Lysine fermentation process

which is characterized by nonlinear

large time-delay

multivariable dynamic coupling

is very difficult to realize crucial biochemical parameters on-line measurement.A soft-sensing modeling method was proposed based on PSO-SVM inversion theory.Firstly

the reversibility of the system was analyzed and the inverse extension model was built by introducing the feature information and abandoning the secondary information

and the inverse extension model was built offline with SVM which had good fitting capability and was adjusted online by PSO based on the error information between actual fermentation process input and model output.Finally

the inverse extension model was applied to the fed-batch L-lysine fermentation process and prediction with on-line soft-sensor of the directly immeasurable crucial biochemical parameters was realized.The simulations showed that the crucial biochemical parameters could be predicted.The results also showed that the proposed method was more accurate compared with the alternative modeling methods.