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Volume 2 Issue S1
Jul.  2020
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QIAO Hong, LI Han, WANG Tiankun. Multi-model Fusion Soft Sensor Method for Thermal Power Plant Carbon Content of Fly Ash[J]. SOUTHERN ENERGY CONSTRUCTION, 2015, 2(S1): 10-14. doi: 10.16516/j.gedi.issn2095-8676.2015.S1.003
Citation: QIAO Hong, LI Han, WANG Tiankun. Multi-model Fusion Soft Sensor Method for Thermal Power Plant Carbon Content of Fly Ash[J]. SOUTHERN ENERGY CONSTRUCTION, 2015, 2(S1): 10-14. doi: 10.16516/j.gedi.issn2095-8676.2015.S1.003

Multi-model Fusion Soft Sensor Method for Thermal Power Plant Carbon Content of Fly Ash

doi: 10.16516/j.gedi.issn2095-8676.2015.S1.003
  • Received Date: 2015-11-15
  • In view of the present fly ash carbon content measurement method for thermal power plant coal-fired boiler exists the problems such as time delay and low precision, on the basis of the boiler fly ash carbon content influencing factors' analysis, using the thought that the combination of multiple models can improve the model accuracy and robustness, we proposed a multi-model dynamic soft sensor modeling method based on support vector machine (SVM) fusion. This modeling method uses the time series data to build a model, each submodel express the estimate of the output in one condition, and each submodel's predicted output realizes the variable weighting fusion through SVM method. We did a modeling study of fly ash carbon content soft sensor, using the historical data of thermal power plant, and the result shows that the method can achieve better measurement effect.
  • 通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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Multi-model Fusion Soft Sensor Method for Thermal Power Plant Carbon Content of Fly Ash

doi: 10.16516/j.gedi.issn2095-8676.2015.S1.003

Abstract: In view of the present fly ash carbon content measurement method for thermal power plant coal-fired boiler exists the problems such as time delay and low precision, on the basis of the boiler fly ash carbon content influencing factors' analysis, using the thought that the combination of multiple models can improve the model accuracy and robustness, we proposed a multi-model dynamic soft sensor modeling method based on support vector machine (SVM) fusion. This modeling method uses the time series data to build a model, each submodel express the estimate of the output in one condition, and each submodel's predicted output realizes the variable weighting fusion through SVM method. We did a modeling study of fly ash carbon content soft sensor, using the historical data of thermal power plant, and the result shows that the method can achieve better measurement effect.

QIAO Hong, LI Han, WANG Tiankun. Multi-model Fusion Soft Sensor Method for Thermal Power Plant Carbon Content of Fly Ash[J]. SOUTHERN ENERGY CONSTRUCTION, 2015, 2(S1): 10-14. doi: 10.16516/j.gedi.issn2095-8676.2015.S1.003
Citation: QIAO Hong, LI Han, WANG Tiankun. Multi-model Fusion Soft Sensor Method for Thermal Power Plant Carbon Content of Fly Ash[J]. SOUTHERN ENERGY CONSTRUCTION, 2015, 2(S1): 10-14. doi: 10.16516/j.gedi.issn2095-8676.2015.S1.003

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