1. Multiscale variational autoencoder regressor for production prediction and energy saving of industrial processes.
- Author
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Han, Yongming, Wang, Yue, Chen, Zhiwei, Lu, Yi, Hu, Xuan, Chen, Liangchao, and Geng, Zhiqiang
- Subjects
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MANUFACTURING processes , *BACK propagation , *RADIAL basis functions , *MACHINE learning , *CARBON emissions , *CARBON offsetting - Abstract
[Display omitted] • Novel multiscale variational autoencoder regressor is proposed. • The multiscale CNN module can extract multiscale information. • Establishing prediction models for ethylene and propylene production for energy structure optimization. • The prediction accuracy of the proposed model on ethylene and propylene data can reach around 95% and 96%, respectively. • The proposed model can effectively increase propylene and ethylene production and reduce carbon emissions. As modern industries increase in scale and integration, the industrial process data show more complex dynamic time variability. To solve the dynamic time-varying problem of industrial process data, a novel multiscale variational autoencoder (MSVAE) based Regressor (REG) (MSVAE-REG) is proposed for production prediction and energy saving. The encoder of the MSVAE is constructed by multiscale convolutional neural network (MSCNN) to extract the multiscale temporal dynamic features, including the overall and local trend features in the Gaussian latent space. Then, the decoder of the MSVAE recovers the input data to enhance the dynamic adaptability of multiscale features. Moreover, the REG utilizes the gated recurrent unit (GRU) to build a dynamic relationship between multiscale temporal dynamic features and the predicted output. Finally, the proposed model is verified on the propylene and ethylene production datasets. Compared with the back propagation (BP) network, the extreme learning machine (ELM), the radial basis function (RBF), the convolutional neural network (CNN) and the variational autoencoder (VAE), the MSVAE-REG achieves state-of-the-art results, with the prediction accuracy respectively reaching about 95% and 96%, providing a new judgment basis for optimizing the production process and saving energy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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