Soft Sensing of the Lysozyme Mycelium Bacteria Concentration Based on SUKF Algorithm

摘要

In order to measure lysozyme biomass activity concentration accurately and in real-time in the fermentation process of marine biological enzyme preparation, soft sensing with the nonlinear state-estimation based on SUKF has been used, the method uses KF framework, embedded in SUT. In fact the fermentation bacteria is lysozyme, which is fermented in a fermenter of KRH-100L according to process requirements. The statistical properties of variables through the nonlinear transformation has been calculated and the degradation effects of aggregation of high-dimensional and nonlinear fermentation model would be effectively settled in sample. By using σ-point set with symmetric sampling strategy, the mean points increased, according to the fermentation of priori information of each dimension mean. By using cross-validation method to select model parameters, compared with the support vector machine SVM with RBFNN algorithm, the experimental results show that the smallest root mean square statistical error of training and testing in soft Sensing with SUKF reducetl by 2% or so.