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Atmosphere 2018, 9(4), 119; https://doi.org/10.3390/atmos9040119

Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm

1
College of System Engineering, National University of Defense Technology, 109 Deya Road, Changsha 410073, China
2
Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Building 28, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
3
The Naval 902 Factory, Shanghai 200083, China
*
Author to whom correspondence should be addressed.
Received: 9 January 2018 / Revised: 16 February 2018 / Accepted: 6 March 2018 / Published: 22 March 2018
(This article belongs to the Section Air Quality)
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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. View Full-Text
Keywords: source estimation; atmospheric dispersion model; artificial neural network; particle swarm optimization; simulated annealing algorithm source estimation; atmospheric dispersion model; artificial neural network; particle swarm optimization; simulated annealing algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, R.; Chen, B.; Qiu, S.; Ma, L.; Zhu, Z.; Wang, Y.; Qiu, X. Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm. Atmosphere 2018, 9, 119.

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