Texaco coal gasification quality prediction by neural estimator based on MSA and dynamic PCA

A novel estimator model, which incorporates DPCA (dynamic principal component analysis), RBF (radial basis function) networks, and MSA (multi-scale analysis), is proposed to infer the properties of manufactured products from real process variables. DPCA is carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis is introduced to acquire much more information and to reduce uncertainty in the system; and RBF networks are used to characterize the nonlinearity of the process. A prediction of the syngas compositions in Texaco coal gasification process is taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy.