The sealing ring of the external floating roof tank is prone to petroleum gas leakage due to material aging and oil corrosion. Petroleum gas leakage and diffusion easily accumulate above the floating deck. When it is within the explosion limit range, there will be the risk of explosion and fire. To deal with the explosion accident of storage tank caused by the concentration distribution of petroleum gas leakage for the sealing ring, and to study the influence of petroleum gas diffusion and concentration distribution after sealing ring leakage on the control area above the floating deck in the tank farm environment, this paper established numerical models of sealing ring leakage under different liquid level heights for 10 × 104 m3 external floating roof tank. Through numerical calculation, it is found that the diffusion concentration of petroleum gas is related to the wind speed, the range of the control area above the floating deck, and leakage when sealing rings leak at different liquid levels. Through dimensionless analysis, the functional relationship of gas leakage diffusion concentration distribution under different liquid level heights of external floating roof tank sealing rings is verified by numerical calculation results. The results show that the numerical results are consistent with those predicted by the formula.
Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion conditions have a significant impact on the performance of the method for estimating source terms. This work proposes the particle swarm optimization (PSO) algorithm coupled with the Gaussian dispersion model for estimating leakage source parameters. The method is validated using experimental cases of the prairie grass field dispersion experiment with various atmospheric stability classes. The results prove the effectiveness of this method. The effects of atmospheric diffusion conditions on estimation outcomes are also investigated. The estimated effect in extreme atmospheric diffusion conditions is not as good as in other diffusion conditions. Accordingly, the Gaussian dispersion model is improved by adding linear and polynomial correction coefficients to it for its inapplicability under extreme diffusion conditions. Finally, the PSO method coupled with improved models is adapted for the source-term parameter estimation. The findings demonstrate that the estimation performance of the PSO method coupled with improved models is significantly improved. It was also found that estimated performances of source parameters of two correction models were significantly distinct under various atmospheric stability classes. There is no single optimal model; however, the model can be selected according to practical diffusion conditions to enhance the estimated precision of source-term parameters.
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