SSP-WGAN-Based Data Enhancement and Prediction Method for Cement Clinker f-CaO

Aiming at the problem of low prediction accuracy of traditional prediction models due to the limited labeled sample data and the imbalance of multitimescale sample data in the cement production process, a cement clinker-free calcium oxide (f-CaO) data enhancement and prediction model based on semisu...

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Bibliographic Details
Published in:IEEE sensors journal 2022-12, Vol.22 (23), p.22741-22753
Main Authors: Hao, Xiaochen, Dang, Hui, Zhang, Yuxuan, Liu, Lin, Huang, Gaolu, Zhang, Yifu, Liu, Jinbo
Format: Article
Language:English
Subjects:
Accuracy
Calcium oxide
Cement
Clinker
Data models
Feature extraction
Free calcium oxide (f-CaO) in cement clinker
generating adversarial networks
Generative adversarial networks
Generators
Lime
Model accuracy
Prediction models
Predictive models
Production
semisupervised data enhancement
soft sensing
Time series
Time series analysis
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Summary:Aiming at the problem of low prediction accuracy of traditional prediction models due to the limited labeled sample data and the imbalance of multitimescale sample data in the cement production process, a cement clinker-free calcium oxide (f-CaO) data enhancement and prediction model based on semisupervised prediction Wasserstein generative adversarial networks (SSP-WGANs) is proposed in this article. The model is constructed by WGAN and a prediction model. In the generator of WGAN, the traditional noise input is replaced by time-series matrices composed of related variables affecting the f-CaO content of cement clinker. The generator maps the input high-dimensional time-series data into low-dimensional labeled values through its internal convolutional layer, which can fill in the missing labeled values of the input unlabeled samples. The generated labels are spliced with the related variables affecting the f-CaO content of clinker according to the timescale relationship and are uniformly sent to the discriminator with the real sample pairs for discrimination, thus eliminating the influence of multiple timescales. The generated labeled data are matched with the unlabeled data and used to expand the training set of the prediction model composed of CNN-GRU, which significantly improves the prediction accuracy of the model. The results show that the SSP-WGAN model with data enhancement has higher accuracy and stronger robustness.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3211007