the error is decreased by 30.4% and 33.3%, respectively. The proposed sensor layout optimization method based on the NSGA-II can effectively balance monitoring coverage and economic cost, and significantly improve the localization accuracy of gas leakage sources. This study provides a theoretical basis and technical support for the optimal deployment of fixed gas sensor networks in complex scenarios.