Research on leak detection of oil and gas station based on Gauss plume model and ISSA algorithm
In order to improve the prediction accuracy of leakage detectionat oil and gas stations and reduce the economic loss and environmental pollution caused by post-treatment,the Gaussian plume model was used as a forward gas diffusion model,the sparrow population initialized through Circle chaotic mapping,and the butterfly algorithm added to the sparrow finder search strategy;then,the ISSA algorithm was used to achieve the iterative calculation of the concentration error minimization,with its influence on the inversion results measured from the perspectives of iteration times,population size,mesh size and noise intensity.The results show that the ISSA algorithm is consistent with the SSA algorithm in the complexity of time algorithm,with the global search ability and local development ability of the optimized algorithm enhanced.By transforming the geographical coordinate system into the standard wind direction coordinate system,the calculation process was simplified.Under the settings of maximum iteration number 100,population size 100 and grid size 0.5 m×0.5 m×0.5 m,when there are two monitoring points in the station affected by noise,the maximum error of leakage position in the three directions is 1.37%,1.02%and 9.70%respectively,and the maximum relative error of leakage rate 0.22%,meeting the requirements of on-site location detection.The research results can provide practical reference for the improvement of the integrity management of oil and gas stations.
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