石油化工是我国支柱产业，而硫化氢泄漏事故是石化产业的重要灾害之一，具有突发性、群体性和死亡率高等特点。管道作为硫化氢气体输送的主要途径，起着至关重要的作用，但是由于磨损、压力、腐蚀等原因，导致管道易发生泄漏，为了更加全面的了解硫化氢管道在不同条件下的泄漏扩散规律，建立风险分级预警方法，本文开展了硫化氢管道泄漏实验及数值模拟研究。

基于硫化氢管道泄漏扩散机理及影响因素，搭建了硫化氢泄漏扩散实验装置，开展微量泄漏扩散实验研究。通过调节泄漏源硫化氢浓度、泄漏流量、风速和泄漏孔径的参数条件进行实验，研究发现，各测点硫化氢浓度与泄漏源硫化氢浓度、泄漏流量呈线性正相关，与风速呈线性负相关。泄漏源硫化氢浓度由1%增至3%，各测点浓度最大增幅227.27%，泄漏流量由0.5L/min增至1.5L/min，各测点浓度最大增幅142.70%，风速由0.2m/s增至1.0m/s，各测点浓度最大下降幅度57.14%，而在硫化氢微量泄漏情况下，泄漏孔径的改变对硫化氢扩散的影响程度相对较小。

利用Fluent模拟软件对实验相同参数条件下的硫化氢泄漏扩散规律进行对比分析，并建立了较贴合实际条件的硫化氢管道泄漏模型，研究不同泄漏源硫化氢浓度、管道内部压力、风速、泄漏孔径的参数条件下，硫化氢气体浓度分布及危险区域范围。经研究发现，泄漏源硫化氢浓度、管内压力、泄漏孔径的增大，对硫化氢的聚集起到了积极作用，风速的增加则会抑制高浓度硫化氢的聚集。

根据实验和数值模拟结果，获取硫化氢管道泄漏危险区域范围相关数据，利用CatBoost、XGBoost、LightGBM、SVR及线性回归五种算法建立硫化氢管道泄漏扩散距离预测模型，对比发现，CatBoost模型预测结果与真实值之间误差最小。然后根据硫化氢管道泄漏扩散距离预测模型，制定硫化氢管道泄漏风险分级预警方法，为石化园区硫化氢管道泄漏距离预测及事故应急处置提供了有效的理论基础。

The petrochemical industry is the pillar industry in China, and hydrogen sulfide leakage accident is one of the important disasters in the petrochemical industry, which has the characteristics of the sudden, group, and high mortality. Pipe as the main way of hydrogen sulfide gas plays an important role, but due to wear, pressure, corrosion, etc, lead to a pipeline leak, to more comprehensive understanding of the hydrogen sulfide leakage diffusion law of pipeline under different conditions, to establish risk early warning method, this paper carried out the study of hydrogen sulfide pipeline leak experiment and numerical simulation.

Based on the leakage and diffusion mechanism and influencing factors of the hydrogen sulfide pipeline, an experimental device for hydrogen sulfide leakage and diffusion was built to carry out the microleakage and diffusion experimental study. By adjusting the concentration of hydrogen sulfide from the leakage source, the leakage flow rate, the wind speed, and the leakage aperture of the parameters of the experiment, the study found that the concentration of hydrogen sulfide at each measuring point and the concentration of hydrogen sulfide from the leakage source, the leakage flow rate showed a linear positive correlation, and the wind speed showed a linear negative correlation. The concentration of hydrogen sulfide from the leakage source increases from 1% to 3%, and the concentration at each measuring point increases by 227.27%. The leakage flow increases from 0.5L/min to 1.5L/min, and the concentration at each measuring point increases by 142.70%. The wind speed increases from 0.2m/s to 1.0m/s and the concentration at each measuring point decreases by 57.14%. The influence of leakage aperture on hydrogen sulfide diffusion is relatively small.

Using Fluent simulation software of experiment parameters under the condition of the same hydrogen sulfide diffusion regularity, this paper compares and analyzes, and establishes the more realistic conditions of hydrogen sulfide pipeline leakage model, the research of different concentrations of hydrogen sulfide leakage, pipeline internal pressure, wind speed, leakage aperture parameters under the condition of hydrogen sulfide gas concentration distribution and scope of the danger zone. It was found that the concentration of H₂S, the pressure in the tube, and the pore size of the leakage source increased, which played a positive role in the accumulation of H₂S, while the increase of wind speed inhibited the accumulation of a high concentration of H₂S.

According to the experimental and numerical simulation results, the relevant data on the hydrogen sulfide pipeline leakage risk area were obtained, and the prediction model of hydrogen sulfide pipeline leakage diffusion distance was established by using CatBoost, XGBoost, LightGBM, SVR, and linear regression algorithms. The comparison showed that the error between the CatBoost model prediction and the real value was the smallest. Then, according to the prediction model of hydrogen sulfide pipeline leakage and diffusion distance, the classification warning method of hydrogen sulfide pipeline leakage risk is developed, which provides an effective theoretical basis for the prediction of hydrogen sulfide pipeline leakage distance and emergency treatment of accidents in petrochemical parks.

[1]何娟霞, 刘磊, 周琪勇, 等. 基于SLAB的重气泄漏扩散模拟分析[J]. 科学技术与工程, 2020, 20(17): 7108-7113.

[2]张武星. 石化企业有毒有害气体中毒事故分析与防范[J]. 安全、健康和环境, 2006, 6(9): 10-12.

[3]赵桂利, 赵祥迪, 姜春雨, 等. 有毒气体重大突发泄漏情景设计及仿真分析[J]. 安全、健康和环境, 2020, 20(09): 5-10.

[4]山东东营一石化公司硫化氢公司气体泄漏3人中毒2人己死亡[J]. 化工安全与环境, 2017, (32): 24-24.

[5]马野, 何娟霞, 刘磊, 等. 易燃易爆液池重气浓度扩散规律分析[J]. 消防科学与技术, 2021, 40(11): 1575-1579.

[6]朱红亚, 有毒有害重气多源泄漏演变规律及预测预警技术研究. 天津市,公安部天津消防研究所, 2018.05.30.

[7]Arystanbekova N K, APPlieation of Gaussian plume models for air pollution simulation at instantaneous emissions[J].Mathematics and Computers in Simulmion,2004,67(4-5):451-458.

[8]Chamindu Deepagoda T K K , Smits K M , Oldenburg C M. Effect of subsurface soil moisture variability and atmospheric conditions on methane gas migration in shallow subsurface[J]. International Journal of Greenhouse Gas Control, 2016, 55: 105-117.

[9]Bonnaud C, Cluzel V, Corcoles P, et al. Experimental study and modelling of the consequences of small leaks on buried transmission gas pipeline[J]. Journal of Loss Prevention in the Process Industries, 2018, 55: 303-312.

[10]Liu A H, Huang J, Li Z W, et al. Numerical simulation and experiment on the law of urban natural gas leakage and diffusion for different building layouts[J]. Journal of Natural Gas Science and Engineering, 2018, 54: 1-10.

[11]A Dandrieux, JP Dimbour, G Dusserre. Are dispersion models suitable for simulating small gaseous chlorine releases?[J].Journal of Loss Prevention in the Process Industries,2006,19(6):683-689.

[12]曹杨, 王红红, 吕松松, 等. 危险性气体泄漏扩散研究方法对比[J]. 工业安全与环保, 2021, 47(01): 17-21.

[13]杨昭, 赖建波, 韩金丽. 天然气管道孔口泄漏危险域的研究[J]. 天然气工业, 2006, 26(11): 156-159.

[14]杨昭, 赖建波, 韩金丽. 天然气管道气体泄漏扩散过程研究[J]. 天然气工业, 2007, 27(7): 97-99.

[15]曲翔宇, 朱红亚. 泄漏源间距对多源重气泄漏扩散的影响[J]. 消防科学与技术, 2020, 39(7): 893-896.

[16]周宁, 滕欣, 袁雄军, 等. 基于可视化理论的重气泄漏扩散研究[J]. 环境工程, 2016, 34(10): 147-152.

[17]史先召, 黄伟金, 吴东伟, 等. 障碍物对石化罐区重质气体泄漏扩散影响的探讨[J]. 石油库与加油站, 2017, 26(04): 16-19+6.

[18]丁慧心. 地下水电站出线竖井中SF6泄漏扩散的实验与数值模拟研究[D]. 重庆: 重庆大学, 2016.

[19]Cui D. Study on Influence Characteristics of Natural Gas Leakage in Long-distance Pipelines[J]. IOP Conference Series: Earth and Environmental Science, 2021, 687(1): 012125.

[20]Li X, Zhou N, Chen B, et al. Numerical Simulation of Leakage and Diffusion Process of LNG Storage Tanks[J]. Energies, 2021, 14(19):6282.

[21]Zhou N, Cui W, Wang K Q, et al. Numerical Simulation Analysis of Dangerous Heavy Gas Leakage and Diffusion[J]. Advanced Materials Research, 2013,726-731: 1354–1357.

[22]Cui J, Ma S Y, Liang Y P, et al. Numerical Simulation for Perforation-Caused Leakage Diffusion of Buried Gas Pipeline[J]. Applied Mechanics and Materials, 2014, 509: 135–140.

[23]Guo D P, Zhao P, Wang R, et al. Numerical simulation studies of the effect of atmospheric stratification on the dispersion of LNG vapor released from the top of a storage tank[J]. Journal of Loss Prevention in the Process Industries, 2019, 61(C).

[24]Ebrahimi.M A, Farzaneh.G M, Arabkoohsar A, et al. CFD analysis of natural gas emission from damaged pipelines: correlation development for leakage estimation[J]. Journal of Cleaner Production, 2018, 199: 257-271.

[25]Bezaatpour J, Fatehifar E, Rasoulzadeh A. CFD investigation of natural gas leakage and propagation from buried pipeline for anisotropic and partially saturated multilayer soil[J]. Journal of Cleaner Production, 2020, 277: 123940.

[26]Wang H D, Lu S, Cheng J J, et al. Inverse modeling of in door instantaneous airborne contaminant source location with adjoint probability-based method under dynamic airflow field [J]. Building&Environment, 2017, 117: 178-190.

[27]赵桂利, 赵祥迪, 姜春雨, 等. 有毒气体重大突发泄漏情景设计及仿真分析[J]. 安全、健康和环境, 2020, 20(09): 5-10.

[28]张淼淼, 卢怡, 王文深. 基于高斯模型的硫化氢泄漏危险区域模拟分析[J]. 广东化工, 2020, 47(14): 73-75.

[29]张鑫, 郭文杰. 环境参数对硫化氢储罐泄漏后气体有毒区域的影响[J]. 黑龙江工程学院学报, 2020, 34(01): 38-41.

[30]李金洁, 吴洁, 邢志祥, 等. 障碍物对氯乙烯气体泄漏扩散影响的模拟研究[J]. 消防科学与技术, 2021, 40(03): 307-312.

[31]彭伟, 张俊, 袁宏永, 等. 深埋管道中泄漏孔对燃气泄漏影响的数值模拟[J]. 中国安全生产科学技术, 2020, 16(10): 59-64.

[32]张倩茹, 张旭, 叶蔚, 等. 大空间重气泄漏下速度场、浓度场特性分析[J]. 化工学报, 2020, 71(S1): 57-67.

[33]吴姗姗. 受限空间内重质气体扩散与应急救援实验研究[D]. 华北科技学院, 2017.

[34]Xing J, Liu Z Y, Huang P, et al. CFD validati on of scaling rules for reduced-scale field releases of carbon dioxide[J]. Applied Energy, 2014, 115(4): 525-530.

[35]Liu C, Liao Y, Liang J, et.al. Quantifying methane release and dispersion estimations for buried natural gas pipeline leakages[J]. Process Safety and Environmental Protection, 146: 552–563.

[36]喻闯闯, 刘瑞敏, 罗天培, 等. 重气泄漏扩散过程数值模拟[J]. 低温工程, 2018(03): 45-51.

[37]Yin S T, Wang N N, Huang X, et.al. Characteristic of vapor leakage behavior from a pressurized pipeline system: Experiment and model study[J]. International Journal of Heat and Mass Transfer, 162: 120335.

[38]Olorunmaiye J A, Imide N E. Computation of natural gas pipeline rupture problems using the method of characteristics[J]. Journal of Hazardous Materials,1993, 34(1): 81-98.

[39]Lang E, Fannelop T.K. Efficient computation of pipeline break problem. 1987.

[40]Flatt R. Unsteady compressible flow in long pipelines following a rupture[J]. International Journal for Numerical Methods in Fluids, 1986, 6(2): 83-100.

[41]Ebrahimi.M A，Farzaneh.G M，Deymi.D M. Correlations for estimating natural gas leakage from above-ground and buried urban distribution pipelines[J]. Journal of Natural Gas Science and Engineering，2016，34：185-196.

[42]陈潜, 邢晓凯, 王炜硕. 基于特征线法的输气管道泄漏瞬态仿真与分析[J]. 科学技术与工程, 2017, 17(30): 217-222.

[43]Li X, Chen G, Zhang R, et al. Simulation and assessment of underwater gas release and dispersion from subsea gas pipelines leak[J]. Process Safety and Environmental Protection, 2018, 119: 46-57.

[44]Ebrahimi.M A, Farzaneh.G M, Arabkoohsar A, et al. CFD analysis of natural gas emission from damaged pipelines: correlation development for leakage estimation[J]. Journal of Cleaner Production, 2018, 199: 257-271.

[45]Ebrahimi.M A, Farzaneh.G M, Deymi.D M. Correlations for estimating natural gas leakage from above-ground and buried urban distribution pipelines[J]. Journal of Natural Gas Science&Engineering, 2016, 34: 185-196.

[46]Ebrahimi.M A, Farzaneh.G. Mahmood, et,al. CFD analysis of natural gas emission from damaged pipelines: Correlation development for leakage estimation[J]. Journal of Cleaner Production, 199: 257–271.

[47]张嘉琦. 基于FLUENT室内天然气管道泄漏扩散数值模拟[D]. 东北石油大学, 2020.

[48]Luo J H, Zheng M, Zhao X W, et al. Simplified expression for estimating release rate of hazardous gas from a hole on high-pressure pipelines[J].Journal of Loss Prevention in the Process Industries,2006,19(4):362-366.

[49]Kostowski W J, Skorek J. Real gas flow simulation in damaged distribution pipelines[J]. Energy, 2012, 45(1): 481-488.

[50]李广贞. 超高压天然气管线泄漏的数值模拟[J]. 管道技术与设备, 2019(04): 9-12.

[51]彭琳, 杨应迪, 彭伟, 等. 基于ALOHA和多元回归预测的地面天然气管道泄漏扩散模型研究[J]. 河南理工大学学报(自然科学版), 2021, 40(01): 8-14.

[52]王瀚霖, 黄卫星. 基于可压缩流体的天然气管道泄漏量预测模型[J]. 安全与环境工程, 2015, 22(04): 136-141.

[53]Li J, Xing J, Murong H D. Emergency prediction algorithm for leakage and diffusion of flammable and explosive gas at sea port[J]. Arabian Journal of Geosciences, 2021, 14(13).

[54]Wu J S, Cai J T, Yuan S Q et al. CFD and EnKF coupling estimation of LNG leakage and dispersion[J] Safety Science, 2021, 139.

[55]Zhu G R, Guo X Y, Yi Y, et al. Experiment and simulation research of evolution process for LNG leakage and diffusion[J]. Journal of Loss Prevention in the Process Industries, 2020, 64(C).

[56]汪明敏. 预警概念界定问题研究[J]. 情报杂志, 2022, 41(01): 14-18.

[57]彭军龙, 张学民. 灾害气候环境下道路交通安全动态趋势预警分析方法[J]. 系统工程, 2011, (4): 66-70.

[58]谭琼, 冯国梁, 袁宏永, 等. 燃气管线相邻地下空间安全监测方法及其应用研究[J]. 安全与环境学报, 2019, 19(03): 902-908.

[59]时国明, 周智, 苏晔华, 等. 天然气站场扫描式激光气体监测系统的研制[J]. 油气储运, 2021, 40(05): 554-560.

[60]曾稳稳, 邓付洁, 蔡静波, 等. 环氧乙烷反应器泄漏实时监测预警模型构建方法及应用[J]. 化工进展, 2019, 38(11): 5200-5209.

[61]李军, 李庆奇, 贺城墙, 等. 基于GIS的危险化学气体泄漏事故应急响应研究[J]. 中国安全生产科学技术, 2017, 13(11): 66-72.

[62]李兴华, 王媛媛, 袁子龙. 基于新一代信息技术的化工园区气体泄漏监测预警系统[J]. 中国安全生产科学技术, 2021, 17(S1): 10-14.

[63]杨志华, 宋佩月, 付建民, 等. 原油储罐区可燃气体探测器布置优化方案研究[J]. 中国安全生产科学技术, 2021, 17(01): 85-89.

[64]周亚芬. 风沙环境下风力机气动性能及磨损特性研究[D]. 东北电力大学, 2021.

[65]Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generativeadversarial nets[C]. Proceedings of the 27th International Conference on Neural Information Processing System.Cam-bridge: MIT Press, 2014: 2672–2680.

[66]牛丽. 基于CatBoost融合算法的信用风险评估及模型研究[D]. 太原理工大学, 2021.

[67]刘清, 葛永慧. 稳健总体最小二乘法一元线性回归的相对有效性探讨[J]. 统计与决策, 2017(24): 14-17.

[68]张琳, 汪廷华 ,周慧颖. 基于群智能算法的SVR参数优化研究进展[J]. 计算机工程与应用, 2021, 57(16): 50-64.

[69]Ke G, Meng Q, Finely T, et al. LightGBM: A highly efficient gradient boosting decision tree[C]. Advances in Neural Information Processing Systems 30, 2017.

[70]Chen T, Guestrin C. XGBoost: A scalable tree boosting system[C]. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 785–794.

[71]胡敏. 炼化企业硫化氢危害分析与防护设计若干问题探析[J]. 炼油技术与工程, 2019,49(09): 59-64.