DA-TCN Based Temperature Prediction Algorithm for Heating Furnace

During the operation of the heating furnace, accurate perception and advance prediction of the furnace temperature are of great significance for preventing safety hazards in the petrochemical process and ensuring the safe operation of the heating furnace. Traditional prediction models have low accuracy in predicting actual working condition data. This paper proposes a Dual-Stage Attention-Based Temporal Convolutional Network (short as DA-TCN) prediction model based on the dual-stage attention mechanism (short as DA). The input variable attention mechanism and time attention mechanism are used to weight sequence features, and then the furnace temperature of the heating furnace is predicted based on the temporal convolutional neural network. The effectiveness of the proposed method is validated based on the sampled data. Measured data are used to validate the effectiveness of this method. The prediction results t of the DA-TCN model, CNN-LSTM model, and DA-LSTM model are compared, which shows that the proposed prediction model can effectively improve the prediction accuracy. Compared with the latter two, the root mean square error of the DA-TCN model decreased by 55% and 89%, and the mean absolute error decreased by 74% and 85%, respectively, which further demonstrates the feasibility and effectiveness of the proposed method.