Final prediction of product quality in batch process based on bidirectional neural network algorithm

Wanshui Li*, Xiang Wang, Qianyuan Feng

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

Based on the analysis of time series characteristics of production process based on common batch process end point quality prediction a predictive model based on bidirectional gated loop neural network is proposed to predict final product quality for unequal interval batch processes. Based on the requirement of forecasting value in actual production, loss function adapted to batch process is constructed, which makes model meet forecasting requirement under guarantee prediction precision, thus obtaining greater production benefit. Two-way gated loop (GRU) neural networks with different loss functions are compared with multi-directional partial least squares (MPLS) neural networks support vector regression SVR and gated cyclic unit neural networks. Results show that bidirectional gated loop neural networks have stronger applicability and higher accuracy.

Original languageEnglish
Article number032091
JournalIOP Conference Series: Earth and Environmental Science
Volume692
Issue number3
DOIs
Publication statusPublished - 25 Mar 2021
Externally publishedYes
Event2020 4th International Conference on Energy Material, Chemical Engineering and Mining Engineering, EMCEME 2020 - Qingdao, China
Duration: 26 Dec 202027 Dec 2020

Keywords

  • Bidirectional cyclic neural networks
  • Depth learning
  • Intermittent process
  • Loss function
  • Time series

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