Soft sensor based on stacked auto-encoder deep neural network for air preheater rotor deformation prediction. (April 2018)

• Record Type: Journal Article
• Authors: Wang, Xiao; Liu, Han
• Is Part Of: Advanced engineering informatics. Volume 36(2018)
• Page Start: 112
• Page End: 119
• Publication Date: 2018-04
• Subjects: Soft sensor -- Deep learning -- Process industry -- Nonlinear regression; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.03.003
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Soft sensors have been widely used in industrial processes over the past two decades because they use easy-to-measure process variables to predict difficult-to-measure ones. Some success has been achieved by the dominant traditional methods of modeling soft sensors based on statistics, such as principal components analysis (PCA) and partial least square (PLS), but such sensors usually become inaccurate and inefficient when processing strong nonlinear data. In this paper, a new soft sensor modeling approach is proposed based on a deep learning network. First, stacked auto-encoders (SAEs) are employed to extract high-level feature representations of the input data. In the process of training each layer of a SAE, the Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) is adopted to optimize the weights parameters. Then, a support vector regression (SVR) is added to predict the target value on the basis of the features obtained from the SAE. To improve the model performance, Genetic Algorithm (GA) is used to obtain the optimal parameters of the SVR. To evaluate the proposed method, a soft sensor model for estimating the rotor deformation of air preheaters in a thermal power plant boiler is studied. The experimental results demonstrate that the soft sensor based on the SAE-SVR algorithm is more effective than the existing methods are.