On-line Estimation of Texaco Coal Gasification Quality Based on Support Vector Machine
Prediction of syngas compositions, the most important parameter in determining the products grade and quality control of raw syngas produced in coal gasification, was studied. An on-line estimator model based on dynamic principal component analysis (DPCA) and support vector machine (SVM) was proposed to infer the syngas compositions from real process variables. DPCA was carried out to select the most relevant process features and to eliminate the correlations of the input variables. The SVM based model was established and used to characterize the nonlinearity of the process. After training with available data set, the SVM based model was able to estimate gasification quality quantitatively. The implementation of the model was presented and the model was applied to Texaco coal gasification system to predict the syngas compositions. Research results show that the proposed method provides promising prediction reliability and accuracy.
Texaco coal gasification system (TCGS) on-line estimator dynamic principal component analysis (DPCA) support vector machine (SVM) 1
GUO Rong WANG Wei WANG Xiaojuan
School of Optoelectronical Engineering, Xian Technological University, Xian, 710032
国际会议
北京
英文
2007-08-05（万方平台首次上网日期，不代表论文的发表时间）