TY - JOUR
T1 - Combined Grey Wolf Optimizer Algorithm and Corrected Gaussian Diffusion Model in Source Term Estimation
AU - Liu, Yizhe
AU - Jiang, Yu
AU - Zhang, Xin
AU - Pan, Yong
AU - Qi, Yingquan
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/7
Y1 - 2022/7
N2 - It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine the appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and ant colony optimization (ACO) are selected to be applied in STE. After calculation, all four algorithms can obtain leak source parameters. Among them, GWO and GA have similar computational efficiency, while ACO is computationally inefficient. Compared with GWO, GA and PSO, ACO requires larger population and more iterations to ensure accuracy of source parameters. Most notably, the convergence factor of GWO is self-adaptive, which is in favor of obtaining accurate results with lower population and iterations. On this basis, combination of GWO and a modified Gaussian diffusion model with surface correction factor is used to estimate the emission source term in this work. The calculation results demonstrate that the corrected Gaussian plume model can improve the accuracy of STE, which is promising for application in emergency warning and safety monitoring.
AB - It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine the appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and ant colony optimization (ACO) are selected to be applied in STE. After calculation, all four algorithms can obtain leak source parameters. Among them, GWO and GA have similar computational efficiency, while ACO is computationally inefficient. Compared with GWO, GA and PSO, ACO requires larger population and more iterations to ensure accuracy of source parameters. Most notably, the convergence factor of GWO is self-adaptive, which is in favor of obtaining accurate results with lower population and iterations. On this basis, combination of GWO and a modified Gaussian diffusion model with surface correction factor is used to estimate the emission source term in this work. The calculation results demonstrate that the corrected Gaussian plume model can improve the accuracy of STE, which is promising for application in emergency warning and safety monitoring.
KW - Gaussian diffusion model
KW - Grey Wolf Optimizer (GWO)
KW - hazardous gases leakage
KW - source term estimation (STE)
KW - swarm intelligence optimization (SIO)
UR - http://www.scopus.com/inward/record.url?scp=85133019434&partnerID=8YFLogxK
U2 - 10.3390/pr10071238
DO - 10.3390/pr10071238
M3 - 文章
AN - SCOPUS:85133019434
SN - 2227-9717
VL - 10
JO - Processes
JF - Processes
IS - 7
M1 - 1238
ER -