Soft sensing algorithm of marine lysozyme mycelium fermentation bacteria concentration based on SUKF
1.School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2.China Machinery Industry Suzhou Technical School, Suzhou, Jiangsu 215101, China; 3.Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, Shandong 266071, China
Abstract：In order to accurately and in real-time measure lysozyme biomass activity concentration in the fermentation process of marine biological enzyme preparation, the soft-sensing algorithm with nonlinear state-estimation was proposed based on scale unscented transformation Kalman filter(SUKF) algorithm. The Kalman filter framework was adopted and embedded with scaled unscented transformation(SUT). The aggregation degradation effects of high-dimensional and nonlinear fermentation model were effectively solved in sample. By σ-point set with symmetric sampling strategy, the mean points were increased according to the fermentation of priori information of each dimension mean. Using cross-validation method to select mo-del parameters, the method was compared with the support vector machine (SVM) and the radial basis function neural network (RBFNN) algorithm. The results show that the smallest root mean square statistical error between training and testing in soft sensing with SUKF is reduced by about 2%. The establishment of accurate fermentation model or observation model is not required in the proposed method. For nonlinear system identification, SUKF shows better generalization performance with high accuracy.
2013, Vol. 34 
