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基于激光复合的甲烷泄漏遥测模型及算法仿真

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提出了一种基于激光遥测仪的复合厂区甲烷(CH4)泄漏率量化和泄漏源定位观测模型,并结合改进的高斯烟羽气团扩散模型进行泄漏率量化和定位算法仿真研究.该观测模型和算法利用小型化、连续化定点厂区监测设备优势,基于实际观测中的样本体积分数、风速、风向、采样点坐标和采样点数误差对反演的泄漏率和泄漏源坐标进行灵敏度分析.对提出的模型和优化算法进行了仿真验证,对实际观测中的各种误差影响进行量化表征.该观测模型的泄漏率量化和泄漏源定位算法研究可为目前厂区的CH4泄漏源量化和定位观测提供参考.
Simulations of Methane Leakage Remote Sensing Model and Algorithm Based on Laser Composite
Objective Methane(CH4)is a critical greenhouse gas with significant implications for the energy and environmental sectors.It plays a pivotal role in advancing global energy transitions.Despite its shorter atmospheric lifetime compared to carbon dioxide(CO2),CH4's per-molecule radiative forcing is substantially higher.Anthropogenic CH4 emissions contribute significantly to global climate change,and making their reduction is a key strategy to mitigate global warming.The coal,oil,and gas industries account for most anthropogenic CH4 emissions.Quantifying CH4 leakage rates and pinpointing leakage sources are vital steps in achieving measurable CH4 emission reductions.However,the development of cost-effective and efficient CH4 monitoring methods remains a challenge.While vehicle-mounted and airborne observations offer mobility,they lack the continuity required for long-term plant monitoring.Fixed-point remote sensing systems provide a promising alternative.In this paper,we propose a composite observation model leveraging laser-based TDLAS sensors for quantitative monitoring of CH4 leakage sources and rates.By utilizing miniaturized and universally adaptable observation equipment,the model can further provide solid theoretical and methodological support for global CH4 leakage emission monitoring.Methods We utilize an active laser TDLAS sensor,a miniaturized tachymeter,and a visible-light camera to create an elevation-based model for leakage monitoring in industrial plants.The laser instrument measures the integral CH4 volume fraction along its path,while a scanning head enables broad-area observations.Using a visible-light camera and rangefinder,an observation field-of-view model is constructed.The laser scans the field to capture CH4 volume fraction points,and coordinates are calculated based on the scanner's angles and tachymeter data.This yields a comprehensive data matrix of CH4 volume fraction and location.Environmental parameters like temperature and pressure,obtained from meteorological stations,are factored into the volume fraction calculations.An improved Gaussian plume diffusion model that incorporates wind direction is utilized to align with the camera's observation field of view,simulating data point acquisition across the entire observation model.A dedicated algorithm for quantifying CH4 leakage rates and locating leakage sources is developed,with its performance evaluated using theoretical data generated by the observation model.Key error sources,including sampling concentration errors,deviations of wind speed and wind direction,coordinate inaccuracies of sampling points,and data point errors,are thoroughly analyzed.We integrate multiple algorithms,compare their adaptability to various error sources,and examine the overall performance of the theoretical observation model and the algorithms.Results and Discussions Simulation results indicate that under the IPPF algorithm,a 30%sampling volume fraction error results in a leakage rate deviation of about 3 mg/s,with upper and lower quartile deviations of about 10 mg/s.For a preset leakage rate of 500 mg/s,the relative deviation is about 2%.Wind direction errors of 60°can cause a maximum leakage rate deviation of 100 mg/s,while coordinate deviations of 2.5 m result in a 40 mg/s leakage rate error.Increasing sampling points improves leakage rate accuracy(Fig.6).Wind direction and observation point coordinates significantly influence leakage source localization,with X-coordinates being more sensitive than Y-coordinates.For low wind speeds(<0.5 m/s),the error in leakage source localization is negligible(Fig.7).Under different atmospheric stability conditions,quantification performs best under condition A,where greater lateral dispersion enhances sampling distribution(Figs.8-10).Among algorithms,IPPF and GA+IPPF yield similar results for leakage rates(Fig.11),while GA+PSO demonstrates improved robustness against wind direction bias,coordinate errors,and sample point density.However,GA+PSO underperforms the other two algorithms in scenarios involving wind speed errors.Conclusions To address CH4 leakage source localization and rate quantification in industrial plants,we propose a multi-device fusion model combining TDLAS sensors,a miniaturized tachymeter,and a visible-light camera.Simulation results show that under a 30%sampling volume fraction error,the IPPF algorithm achieves a leakage rate deviation of about 3 mg/s,with a relative error of about 2%for a theoretical rate of 500 mg/s.Wind speed and wind direction significantly affect leakage rate quantification,with deviations of 5 mg/s observed for wind speed errors of 0.5 m/s.Atmospheric stability conditions further influence quantification accuracy,with condition A providing optimal results.The GA+PSO algorithm effectively addresses uncertainties arising from wind direction bias,coordinate errors,and sampling density,while IPPF and GA+IPPF demonstrate reliability under severe concentration and wind speed errors.Our study offers a robust theoretical and methodological foundation for continuous large-scale CH4 leakage monitoring in industrial settings.

remote sensing and sensormethaneleakage localizationalgorithm simulationerror analysislaser remote sensing

朱首正、刘世界、王森远、唐国良、李春来、王建宇

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中国科学院大学杭州高等研究院物理与光电工程学院,浙江 杭州 310024

中国科学院上海光学精密机械研究所,上海 201800

中国科学院大学,北京 100049

中国科学院上海技术物理研究所空间主动光电技术重点实验室,上海 200083

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遥感与传感器 甲烷 泄漏定位 算法仿真 误差分析 激光遥测

2024

光学学报
中国光学学会 中国科学院上海光学精密机械研究所

光学学报

CSTPCD北大核心
影响因子:1.931
ISSN:0253-2239
年,卷(期):2024.44(24)