Quality prediction model for process sequential data of irregular measurements with sampling-interval-attention LSTM

摘要

Modern industrial processes are often characterized with nonlinearities and dynamics. The long short-term memory (LSTM) based soft sensor models have been used for process quality variable prediction. However, most traditional dynamic models like LSTM cannot handle dynamic data sequences with irregular sampling measurements, which is very common in many industrial processes. In this paper, a novel sampling-interval-attention LSTM (SIA-LSTM) is proposed to solve the irregular sampling problem in industrial process. In SIA-LSTM, an additional attention network is designed to learn sampling-interval relevant weight to adjust the dynamic relationship between two adjacent sampled data. The effectiveness and performance of the proposed method is validated on an industrial hydrocracking process to predict the 90% boiling point of aviation kerosene and heavy naphtha.