Refinery scheduling with varying crude: a deep belief network classification and multi-model approach

In model-based refinery scheduling, the varying composition of the crude being refined is a major challenge, especially for those reaction processes. In this paper, a classification based, multi-model approach is proposed to handle the frequently varying crude. The idea is to build a scheduling model for each type of feed crude, and the type can be determined by using an online classifier. The recently emerged deep belief network is introduced to develop the classifier, which provides more accurate classification than the traditional neural network. The proposed method is demonstrated through modeling a fluidized catalytic cracking unit (the mostly affected by varying crude), and then the scheduling of a refinery that was carefully simulated to mimic the actual operation of a refinery in northern China. The results reveal that the multi-model approach is effective in handling varying crude.