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For the soft sensor modeling of chemical processes exhibiting pronounced nonlinearity and intricacy, this study proposes a soft sensor model -- TCN-LSTM, which combines Temporal Convolutional Networks (TCN) and Long Short-term Memory Networks (LSTM). TCN-LSTM can extract the spatio-temporal features and dynamic response relationships of the input samples, addressing their time-varying delay issue by discerning dynamic response relationships. To confirm TCN-LSTM's efficacy, we apply it to modeling a soft sensor instance of a debutanizer column. The experiment outcomes demonstrate that TCN-LSTM exhibits superior measurement accuracy over Backpropagation Neural Network (BP), Radial Basis Function Neural Network (RBF), Convolutional Neural Networks (CNN), LSTM, and TCN approaches.

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Application of a Soft Sensor Model Based on TCN-LSTM to Chemical Processes

  • 会议论文

  • 发表日期：

    2025年

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  • Yang Hao,
  • Jun Li

摘要

For the soft sensor modeling of chemical processes exhibiting pronounced nonlinearity and intricacy, this study proposes a soft sensor model -- TCN-LSTM, which combines Temporal Convolutional Networks (TCN) and Long Short-term Memory Networks (LSTM). TCN-LSTM can extract the spatio-temporal features and dynamic response relationships of the input samples, addressing their time-varying delay issue by discerning dynamic response relationships. To confirm TCN-LSTM's efficacy, we apply it to modeling a soft sensor instance of a debutanizer column. The experiment outcomes demonstrate that TCN-LSTM exhibits superior measurement accuracy over Backpropagation Neural Network (BP), Radial Basis Function Neural Network (RBF), Convolutional Neural Networks (CNN), LSTM, and TCN approaches.