Research and application of interval prediction method for complex processes based on principal component independent analysis and mixed kernel RVM

In recent years, higher requirements have been put forward to process monitoring and key variable prediction with increasing complexity of chemical processes. Traditional point predictions do not meet these actual needs nor describe uncertainty concern, so that they could not predict variable trending well. An interval prediction method was proposed from principal component independent analysis and mixed kernel RVM. First, kernel principal component analysis (KPCA) and independent element analysis (ICA) were combined to extract principal components from original variables in complex process and to form independent principal components by independent analysis. Second, mixed kernel from Gauss and polynomial kernel functions and RVM were combined to generate a regression prediction model for the independent principal components, and T distribution was used to make interval estimation on predicted values of the model. Third, comprehensive interval evaluation function was constructed to analyze quality of the interval estimation results. Based on prediction interval coverage probability (PICP) and normal mean prediction interval width (NMPIW), accumulative deviation (AD) was introduced to improve rationality of the interval evaluation. The interval prediction analysis on TE simulation process showed that the proposed interval prediction method had better prediction accuracy and interval estimation quality, which could effectively predict trending of key variables in actual production process.