免疫粒子群算法在修正高斯模型下的源强反演

doi:10.16265/j.cnki.issn1003-3033.2024.07.0146

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (7): 132-138.doi: 10.16265/j.cnki.issn1003-3033.2024.07.0146

免疫粒子群算法在修正高斯模型下的源强反演

万邦银¹^,²(), 蒯念生²^,^**(), 何雄元³, 彭敏君³, 邓利民²

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

收稿日期:2024-01-15 修回日期:2024-04-18 出版日期:2024-07-28
通信作者:
^** 蒯念生(1985—),男,四川成都人,博士,高级工程师,主要从事化工和危险化学品安全生产技术方面的工作。E-mail:kuains@qq.com。
作者简介:
万邦银 (1996—),男,重庆人,硕士研究生,研究方向为危险化学品泄漏危害影响分析及溯源定位。E-mail: wanby@mails.swust.edu.cn。
何雄元工程师；
彭敏君高级工程师；
邓利民教授级高级工程师
基金资助:
重大危险源测控四川省重点实验室基金资助(KFKT2023-05)

Source strength inversion of PSO-IA under modified Gaussian models

WAN Bangyin¹^,²(), KUAI Niansheng²^,^**(), HE Xiongyuan³, PENG Minjun³, DENG Limin²

¹ School of Environment and Resources, Southwest University of Science and Technology, Mianyang Sichuan 621010,China
² Sichuan Institute of Safety Science and Technology, Chengdu Sichuan 610045, China
³ Sichuan Key Laboratory of Measurement and Control of Major Hazardous Sources, Chengdu Sichuan 610045,China

Received:2024-01-15 Revised:2024-04-18 Published:2024-07-28

摘要/Abstract

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。

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

Abstract:

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.

Key words: particle swarm optimization-immune algorithm(PSO-IA), modified Gaussian smoke plume model, source-strength inversion, hazardous gas leakage, solving accuracy

中图分类号:

X937

万邦银, 蒯念生, 何雄元, 彭敏君, 邓利民. 免疫粒子群算法在修正高斯模型下的源强反演[J]. 中国安全科学学报, 2024, 34(7): 132-138.

WAN Bangyin, KUAI Niansheng, HE Xiongyuan, PENG Minjun, DENG Limin. Source strength inversion of PSO-IA under modified Gaussian models[J]. China Safety Science Journal, 2024, 34(7): 132-138.

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对比模型	经典算法	α	x值/m	y值/m	Q/(kg·s^-1)	适应度值 (方差和)	收敛迭代次数
高斯模型	PS	0.02	242.3	44.2	1.255	1.844×10^-4	401
	GA		276.4	42.2	1.249	1.832×10^-4	120
	PSO		252.2	42.6	1.223	1.826×10^-4	102
修正高斯模型	PS		248.5	45.1	1.216	1.806×10^-4	400
	GA		250.3	43.7	1.208	1.795×10^-4	126
	PSO		265.9	43	1.071	1.779×10^-4	113

算法	种群 N	迭代次数	真实坐标/m	反算均值坐标/m	偏离d/ m	θ真实值/ (kg·s^-1)	反算Q均值/ (kg·s^-1)	相对误差/%	计算总时间/s	平均单次时间/s
PSO	30	500	260,45.9	253.7,40.4	8.4	1.127	1.191	5.7	44.7	0.45
	50	500		265.6,42.8	6.4		1.076	4.5	70.9	0.71
	100	500		267.8,47.9	8.1		1.066	5.4	139.2	1.39
PSO-IA	30	500		255.2,41.6	6.4		1.099	2.5	45	0.45
	50	500		261.2,46.5	1.3		1.118	0.8	71.2	0.71
	100	500		262.5,43.1	3.8		1.137	0.9	140.8	1.41

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

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