Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2024 , Vol. 58 >Issue 4: 534 - 544

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.011

Electronic Information and Electrical Engineering

Polyester Melt Characteristic Viscosity Prediction Method Under Incomplete Data

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  • 1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    2. Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China
    3. Xinfengming Group Huzhou Zhongshi Technology Co., Ltd., Huzhou 313000, Zhejiang, China

Received date: 2023-01-10

  Revised date: 2023-05-22

  Accepted date: 2023-06-01

  Online published: 2023-06-08

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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.

Key words： intrinsic viscosity prediction; incomplete data; generative adversarial networks (GAN); recurrent neural networks (RNN)

Cite this article

BI Jinmao, ZHANG Peng, ZHANG Jie, ZHAO Chuncai, CUI Li . Polyester Melt Characteristic Viscosity Prediction Method Under Incomplete Data[J]. Journal of Shanghai Jiaotong University, 2024 , 58(4) : 534 -544 . DOI: 10.16183/j.cnki.jsjtu.2023.011

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