Soft measurement of lysine fermentation based on FCM and integrated Gaussian process regression

In order to solve the problem that cell concentration is difficult to directly measure in the lysine fermentation process,a kind of soft measurement modeling method is proposed on the basis of fuzzy C-mean clustering( FCM) and integrated Gaussian process regression( GPR). The characteristics of typical biological fermentation process can be divided into 4 reaction cycles,including lag phase,exponential growth phase,stable phase,and dead phase. The cluster analysis is conducted for a sample set by applying fuzzy C-mean clustering algorithm,so as to form several sub-sample sets. In order to improve the generalization performance of the GPR,each group is trained through Gaussian Process Regression based on Adaboost and the corresponding integrated sub-models are established. The memberships between each new sample and each group are set as the weights through Euclidean distance and the predicted result is obtained by weighted sum by using typical bacterium of amino acid—L-lysine fermentation as an example. The simulation results showed that compared with the global single GPR model,integrated GPR model and the model based on FCM and multiple GPR,the soft measurement model based on integrated GPR and FCM has high fitting precision. It also had strong generalization ability,which meets the control requirements of lysine fermentation process