LAUSR

The main purpose of soft sensor modeling is to capture the dynamic nonlinear features between the easy-to-measure auxiliary variables and the difficult-to-measure key variables. However, in complex industrial process, it is a challenging work due to the too complicated relationships among the process variables and the base measurement problems. Recently, long short-term memory (LSTM) network shows powerful long-term feature extraction capabilities in complex industrial processes. LSTM focuses on the relationship between the input time series and the output. However, what we concern with are the impact of changes in secondary variables over time on the being detected key variables. In this article, a novel soft sensor modeling approach called difference long short-term memory network is proposed for key variables prediction in complex industrial process. In the method, dynamic information of the inputs is introduced to build a new network unit. Thus, the dynamic temporal features in difference variable and nonlinear features in sequential data are merged to improve the prediction performance. Effectiveness and superiority of the method are validated through detection of the particle size index for a grinding-classification process by comparing to other popular methods.