Print Email Facebook Twitter Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm Title Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm Author Wang, Rongxiao (National University of Defense Technology) Chen, B. (National University of Defense Technology) Qiu, S. (TU Delft Web Information Systems; National University of Defense Technology) Ma, Liang (National University of Defense Technology) Zhu, Zhengqiu (National University of Defense Technology) Wang, Yiping (Naval 902 Factory) Qiu, Xiaogang (National University of Defense Technology) Date 2018 Abstract Locating and quantifying the emission source plays a significant role in the emergency management of hazardous gas leak accidents. Due to the lack of a desirable atmospheric dispersion model, current source estimation algorithms cannot meet the requirements of both accuracy and efficiency. In addition, the original optimization algorithm can hardly estimate the source accurately, because of the difficulty in balancing the local searching with the global searching. To deal with these problems, in this paper, a source estimation method is proposed using an artificial neural network (ANN), particle swarm optimization (PSO), and a simulated annealing algorithm (SA). This novel method uses numerous pre-determined scenarios to train the ANN, so that the ANN can predict dispersion accurately and efficiently. Further, the SA is applied in the PSO to improve the global searching ability. The proposed method is firstly tested by a numerical case study based on process hazard analysis software (PHAST), with analysis of receptor configuration and measurement noise. Then, the Indianapolis field case study is applied to verify the effectiveness of the proposed method in practice. Results demonstrate that the hybrid SAPSO algorithm coupled with the ANN prediction model has better performances than conventional methods in both numerical and field cases. Subject Artificial neural networkAtmospheric dispersion modelParticle swarm optimizationSimulated annealing algorithmSource estimation To reference this document use: http://resolver.tudelft.nl/uuid:a7a44200-864d-4d88-bb04-0a4224f9271e DOI https://doi.org/10.3390/atmos9040119 ISSN 2073-4433 Source Atmosphere, 9 (4) Part of collection Institutional Repository Document type journal article Rights © 2018 Rongxiao Wang, B. Chen, S. Qiu, Liang Ma, Zhengqiu Zhu, Yiping Wang, Xiaogang Qiu Files PDF 44902977_atmosphere_09_00119.pdf 3.03 MB Close viewer /islandora/object/uuid:a7a44200-864d-4d88-bb04-0a4224f9271e/datastream/OBJ/view