Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
