Quality prediction of batch processes based on incremental support vector regression

Batch processes are widely used in biopharmaceuticals, chemical engineering and other repetitive tasks in industries. Generally, batch processes focus on the quality of the product. Therefore, it is necessary to study the quality prediction of batch processes. In this paper, a quality prediction method based on incremental support vector machine is proposed for the multi-phase and non-linear batch processes.The basic idea of this method is to select some of the process variables of the batch process, i.e., to determine the corresponding input variables affecting a certain output, and then build a support vector regression model and learn this model incrementally to obtain a new model , thereafter quality prediction is performed based on this new model. In the paper, the penicillin fermentation process is taken as the simulation object for quality prediction. The simulation results show that the proposed incremental support vector regression algorithm can describe the fermentation process well.