烟气排放连续监测系统不确定度分析
发布时间：2019-05-22 23:52
【摘要】：烟气排放连续监测系统(CEMS),是我国对固定污染源有组织排放进行连续监测的设备,尤其是对火电厂污染源的连续监测。目前相关环保部门已经将CEMS监测数据作为排污核查、排污收费等相关工作的制定基础,因此,CEMS监测数据的准确性对于环境监测工作具有非常重要的意义。我国从2000年开始对CEMS的性能进行检测,也称为适用性检测。本文通过对兰州某热电厂CEMS设备的监测数据进行不确定度评定,并结合质量控制图对数据质量进行进一步验证,从不确定的角度对CEMS的数据分散性及设备稳定性进行了说明。选取该厂7份CEMS监测日报表中的监测数据作为监测结果。首先对颗粒物监测系统分析发现,1号、2号、7号监测报表的颗粒物浓度不确定度较大,数据分散性大,结合质量控制图可知,三份监测报表的质量控制图均表现为失控,证明数据质量差。4号、5号、6号监测报表的不确定度较小,说明数据分散性小,结合质量控制图可知,三份监测报表的数据质量较高。在对各个分量的分析过程中可知,测量重复性对其贡献度较大,仪器本身的精度与误差对其相对较小。对颗粒物监测系统还分析了置信区间半宽和允许区间半宽,其中置信区间半宽为7.47%,允许区间半宽为21.52%,均符合颗粒物连续自动监测仪器性能指标要求。对于该设备的气态污染物监测系统,本文分析了SO2浓度和NOx浓度的监测数据。对SO2浓度监测数据的不确定度分析可知,1号和3号监测报表的SO2浓度数据的不确定度都较高,说明数据的分散性较大,而对应的质量控制图均失控,证明了数据质量差。4号监测报表的SO2浓度数据的不确定度最小,说明数据分散性小,对应的质量控制图表现为数据质量最好,证明了数据的可靠程度高。对NOx浓度监测数据的不确定度分析可知,1号日报表的NOx浓度监测数据的不确定度最大,说明数据的分散性大,而该报表所对应的质量控制图表现为失控,证明了数据质量差,可信度低。4号报表的NOx浓度监测数据的不确定度最小,对应的质量控制图数据质量表现最好,证明了数据的可靠程度高。由分析结果可知,虽然对两种污染物使用相同的测量原理,但SO2浓度的不确定度明显大于NOx浓度的不确定度。对其不确定度分量进行分析可知,SO2浓度的不确定度主要由测量重复性引入,说明测量过程对SO2浓度的监测影响较大。对CEMS进行不确定度分析时,建立的测量模型并不是唯一的,若采取不同测量方法或程序,则不确定度的分析模型也不相同。本文根据兰州某热电厂的CEMS设备建立了相应的初步分析模型,做到了尽量不遗漏不确定度来源,经过计算得到各个系统的不确定度。为企业烟气自动监测领域的不确定度分析提供参考。更进一步说明了不确定度分析在环境监测工作中所具有的重要学术价值和实际意义。
[Abstract]:(CEMS), a continuous monitoring system for flue gas emissions, is a continuous monitoring equipment for organized emissions of fixed pollution sources in China, especially for pollution sources in thermal power plants. At present, the relevant environmental protection departments have taken CEMS monitoring data as the basis of emission verification, sewage charge and other related work. Therefore, the accuracy of CEMS monitoring data is of great significance to environmental monitoring. Since 2000, the performance of CEMS has been tested in China, also known as applicability testing. In this paper, the uncertainty of the monitoring data of CEMS equipment in a thermal power plant in Lanzhou is evaluated, and the data quality is further verified by the quality control chart. The data dispersion and equipment stability of CEMS are explained from an uncertain point of view. The monitoring data of 7 CEMS monitoring daily statements were selected as the monitoring results. First of all, it is found that the particle concentration uncertainty of No. 1, No. 2 and No. 7 monitoring reports is large and the data dispersion is large. Combined with the quality control chart, it can be seen that the quality control charts of the three monitoring reports are out of control. It is proved that the quality of the data is poor. The uncertainty of the monitoring statements No. 4, No. 5 and No. 6 is small, which indicates that the dispersion of the data is small. Combined with the quality control chart, it can be seen that the data quality of the three monitoring reports is higher. In the process of analyzing each component, it can be seen that the repeatability of measurement contributes greatly to it, and the accuracy and error of the instrument itself are relatively small. The half width of confidence interval and the half width of allowable interval are also analyzed for particulate matter monitoring system, in which the half width of confidence interval is 7.47%, and the half width of allowable interval is 21.52%, all of which meet the performance requirements of continuous automatic monitoring instrument of particulate matter. For the gaseous pollutant monitoring system of the equipment, the monitoring data of SO2 concentration and NOx concentration are analyzed in this paper. The uncertainty analysis of SO2 concentration monitoring data shows that the uncertainty of SO2 concentration data of No. 1 and No. 3 monitoring reports is higher, which indicates that the dispersion of the data is large, and the corresponding quality control chart is out of control. It is proved that the data quality is poor. The uncertainty of SO2 concentration data in No. 4 monitoring report is the smallest, which indicates that the data dispersion is small, and the corresponding quality control chart shows that the data quality is the best, which proves that the reliability of the data is high. The uncertainty analysis of NOx concentration monitoring data shows that the uncertainty of NOx concentration monitoring data in daily report No. 1 is the largest, which indicates that the dispersion of the data is large, and the quality control chart corresponding to the report is out of control, which proves that the data quality is poor. The reliability is low. The uncertainty of NOx concentration monitoring data in report No. 4 is the smallest, and the quality performance of the corresponding quality control chart is the best, which proves that the reliability of the data is high. The results show that although the same measurement principle is used for the two pollutants, the uncertainty of SO2 concentration is obviously greater than that of NOx concentration. Through the analysis of its uncertainty component, it can be seen that the uncertainty of SO2 concentration is mainly introduced by the repeatability of measurement, which indicates that the measurement process has a great influence on the monitoring of SO2 concentration. When the uncertainty analysis of CEMS is carried out, the measurement model is not unique, and if different measurement methods or programs are adopted, the analysis model of uncertainty is also different. In this paper, according to the CEMS equipment of a thermal power plant in Lanzhou, the corresponding preliminary analysis model is established, and the source of uncertainty is not omitted as far as possible, and the uncertainty of each system is obtained by calculation. It provides a reference for the uncertainty analysis in the field of automatic monitoring of enterprise flue gas. The important academic value and practical significance of uncertainty analysis in environmental monitoring are further explained.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：X84
本文编号：2483393
[Abstract]:(CEMS), a continuous monitoring system for flue gas emissions, is a continuous monitoring equipment for organized emissions of fixed pollution sources in China, especially for pollution sources in thermal power plants. At present, the relevant environmental protection departments have taken CEMS monitoring data as the basis of emission verification, sewage charge and other related work. Therefore, the accuracy of CEMS monitoring data is of great significance to environmental monitoring. Since 2000, the performance of CEMS has been tested in China, also known as applicability testing. In this paper, the uncertainty of the monitoring data of CEMS equipment in a thermal power plant in Lanzhou is evaluated, and the data quality is further verified by the quality control chart. The data dispersion and equipment stability of CEMS are explained from an uncertain point of view. The monitoring data of 7 CEMS monitoring daily statements were selected as the monitoring results. First of all, it is found that the particle concentration uncertainty of No. 1, No. 2 and No. 7 monitoring reports is large and the data dispersion is large. Combined with the quality control chart, it can be seen that the quality control charts of the three monitoring reports are out of control. It is proved that the quality of the data is poor. The uncertainty of the monitoring statements No. 4, No. 5 and No. 6 is small, which indicates that the dispersion of the data is small. Combined with the quality control chart, it can be seen that the data quality of the three monitoring reports is higher. In the process of analyzing each component, it can be seen that the repeatability of measurement contributes greatly to it, and the accuracy and error of the instrument itself are relatively small. The half width of confidence interval and the half width of allowable interval are also analyzed for particulate matter monitoring system, in which the half width of confidence interval is 7.47%, and the half width of allowable interval is 21.52%, all of which meet the performance requirements of continuous automatic monitoring instrument of particulate matter. For the gaseous pollutant monitoring system of the equipment, the monitoring data of SO2 concentration and NOx concentration are analyzed in this paper. The uncertainty analysis of SO2 concentration monitoring data shows that the uncertainty of SO2 concentration data of No. 1 and No. 3 monitoring reports is higher, which indicates that the dispersion of the data is large, and the corresponding quality control chart is out of control. It is proved that the data quality is poor. The uncertainty of SO2 concentration data in No. 4 monitoring report is the smallest, which indicates that the data dispersion is small, and the corresponding quality control chart shows that the data quality is the best, which proves that the reliability of the data is high. The uncertainty analysis of NOx concentration monitoring data shows that the uncertainty of NOx concentration monitoring data in daily report No. 1 is the largest, which indicates that the dispersion of the data is large, and the quality control chart corresponding to the report is out of control, which proves that the data quality is poor. The reliability is low. The uncertainty of NOx concentration monitoring data in report No. 4 is the smallest, and the quality performance of the corresponding quality control chart is the best, which proves that the reliability of the data is high. The results show that although the same measurement principle is used for the two pollutants, the uncertainty of SO2 concentration is obviously greater than that of NOx concentration. Through the analysis of its uncertainty component, it can be seen that the uncertainty of SO2 concentration is mainly introduced by the repeatability of measurement, which indicates that the measurement process has a great influence on the monitoring of SO2 concentration. When the uncertainty analysis of CEMS is carried out, the measurement model is not unique, and if different measurement methods or programs are adopted, the analysis model of uncertainty is also different. In this paper, according to the CEMS equipment of a thermal power plant in Lanzhou, the corresponding preliminary analysis model is established, and the source of uncertainty is not omitted as far as possible, and the uncertainty of each system is obtained by calculation. It provides a reference for the uncertainty analysis in the field of automatic monitoring of enterprise flue gas. The important academic value and practical significance of uncertainty analysis in environmental monitoring are further explained.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：X84
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