Multi-output soft sensor modeling approach for penicillin fermentation process based on features of big data

The product quality indicators of the penicillin fermentation process have multiple semantics and are interrelated. There is a complex nonlinear mapping relationship between input characteristics and multiple-output objectives, and the time dependence is strong. As a result, the prediction accuracy of existing soft sensor models is poor, and it is difficult to meet the needs of industrial sites. To solve the above problems, this paper proposes a multi-output soft sensor modeling method for the penicillin fermentation process based on big data feature analysis. In this method, the process data is divided into several batches in order, and then the data features of multivariable and time-dependent datasets are extracted according to the deep sparse self-coding neural network method to realize the effective mining of the relationship between multivariable time series factors, based on the multi-output support vector regression method, several soft sensor models for different prediction targets are established. Meanwhile, to improve the Predictive performance of the soft sensor model, the improved black hole algorithm is used to optimize the model parameters. Finally, a simulation experiment is carried out based on the simulation dataset of the penicillin fermentation process to verify the effectiveness of the proposed method.