Multi-model soft sensor development for penicillin fermentation process based on improved density peak clustering

The penicillin fermentation process is a typical nonlinear, dynamic, multiphase, and uncertain process. A single model-based soft sensor is difficult to meet the requirements of system for estimation accuracy. A multi-model soft sensor method based on the improved density peak clustering algorithm has been proposed in this paper to estimate the product concentration in penicillin fermentation process. Firstly, a similarity function instead of the Euclidean distance is introduced to calculate the k-nearest neighbors of the sample points, and the shared neighbors between the sample points and their k-nearest neighbors are computed, then the k-nearest neighbors and the shared neighbors are used to redefine the local density for the sample points. Secondly, the k-nearest neighbors between sample points is used to redefine the allocation strategy of sample points. Finally, the improved clustering algorithm is used to obtain clustering subsets, and the soft sensors based on least squares support vector machine for each subset are established. The verification results of the Pensim simulation platform show that the improved clustering algorithm can more accurately cluster the sample data, thereby effectively improving the estimation accuracy of the soft sensor model of the penicillin fermentation process. © 2021, Editorial Board of CIESC Journal. All right reserved.