基于混沌映射自适应萤火虫算法的多参优化高斯模型反演

doi:10.16265/j.cnki.issn1003-3033.2025.12.0732

中国安全科学学报 ›› 2025, Vol. 35 ›› Issue (12): 139-146.doi: 10.16265/j.cnki.issn1003-3033.2025.12.0732

基于混沌映射自适应萤火虫算法的多参优化高斯模型反演

罗晋宇¹^,²(), 王永强², 张圣柱³, 邓利民¹^,⁴, 彭敏君¹^,⁴, 蒯念生¹^,⁴^,^**()

¹ 四川省安全科学技术研究院, 四川成都 610045
² 西南科技大学环境与资源学院, 四川绵阳 621010
³ 中国安全生产科学研究院, 北京 100012
⁴ 重大危险源测控与灾害事故应急四川省重点实验室, 四川成都 610045

收稿日期:2025-07-20 修回日期:2025-10-19 出版日期:2025-12-27
通信作者:
^** 蒯念生(1985—),男,四川成都人,博士,高级工程师,主要从事化工安全技术研究。E-mail:kuains@qq.com。
作者简介:
罗晋宇 (2001—),男,陕西西安人,硕士研究生,研究方向为化工园区危险气体源信息反演。E-mail:sakuraka2024@163.com。
张圣柱正高级工程师
邓利民正高级工程师
彭敏君高级工程师
基金资助:
四川省省级科研院所基本科研专项(2025JDKY0039-01); 重大危险源测控四川省重点实验室基金资助(KFKT2023-05)

Inversion of source information in multi-factor optimized Gaussian model by using chaotic mapping-based adaptive firefly algorithm

LUO Jinyu¹^,²(), WANG Yongqiang², ZHANG Shengzhu³, DENG Limin¹^,⁴, PENG Minjun¹^,⁴, KUAI Niansheng¹^,⁴^,^**()

¹ Sichuan Institute of Safety Science and Technology, Chengdu Sichuan 610045, China
² School of Environment and Resources, Southwest University of Science and Technology, Mianyang Sichuan 621010, China
³ China Academy of Safety and Technology, Beijing 100012, China
⁴ Major Hazard Monitoring and Emergency Response Key Laboratory of Sichuan Province, Chengdu Sichuan 610045, China

Received:2025-07-20 Revised:2025-10-19 Published:2025-12-27

摘要/Abstract

摘要：

为快速获取危险气体泄漏事故中包含的源强与位置信息,提出一种基于多因素优化的高斯烟羽扩散模型,并结合混沌映射自适应萤火虫算法(CAFA)反演泄漏源参数。将风速分布、地表阻力及地表反射等关键环境因子引入高斯烟羽模型,通过多因素校正提升模型对复杂工况的拟合能力;在此基础上引入混沌映射以增强萤火虫算法(FA)的种群多样性与全局搜索能力,从而在全局寻优与局部精化之间实现有效平衡,降低陷入局部最优的风险。结果表明:基于风速分布、地面阻力和地面反射优化后,高斯烟羽模型的误差降低16%。CAFA算法能有效避免陷入局部最优解,源强反演的误差可从63.56%降低至0.22%,泄漏源坐标反演的误差可从1.5 m降低至0.2 m。

关键词: 混沌映射自适应萤火虫算法(CAFA), 高斯烟羽模型, 危险气体泄漏, 源信息反演, 化工园区

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

中图分类号:

X937

罗晋宇, 王永强, 张圣柱, 邓利民, 彭敏君, 蒯念生. 基于混沌映射自适应萤火虫算法的多参优化高斯模型反演[J]. 中国安全科学学报, 2025, 35(12): 139-146.

LUO Jinyu, WANG Yongqiang, ZHANG Shengzhu, DENG Limin, PENG Minjun, KUAI Niansheng. Inversion of source information in multi-factor optimized Gaussian model by using chaotic mapping-based adaptive firefly algorithm[J]. China Safety Science Journal, 2025, 35(12): 139-146.

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参考文献 18

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距泄漏源距离/m	1	5	8
经典高斯烟羽模型	1.43	0.3	0.015 8
多因素优化高斯烟羽模型	1.52	0.41	0.022
实际泄漏的测量质量浓度	1.77	0.62	0.039

气体扩散模型名称	收敛代数	源强/(kg·s^-1)	X坐标/m	Y坐标/m	Z坐标/m
经典高斯烟羽模型	33	0.435 6	0.384 1	4.288 4	2.904 8
多因素优化高斯烟羽模型	31	0.442 6	0.526 1	4.485 5	3.076 1

算法名称	收敛代数	反演坐标	真实坐标	误差	源强反演均值	源强真实值	相对误差	绝对误差/%
FA	34	(0.482,3.055,2.509)	(0.45,4.5,3)	1.526	0.736	0.45	0.286	63.56
AFA	30	(0.545,2.511,3.254)	(0.45,4.5,3)	2.007	0.465	0.45	0.015	3.33
CAFA	31	(0.448,4.488,3.211)	(0.45,4.5,3)	0.211	0.451	0.45	0.001	0.22

基于混沌映射自适应萤火虫算法的多参优化高斯模型反演

Inversion of source information in multi-factor optimized Gaussian model by using chaotic mapping-based adaptive firefly algorithm

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