Abstract
The calorific value of coal gangue is a critical index for coal waste recycling and the energy industry. To establish an accurate and efficient calorific value forecasting model, a method based on hybrid kernel function−support vector regression and genetic algorithms is presented in this paper. Firstly, key features of coal gangue gathered from major coal mines are measured and used to build a sample set. Then, the forecasting performance of single kernel function-based models is established, and linear kernel and Gaussian kernel functions are chosen according to forecasting results. Next, a hybrid kernel combined with the two kernel functions mentioned above is used to establish a calorific value forecasting model. In addition, a genetic algorithm is introduced to optimize critical parameters of SVR and the adjustable weight. Finally, the forecasting model based on hybrid kernel function−support vector regression and genetic algorithms is built to predict the calorific value of new coal gangue samples. The experimental results indicate that the hybrid kernel function is more suitable for forecasting the calorific value of coal gangue than that of a single kernel function. Moreover, the forecasting performance of the proposed method is better than other conventional forecasting methods.