The modeling for fermentation process is an important issue in studying microbial fermentation. A precise mathematics model lays the foundation for the subsequent soft measurement and optimization work.The least squares support vector machine(LSSVM)with mixtures of kernels was used for its good performance in the modeling for fermentation process.Global modeling treats entire fermentation process as a study object.It is difficult to guarantee the prediction accuracy.Moreover

it brings about high computing complexity.To solve these problems

a staged modeling method based on LSSVM with optimal mixtures of kernels was proposed.Firstly

secondary variables which presented the characteristics of each stage were selected and modeling samples were classified according to the disparate stages by FCM clustering algorithm.Secondly

local models of stages were built using LSSVM while each local model used the optimal mixtures of kernels.Lastly

the integral process model was constituted with these local models.This method was applied to modeling the penicillin fermentation process based on Pensim and the recombinant E.coli fermentation process in the interleukin-2 production.Simulation results showed that local models of stages were superior to global model both in prediction accuracy and time complexity.The effectiveness of the proposed approach was validated.