| 基于激光诱导击穿光谱(LIBS)的碳核查关键排放因子快速测量方法研究 |
| Study on Rapid Measurement Method of Key Emission Factors for Carbon Verification Based on LIBS |
| 投稿时间：2023-07-17 修订日期：2023-09-25 |
| DOI：10.19316/j.issn.1002-6002.2025.01.03 |
| 中文关键词: 激光诱导击穿光谱 碳核查 单位热值含碳量 定量分析 快速检测 |
| 英文关键词:laser induced breakdown spectroscopy carbon verification carbon content per unit calorific value quantitative analysis quick detection |
| 基金项目:霍英东教育基金会高等学校青年教师基金(171047);广东省自然科学基金杰出青年项目(2021B1515020071) |
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| 通讯作者:姚顺春* 华南理工大学电力学院, 广东 广州 510640 |
| 摘要点击次数: 517 |
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| 中文摘要: |
| 关键排放因子快速测量对确保碳排放核查的准确性和时效性具有重要意义,其中煤炭的单位热值含碳量是影响火电厂碳排放总量核算的关键因子之一。单位热值含碳量是由煤质指标中的含碳量和发热量计算得到,而传统煤质指标检测方法难以满足实时获取单位热值含碳量的需求。因此,基于激光诱导击穿光谱(Laser-Induced BreakdownSpectroscopy,LIBS)技术,提出一种快速同步检测分析煤炭发热量、含碳量和单位热值含碳量的方法。以0.2 mm煤粉颗粒流为实验对象,采集煤样的光谱信号,通过数据预处理手段,结合偏最小二乘法(Partial Least Square,PLS)模型,对发热量、含碳量、单位热值含碳量进行定量分析。结果显示,模型对煤炭发热量、含碳量和单位热值含碳量的预测集的均方根误差分别为0.442 MJ/kg、1.458%、0.000 466 tC/GJ,平均绝对误差分别为0.287 MJ/kg、1.024%、0.000 319 tC/GJ,平均相对误差分别为1.266%、1.685%、1.215%。研究结果证明了该方法的测量精度满足工业现场实测需求,验证了采用LIBS技术快速测量煤炭单位热值含碳量的可行性,为进一步实现火电行业碳排放量快速、准确监测开辟了新道路。 |
| 英文摘要: |
| Rapid measurement of key emission factors is of significant importance to ensure the accuracy and timeliness of carbon emission verification. Among them,the carbon content per unit heat value of coal is one of the key factors influencing the total carbon emissions from thermal power plants. The unit heat value carbon content is calculated based on coal quality indicators and heating value. However,traditional coal quality detection methods fail to meet the requirements for real-time acquisition of unit heat value carbon content. Therefore,this research propose a method for rapid and synchronous detection of coal heating value, carbon content,and unit heat value carbon content using laser-induced breakdown spectroscopy (LIBS) technology. The spectral signals of the coal samples were collected with 0. 2 mm coal particle flow as the experimental object,and the heat content,carbon content,and carbon content per unit calorific value were quantified by means of data pre-processing combined with partial least square (PLS) modeling. The results showed that the root mean square error of the prediction sets for the models of coal heating value,carbon content,and unit heat value carbon content analysis was 0. 442 MJ/kg,1. 458%,and 0. 000466 tC/GJ,respectively. The average absolute error was 0. 287 MJ/kg, 1. 024%, and 0. 000319 tC/GJ, respectively, and the average relative error was 1. 266%,1. 685%,and 1. 215%,respectively. The research results demonstrated that the proposed method meets the measurement accuracy requirements for industrial sites applications,validating the feasibility of LIBS technology for rapid measurement of coal unit heat value carbon content, and it creates a new approach to further enable the rapid and accurate monitoring of carbon emissions in the thermal power industry. |
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