Prediction of gasoline yield in fluid catalytic cracking based on multiple level LSTM

Abstract

Data-driven method has been widely used in Fluid Catalytic Cracking (FCC) process modeling. However, due to the complexity of chemical process both in time and spatial domain, how to reflect the time and spatial characteristics of FCC units and build corresponding model is important to construct a better model for the gasoline yield prediction. In this paper, a special neural network structure was developed to deal with the input variables with different time scales considering the collection characteristics of various variables, as well as the time continuity of large-scale process manufacturing units, LSTMs with different time scales are stacked to extract temporal and spatial features to help capture the relationship between influencing factors and product yield. The characteristics of FCC process are also fully reflected in data processing and building model. It is demonstrated from the conclusions that the new model developed in this paper performs better than the traditional LSTM networks, which will be of great help to the intelligent upgrading of the FCC process.