论文信息 - Online prediction of unburned carbon content in fly ash with clustering LS-SVM models

Online prediction of unburned carbon content in fly ash with clustering LS-SVM models

In this paper, a novel model called clustering least squares support vector machine (CLS-SVM) is proposed to predict the unburned carbon content in fly ash on line. Prediction accuracy and fast response are major advantages of using CLS-SVM for estimating the unburned carbon content. Moreover, the optical factors influencing the unburned carbon content are selected by means of minimal redundancy maximal relevance (mRMR) criterion. An online updating algorithm is applied to the CLS-SVM model to achieve the online prediction. In the end, comparisons between the proposed CLS-SVM and the traditional LS-SVM are presented to demonstrate the effectiveness. Results are verified on practical data obtained from a 300 MW boiler of a thermal power plant.

Jingcheng Wang | Yuanhao Shi | Guanglei Zhao | Weijing Shi

[1] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[2] Chris H. Q. Ding,et al. Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[3] J. C. Dunn,et al. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[4] Liu Ding-ping. Soft-sensing modeling of the unburned carbon in fly ash based on KPCA-SVM for power station boilers , 2006 .

[5] Liu Wanha. Analysis of Influencing Factors of Boiler Fly Ash and its Control Measures , 2014 .

[6] Jizhen Liu,et al. A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler , 2013 .

[7] Li Zhi. Soft Measurement System Based on Neural Network for The Unburned Carbon in Fly Ash from Utility Boilers , 2004 .

[8] Chao,et al. Using FCM to Select Samples in Semi-Supervised Classification , 2012 .

[9] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.