Combustion process modeling based on deep sparse least squares support vector regression

In the face of massive historical data of coal-fired power plants, the method of Deep Sparse Least Squares Support Vector Regression (DS-LSSVR) is proposed for building the combustion process model with ideal prediction accuracy and speed. The sparsity process contains two stages. In the first stage, bottom compensation clustering based on grey relational entropy is proposed for deleting similar training samples on the basis of preserving model information as much as possible. In the second stage, the contributive and weighted particle swarm optimization algorithm is proposed to achieve the deep sparsity of DS-LSSVR model. The simulation experiments show that the DS-LSSVR model owns a higher sparsity rate than other sparse LSSVR models. In the application experiment, DS-LSSVR is used to develop the model of NOx emissions and obtains a sparsity rate of 91% with a high prediction accuracy. Moreover, the prediction time of DS-LSSVR model of NOx emissions is less than 1 ms. Therefore, the DS-LSSVR model is able to provide a powerful support for predicting and optimizing the performance index of combustion process online.

Authors

• Wei Zheng
• Chao Wang Tsinghua University
• Da Liu

Keywords

• Combustion process modeling
• Least squares support vector regression
• Model sparsity
• Grey relational entropy
• Particle swarm optimization