免疫粒子群算法在修正高斯模型下的源强反演  

Source strength inversion of PSO-IA under modified Gaussian models

作　　者：万邦银 蒯念生[2] 何雄元 彭敏君 邓利民[2] WAN Bangyin;KUAI Niansheng;HE Xiongyuan;PENG Minjun;DENG Limin(School of Environment and Resources,Southwest University of Science and Technology,Mianyang Sichuan 621010,China;Sichuan Institute of Safety Science and Technology,Chengdu Sichuan 610045,China;Sichuan Key Laboratory of Measurement and Control of Major Hazardous Sources,Chengdu Sichuan 610045,China)

机构地区：[1]西南科技大学环境与资源学院,四川绵阳621010 [2]四川省安全科学技术研究院,四川成都610045 [3]重大危险源测控四川省重点实验室,四川成都610045

出　　处：《中国安全科学学报》2024年第7期132-138,共7页China Safety Science Journal

基　　金：重大危险源测控四川省重点实验室基金资助(KFKT2023-05)。

摘　　要：为提高危险气体泄漏溯源定位的科学性和实效性,确定危险气体泄漏位置和强度是事故应急响应的关键。首先,根据质量守恒定律,分析、改进近似高斯分布的气体羽流扩散幅度,修正高斯烟羽模型;然后,基于免疫浓度筛选机制作为主策略的免疫算法(IA),通过与粒子群算法(PSO)耦合,将混合免疫粒子群(PSO-IA)算法应用到源强反演中;最后,验证PSO-IA算法溯源定位效果。结果表明:与模式搜索法(PS)、遗传算法(GA)、PSO相比,修正高斯烟羽模型预测值误差均下降2%左右;混合PSO-IA算法相较PSO算法反演源强效果有明显提升,其算法定位误差为1.3 m,求解源强误差为0.8%,单次计算时间小于1 s,能实现快速、准确定位并估算源强度。In order to improve the science and effectiveness of traceability and localization of hazardous gas leaks,determining the location and intensity of dangerous gas leaks is the key to emergency response to accidents.The Gaussian plume model was modified by analyzing the mass conservation law and improving the diffusion amplitude of the gas plume with an approximate Gaussian distribution.Additionally,a heuristic algorithm based on the principle of immunization—IA coupled with PSO—was proposed,and the PSO-IA algorithm was applied to source strength inversion.It is concluded that the modified Gaussian plume model has been verified by three classical algorithms(PS,GA and PSO),resulting in a prediction value error decreased by about 2%.PSO algorithm,which showed a better inversion effect,was selected for comparison with the PSO-IA algorithm.The PSO-IA algorithm has improved the effect of inverting source strength,with a localization error is 1.3 m,a source strength solving error of 0.8%,and a single computation time of less than 1 second.This enables fast and accurate positioning and estimation of source strength.

关 键 词：免疫粒子群(PSO-IA)算法 修正高斯烟羽模型 源强反演 危险气体泄漏 求解精度 

分 类 号：X937[环境科学与工程—安全科学]