Research progress of soft measurement technology optimizing carbon emission measurement of coal-fired power plants
YAO Shunchun
LIU Zeming
LU Zhimin
GUO Songjie
XIE Zili
LI Zhenghui
HUANG Yongru
LI Longqian
LU Weiye
CHEN Xiaoxuan
Abstract：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 en-ergy 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 mo-nitoring 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,di-rect,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 estab-lishment,and model correction.Data preprocessing ensures data quality and improves modeling efficiency.Auxiliary variable selection en-hances 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 re-search progress and application of soft measurement technology for these parameters.Mechanism modeling,based on energy balance and mass conservation principles,has high interpretability and stability but is complex and less accurate.Data-driven modeling,using machine learning and data from distributed control systems(DCS)offers higher accuracy,but lacks transparency and generalization ability.Finally,this study summarizes and prospects the development and application of soft measurement technology in the field of carbon emission measurement.It provides suggestions for integrating the time-series structure of various plant parameters,the computational limi-tations of the plant itself,and the development of methods combining mechanism analysis and data-driven approaches.It summarizes the application scenarios of predictive CO2 emission systems abroad and anticipates the application of such systems combined with soft meas-urement technology in domestic and international coal-fired power plants.
Keywords：coal-fired power plantscarbon emission measurementsoft sensing technologyonline monitoring methodcarbon account-ing method
Publication Date：2024-08-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：14( 18-31 )