软测量技术赋能燃煤电厂碳排放计量的研究进展  

Research progress of soft measurement technology optimizing carbon emission measurement of coal-fired power plants

作　　者：姚顺春[1,2] 刘泽明 卢志民 郭松杰[1,2] 谢子立 李峥辉 黄泳如 李龙千 卢伟业 陈小玄 YAO Shunchun;LIU Zeming;LU Zhimin;GUO Songjie;XIE Zili;LI Zhenghui;HUANG Yongru;LI Longqian;LU Weiye;CHEN Xiaoxuan(School of Electric Power,South China University of Technology,Guangzhou 510640,China;Key Laboratory of Energy Efficiency and Clean Utilization in Guangdong Province,Guangzhou 510640,China;Guangdong Institute of Special Equipment Inspection and Research Shunde Branch,Foshan,Guangdong 528300,China)

机构地区：[1]华南理工大学电力学院,广东广州510640 [2]广东省能源高效清洁利用重点实验室,广东广州510640 [3]广东省特种设备检测研究院顺德检测院,广东佛山528300

出　　处：《洁净煤技术》2024年第8期18-31,共14页Clean Coal Technology

基　　金：国家自然科学基金联合基金重点资助项目(U22B20119);广东省自然科学基金-杰出青年资助项目(2021B1515020071)。

摘　　要：火力发电企业作为我国能源结构的重要组成部分,长期以来是我国碳排放的主要来源,在我国和全球加速推动低碳经济发展的宏观环境下,火电企业积极响应国家“能耗双控”向“碳排放双控”转变的战略部署。在此背景下,精确计量燃煤电厂的碳排放量变得至关重要。在燃煤电厂碳计量中,烟气流量影响燃煤发电中在线监测法的精度,而燃煤消耗量、燃煤元素碳含量以及飞灰碳含量共同决定核算法的可靠性。目前,大多数燃煤发电企业只对流量和燃煤消耗量进行实时监测,在现场恶劣的环境中对燃煤元素碳含量以及飞灰碳含量进行短周期、高频次的直接监测需要花费较大的人力以及物力,流量监测设备也易受烟道环境影响。而软测量技术以其高效和低成本的特点,可为传统碳排放计量过程中关键参数的监测提供一种替代方法。鉴于此,首先阐述了软测量模型的建立过程,包含数据预处理、辅助变量选择、软测量模型建立以及模型校正。数据预处理能够确保数据质量,提高建模效率;辅助变量选择是从大量潜在的变量中筛选出对目标变量的辅助变量,进一步提高建模效率;软测量模型建立主要是基于机理建模和数据驱动建模,是实现目标变量预测的核心;模型校正通过实际的离线或在线数据,对模型进行进一步优化,提高模型的预测精度。其次,针对碳计量相关参数,分析了烟气流量、燃煤消耗量、燃煤元素碳含量和飞灰碳含量监测存在的问题,论述了软测量技术在上述碳计量关键参数的国内外研究进展和应用,评估了机理建模和数据驱动建模技术的有效性、准确性和实用性。其中,机理分析建模主要基于电厂锅炉进出口的能量平衡以及烟风质量守恒等原理,有着确定的数学物理关系式,具有高度可解释性和稳定性,但是建模过程复杂,预测精度较低;数据驱动建模�As a significant component of China′s energy structure,thermal power generation enterprises have long been the main source of carbon emissions in the country.With the global push for a low-carbon economy,those enterprisesare shifting from"dual control of energy consumption"to"dual control of carbon emissions."Under this backdrop,accurately measuring the carbon emissions of coal-fired power plants has become crucial.In carbon measurement for coal-fired power plants,flue gas flow impacts the accuracy of the online monitoring method.In contrast,coal consumption,carbon content in coal,and carbon content in fly ash jointly determine the reliability of the calculation method.Currently,most coal-fired plants only perform real-time monitoring of flow and coal consumption.However,direct,high-frequency,short-cycle monitoring of carbon content in coal and fly ash in harsh plant environments requires significant human and material resources and flow monitoring equipment is easily affected by the flue gas environment.Soft measurement technology,with its efficiency and low cost,provides an alternative method for monitoring key parameters in traditional carbon emission measurements.Firstly,this study reviews the establishment of a soft measurement model,including data preprocessing,auxiliary variable selection,model establishment,and model correction.Data preprocessing ensures data quality and improves modeling efficiency.Auxiliary variable selection enhances modeling efficiency by filtering out useful variables.The soft measurement model,based on mechanism and data-driven modeling,is key to predicting target variables.Model correction optimizes the model with actual data,improving prediction accuracy.Secondly,the study analyzes issues in monitoring flue gas flow,coal consumption,coal carbon content,and fly ash carbon content.It discusses the research progress and application of soft measurement technology for these parameters.Mechanism modeling,based on energy balance and mass conservation principles,has high interpretabilit

关 键 词：燃煤电厂 碳排放计量 软测量技术 在线监测法 核算法 

分 类 号：TK229.6[动力工程及工程热物理—动力机械及工程] X511[环境科学与工程—环境工程]