(54aq) Application of Group Intelligent Optimization Algorithm in Gas Emission Source Identification

Dangerous gas emission may result in serious ecological, environmental, social and human life safety risks, thus gas emission source term identification is crucial for safety management. Based on the experiment data from static sensor distribution, the group intelligent optimization (GIO) algorithms including particle swarm optimization(PSO),ant colony optimization algorithm (ACO) and the firefly algorithm (FA), are compared to identify the parameters of the gas emission source, such as source strength and location parameters. The results showed that the FA has the best performance in terms of source parameter estimation, while the PSO and ACO algorithm are superior to the convergence rate, calculation efficiency, dependence on the initial value and boundary constraints. Finally, the GIO algorithm coupled with correlated matching of concentration distribution (CMCD) method is applied to the source term identification with mobile sensor based on a simulation scenario. The results indicated that it is a useful method to identify source term with high accuracy by GIO-CMCD method in the case of mobile sensor. The researches discussed in this paper can be applied to the management and assessment of the leak risk for the storage and transportation process of the dangerous gas or volatile materials.