Online quality prediction based on the double cumulative model for multiphase batch processes with transitions

Abstract

In multiphase batch processes, the final product quality prediction results depend on the common cumulative effects of all the critical phases with transitions on quality rather than a separated one. However, the common cumulative effect are rarely considered at the same time. To address this issue, a double cumulative model is proposed in this paper. The double model includes the internal cumulative model and the external cumulative model. On one hand, the critical local cumulative quality is introduced to isolate the local cumulative effect of different phases and transitions in the internal cumulative model. Especially, for transitions modeling, an average process trajectories method based on the dominant phase identification is developed to deal with the dynamic and uncertainty. On the other hand, the correlation of different cumulative models is extracted by constructing the external cumulative model. In this way, the cumulative effect of all the phases and transitions are considered simultaneously. The proposed method is applied to penicillin fermentation. The simulation results demonstrate the effectiveness and superiority of the proposed method over the competing models.