Estimation of low calorific value of blended coals based on support vector regression and sensitivity analysis in coal-fired power plants

• Published in: Fuel (Guildford) Vol. 236; pp. 1400 - 1407
• Main Authors: Qi, Minfang, Luo, Huageng, Wei, Peijun, Fu, Zhongguang
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.01.2019; Elsevier BV
• Subjects: Artificial neural networks; Back propagation algorithms; Back propagation networks; Calorific value; Coal; Coal-fired power plants; Cost analysis; Electric power generation; Genetic algorithms; Genetic analysis; Low calorific value of blended coals; Mathematical models; Neural networks; Optimization; Power efficiency; Power plants; Predictions; Propagation; Proximate analysis; Regression analysis; Sensitivity analysis; Support vector machines; Support vector regression; Support vector regression; Sensitivity analysis; Low calorific value of blended coals; Proximate analysis; Coal-fired power plants
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2018.09.117

•Models for low calorific value of blended coals are established.•SA can identify the importance of proximate analysis constituents on the LCV.•The SAW-GA-SVR shows better generalization and convergence.•Nonlinear regression models perform better than the linear regression models. Low calorific value (LCV) of blended coals is a vital property for the economy and efficiency of the coal-fired power plants. The experimental determination of the fuel calorific value is costly, while proximate analysis is easier and cheaper. Many linear correlations have been developed to describe the relationships between the calorific value and a few constituents of proximate and/or ultimate analysis, although some may be nonlinear. In this paper a specially designed support vector regression based approach (Sensitivity analysis weighted and genetic algorithm optimized support vector regression, SAW-GA-SVR) was applied to estimate the LCV based on proximate analysis of coal fed to a power plant. In this, nonlinear sensitivity coefficients were used as weighting factors and genetic algorithm was used for optimization. The prediction capability of the proposed model was compared with support vector regression (SVR), genetic algorithm optimized support vector regression (GA-SVR), genetic algorithm optimized back propagation neural network (GA-BPNN), and linear correlations from the published literature. According to the results obtained, the SAW-GA-SVR model has shown better capability of modeling and predicting the LCV of coals over the other methods.