Abstract:

To qucikly obtain the source strength and location information from hazardous gas leakage incidents, a multi-factor optimized Gaussian plume dispersion model was proposed, and the leakage source parameters were inverted by combining it with a CAFA. Key environmental factors such as wind speed distribution, surface resistance, and surface reflection were incorporated into the Gaussian plume model to enhance its fitting capability under complex conditions through multi-factor calibration. Furthermore, a chaotic mapping was introduced to improve the population diversity and global search ability of firefly algorithm(FA), thereby achieving an effective balance between global optimization and local refinement while reducing the risk of falling into local optima. The results indicate that, after optimization based on wind speed distribution, surface resistance, and surface reflection, the error of the Gaussian plume model is reduced by 16%. CAFA effectively can avoid falling into local optima, reducing the source strength inversion error from 63.56% to 0.22%, and the leak source coordinate inversion error from 1.5 m to 0.2 m.

Key words: chaos-mapped adaptive firefly algorithm(CAFA), Gaussian plume model, hazardous gas leakage, source term inversion, chemical industrial park