Automatic hyper-parameter tuning for soft sensor modeling based on dynamic deep neural network

Deep learning has been proposed for soft sensor modeling in process industries. However, conventional deep neural network (DNN) is a static network and thereby can not embrace evident dynamics in processes. Motivated by nonlinear autoregressive with exogenous input (NARX) model and neural nets based dynamic modeling, a dynamic network called NARX-DNN is put forward by further utilizing historical process samples and quality samples in a period of time. A modified hyper-parameter tuning method is proposed to choose optimal hyper-parameters of NARX-DNN with little manual intervention, which automatizes the training procedure and reduces computational cost. The quality prediction error of validation data is interpreted from different aspects, and the most appropriate delay of historical data can be determined automatically. The effectiveness of the proposed method is validated by case studies on a sulfur recovery unit and a debutanizer column. As training, validation and test data sets are selected by the original orders of data samples, the accurate prediction results of NARX-DNN demonstrate its ability in dealing with operation condition changes which are common in real processes.