Modeling of Fermentation Process Based on QDPSO-SVM

Utilizing the ability simple in application and quick in convergence of Quantum Delta-potential-well-based Particle Swarm Optimization (QDPSO) algorithm and the high generalization ability of Support Vector Machine (SVM), selecting the appropriate state variables, a dynamic time-varying model has been built. Using the model and algorithm to per-estimate some biochemical state variables which can not be measured on-lineand to optimize some operational variables. It is proved that the method is efficiency through the practical application of penicillin fermentation process.