Calorific Value Prediction of Coal Based on Least Squares Support Vector Regression

The calorific value of coal is important in both the direct use and conversion into other fuel forms of coals. Accurate calorific value predicting is essential in ensuring the economic, efficient, and safe operation of thermal power plants. Least squares support vector machine (LSSVM) is a variation of the classical SVM, which has minimal computational complexity and fast calculation. This paper presents Least squares support vector regression (LSSVR) to predict the calorific value of coal in Shanxi Coal Mining Region. The LSSVR model takes full advantage of the calorific value information. It derives excellent prediction accuracy, including the relative errors of less than 3.4 % and relatively high determination coefficients. Experimental results conform the engineering application, and show LSSVR as a promising method for accurate prediction of coal quality.

[1]  S. Channiwala,et al.  A UNIFIED CORRELATION FOR ESTIMATING HHV OF SOLID, LIQUID AND GASEOUS FUELS , 2002 .

[2]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[3]  David M. Mason,et al.  Formulas for calculating the calorific value of coal and coal chars: Development, tests, and uses , 1983 .

[4]  Sanjeev S. Tambe,et al.  Estimation of gross calorific value of coals using artificial neural networks , 2007 .

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

[6]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[7]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[8]  Yong Yan,et al.  Support vector machine based online coal identification through advanced flame monitoring , 2014 .

[9]  Jianjun Wang,et al.  An annual load forecasting model based on support vector regression with differential evolution algorithm , 2012 .

[10]  Weiping Zhang,et al.  Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm , 2013, Knowl. Based Syst..

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

[12]  Jiyuan Zhang,et al.  Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network , 2015 .

[13]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[14]  Kadir Kavaklioglu,et al.  Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression , 2011 .