Integrated Bayesian and Particle Swarm Approaches for Enhanced Gas Leak Detection in Complex Commercial Structures.

During gas leak detection and risk monitoring of commercial sealed areas, different types of sensors are deployed to monitor leak signals. The arrangement of a limited number of sensors in the most strategic positions, solving the problem of optimal sensor placement, is key to improving detection efficiency. Aiming at gas leak detection in commercial areas, this paper proposes a sensor placement methodology based on a particle swarm optimization algorithm to determine the optimal number and position of sensors. First, Bayesian networks assess the gas leak risk levels. Second, a discrete optimization model for sensor placement is established. Finally, the particle swarm optimization algorithm is applied to calculate the optimal sensor placement solution. In the iterative process, partial differential equation models simulate gas diffusion paths to verify the effectiveness of the sensor layout, followed by computational fluid dynamics simulations for further validation of the optimization results. The simulation case of a commercial gas system demonstrates that the proposed method achieves fast convergence and significant optimization results. A real case study shows that the method reduces the number of sensors and data redundancy. Compared with traditional methods, the robustness and efficiency of the system under the optimal solution are significantly improved.