Identifying the locations of atmospheric pollution point source by using a hybrid particle swarm optimization

Abstract

This research aims to improve the particle swarm optimization (PSO) algorithm by combining a multidimensional search with a line search to determine the location of the air pollution point sources and their respective emission rates. Both multidimensional search and line search do not require the derivative of the cost function. By exploring a symmetric property of search domain, this innovative search tool incorporating a multidimensional search and line search in the PSO is referred to as the hybrid PSO (HPSO). Measuring the pollutant concentration emanating from the pollution point sources through the aid of sensors represents the first stage in the process of evaluating the efficiency of HPSO. The summation of the square of the differences between the observed concentration and the concentration that is theoretically expected (inverse Gaussian plume model or numerical estimations) is used as a cost function. All experiments in this research are therefore conducted using the HPSO sensing technique. To effectively identify air pollution point sources as well as calculate emission rates, optimum positioning of sensors must also be determined. Moreover, the frame of discussion of this research also involves a detailed comparison of the results obtained by the PSO algorithm, the GA (genetic algorithm) and the HPSO algorithm in terms of single pollutant location detection, respectively. In the case of multiple sources, only the findings based on PSO and HPSO algorithms are taken into consideration. This research eventually verifies and confirms that the HPSO does offer substantially better performance in the measuring of pollutant locations as well as emission rates of the air pollution point sources than the original PSO.