TY - JOUR
T1 - Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm
AU - Wang, Rongxiao
AU - Chen, Bin
AU - Qiu, Sihang
AU - Ma, Liang
AU - Zhu, Zhengqiu
AU - Wang, Yiping
AU - Qiu, Xiaogang
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Atmospheric dispersion model
KW - Particle swarm optimization
KW - Simulated annealing algorithm
KW - Source estimation
UR - http://www.scopus.com/inward/record.url?scp=85044428880&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:a7a44200-864d-4d88-bb04-0a4224f9271e
U2 - 10.3390/atmos9040119
DO - 10.3390/atmos9040119
M3 - Article
AN - SCOPUS:85044428880
SN - 2073-4433
VL - 9
JO - Atmosphere
JF - Atmosphere
IS - 4
M1 - 119
ER -