Polyester Melt Characteristic Viscosity Prediction Method Under Incomplete Data

Abstract

Characteristic viscosity is a key indicator of the quality of polyester melts, whose accurate prediction can help to identify potential quality problems of polyester melts in advance, adjust the process parameters in time and reduce enterprise losses. Considering the data incompleteness, data time series and high dimensional redundancy of the polyester melt production process, a method is proposed to predict the characteristic viscosity of polyester melt under incomplete data. A missing data generative adversarial nets (MDGAN) with a convolutional neural network discriminator and an attention long short-term memory neural network generator is designed to address the data incompleteness problem caused by the extreme production environment of polyester melts, and the missing data is filled by the adversarial generation mechanism. The extreme gradient boosting-bidirectional gated recurrent unit (XGBoost-BiGRU) is designed to predict the viscosity of polyester melts based on high dimensional redundancy and temporal characteristics prediction. The actual data test results of a polyester fiber manufacturer in Zhejiang show that the filling accuracy of the MDGAN algorithm at different missing rate data sets is better than that of data filling algorithms such as KNN,RF,MICE,and GAIN. The XGBoost-BiGRU characteristic viscosity prediction method has significant advantages over STL-GPR, CAGRU, BiGRU. In combination of MDGAN characteristic viscosity prediction, the method proposed can effectively solve the problem of predicting the characteristic viscosity of polyester melts under incomplete data.

Keywords

• intrinsic viscosity prediction
• incomplete data
• generative adversarial networks (gan)
• recurrent neural networks (rnn)