Data-driven soft sensor development based on deep learning technique

• Published in: Journal of process control Vol. 24; no. 3; pp. 223 - 233
• Main Authors: Shang, Chao, Yang, Fan, Huang, Dexian, Lyu, Wenxiang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2014
• Subjects: Data-driven technique; Deep neural network; Nonlinear regression; Soft sensor; ANN; CD; Soft sensor; SVM; Data-driven technique; PCA; MLP; DBN; Deep neural network; CDU; ASTM; DCS; PLS; NNPLS; RBM; Nonlinear regression; RBFN
• ISSN: 0959-1524; 1873-2771
• DOI: 10.1016/j.jprocont.2014.01.012

•Deep learning technique is applied to data-driven soft sensor modeling.•Complex nonlinear correlations between process variables are well extracted.•Deep neural networks yield better representation ability than traditional methods.•Our method and other data-driven approaches are compared through an industrial case.•Deep learning technique is advantageous in the case of massive data. In industrial process control, some product qualities and key variables are always difficult to measure online due to technical or economic limitations. As an effective solution, data-driven soft sensors provide stable and reliable online estimation of these variables based on historical measurements of easy-to-measure process variables. Deep learning, as a novel training strategy for deep neural networks, has recently become a popular data-driven approach in the area of machine learning. In the present study, the deep learning technique is employed to build soft sensors and applied to an industrial case to estimate the heavy diesel 95% cut point of a crude distillation unit (CDU). The comparison of modeling results demonstrates that the deep learning technique is especially suitable for soft sensor modeling because of the following advantages over traditional methods. First, with a complex multi-layer structure, the deep neural network is able to contain richer information and yield improved representation ability compared with traditional data-driven models. Second, deep neural networks are established as latent variable models that help to describe highly correlated process variables. Third, the deep learning is semi-supervised so that all available process data can be utilized. Fourth, the deep learning technique is particularly efficient dealing with massive data in practice.