Product Yields Forecasting for FCCU via Deep Bi-directional LSTM Network

This paper studies product yields forecasting for fluid catalytic cracking unit (FCCU). Conventional product yields forecasting is usually based on mechanism model, which may ignore some significant factors due to manual approximations. Deep learning methods can extract features automatically based on data without prior knowledge. Considering bidirectional temporal features and spatial features of FCCU, deep bidirectional long-short-term memory (DBLSTM) network is proposed for product yields forecasting. The bidirectional structure can capture bidirectional temporal features of FCCU by considering previous information as well as future information over a period of time. Significant spatial features of sensors at each time step can be extracted automatically through a deep structure by stacking multiple bidirectional structures. Moreover, the deep bidirectional LSTM network can deal with long-term dependencies by integrating deep bidirectional structure with LSTM cell. To avoid overfitting, regularization adopted in this paper is dropout and early stopping. Efficacy of the DBLSTM approach is demonstrated by process data from an actual FCCU in China. Through the comparison of mean squared error on product yields forecasting, the DBLSTM approach is superior to traditional regression models and other recurrent models.