Prediction of coke oven gas generation based on Seq2Seq

Abstract

Abstract: To achieve accurate prediction of by-product gas in the steel production process, a sequence-to-sequence based deep learning model is constructed here. The hidden state matrix is obtained by calculating the hidden state of the input sequence through the encoder, and then the prediction result is obtained by decoding it through the decoder. Input parameters with high correlation are analyzed according to grey correlation. For the characteristics of unstable fluctuations in steel production, box line plots and hampel filtering are used to process extreme outliers and abrupt changes in the raw data, which are input to the model for single and multi-step prediction respectively. The results show that the prediction performance of the models based on the Seq2Seq structure is improved compared to their corresponding single models for single-step prediction, in which the LSTM2GRU model performs best for peak and valley fits. The LSTM2GRU model can effectively reduce the declining trend of model performance in multi-step prediction. Compared with the LSTM2LSTM model and the GRU2GRU model on two data sets, it is found that the root mean square error of the LSTM2GRU model has decreased by 5.3%, 5.6% and 9%, 7.7%, respectively. The mean absolute error of the LSTM2GRU decreased by 7.3%, 7% and 9.7%, 7.8%, respectively. Therefore, the LSTM2GRU model is more suitable for forecasting long-scale time series compared to other models. In addition, the introduction of the GRU structure into the model has improved prediction accuracy and reduced prediction time.

Key words: gas prediction, neural network, deep learning, Seq2Seq, grey relational analysis

CLC Number:

TE09

WANG Wen-ting, LIU Shu-jun, ZHANG Yao-cong, DU Xiao-ze, XU Tong. Prediction of coke oven gas generation based on Seq2Seq[J]. Journal of Lanzhou University of Technology, 2023, 49(5): 50-58.

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