Soft Sensing of Key State Variables in Fermentation Process Based on Relevance Vector Machine with Hybrid Kernel Function

To resolve the online detection difficulty of some important state variables in fermentation process with traditional instruments, a soft sensing modeling method based on relevance vector machine (RVM) with a hybrid kernel function is presented. Based on the characteristic analysis of two commonly-used kernel functions, that is, local Gaussian kernel function and global polynomial kernel function, a hybrid kernel function combing merits of Gaussian kernel function and polynomial kernel function is constructed. To design optimal parameters of this kernel function, the particle swarm optimization (PSO) algorithm is applied. The proposed modeling method is used to predict the value of cell concentration in the Lysine fermentation process. Simulation results show that the presented hybrid-kernel RVM model has a better accuracy and performance than the single kernel RVM model. Copyright © 2014 IFSA Publishing, S. L.