Trend-GRU Model based Time Series Data Prediction in Melt Transport Process

Temperature and pressure are two important performance indicators during the melt transport of polyester fiber production, which can affect the overall properties of the melt. Therefore, the accurate prediction of these two indicators is crucial for the control of melt properties. This paper proposes a data prediction model, Trend-GRU (Gated Recurrent Unit), which can extract the feature of unstable change of the melt data. On the premise that the model is not over-complicated, a new structure is designed to extract the feature of unstable change to improve the prediction accuracy. Two data sets on temperature and pressure collected from the actual production process of a spinning factory are used for comparative experiments. The results show that the accuracy of the data prediction of the proposed model is better than the original one.