Abstract
This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network (DBN). The important quality variable melt index of polypropylene is hard to measure in industrial processes. Lack of online measurement instruments becomes a problem in polymer quality control. One effective solution is to use soft sensors to estimate the quality variables from process data. In recent years, deep learning has achieved many successful applications in image classification and speech recognition. DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture. It can meet the demand of modelling accuracy when applied to actual processes. Compared to the conventional neural networks, the training of DBN contains a supervised training phase and an unsupervised training phase. To mine the valuable information from process data, DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation. Selection of DBN structure is investigated in the paper. The modelling results achieved by DBN and feedforward neural networks are compared in this paper. It is shown that the DBN models give very accurate estimations of the polymer melt index.
Highlights
Much work on soft sensors in the research area of process control has done in the past few decades
deep belief network (DBN) models for the on-line inferential estimation of the polymer melt index in an industrial polymerization process are developed in this paper
The “unlabeled” process data, which ware useless to the conventional neural network models, can be used in the unsupervised training stage of DBN
Summary
Much work on soft sensors in the research area of process control has done in the past few decades. Deep belief network (DBN) is one kind of the most well-known data-driven modelling techniques based on deep learning It shows strong generalization capability in modelling highly nonlinear processes. By using deep learning techniques, large amount of industrial process data samples without pre-existing labels can be used by DBN models in the unsupervised training phase These input data are useless for training the conventional feed-forward neural networks which just use supervised training. These process data samples help the DBN model in adjusting the weights in a desirable region. When a feedforward neural network with more than three layers is training by backpropagation, the model always suffers from the problem of poor generalization This modelling technique cannot meet the demand of the accuracy of the estimation.
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