Deep learning has been applied to the field of soft sensing in process industries. However

the structure and parameters of deep neural network (DNN) have to be tuned manually

which require solid fundamental knowledge about machine learning and rich experiences on parameters tuning. Complicated tuning procedure restricts generalization application of deep learning in chemical industries. A structure and parameters tuning method for DNN soft sensor with little manual intervention was proposed by systematic analysis on selection process of each essential DNN parameter from massive experiments. The presented method could greatly simplify the tuning procedure and offer a reference for engineers to study and use deep learning. Studies on crude-oil distillation and coal gasification process verified effectiveness and generality of the proposed method.