時系列データに対する機械学習モデルによるアルミニウム溶解プロセスの最適化

DOI

書誌事項

タイトル別名
  • Optimization of Aluminum Melting Process by Machine Learning Models for Time Series Data

抄録

<p>  Improving the melting efficiency in aluminum melting furnaces has significant cost and environmental benefits. A large amount of data, including time-series data, have been accumulated from the use of aluminum melting furnaces, but no effective method has been established to utilize these data. In this study, a data-driven model was constructed by combining two machine learning methods : variational autoencoder (VAE) and artificial neural network (ANN). VAE was applied as a model to quantify time series data into 18 latent variables, while ANN was constructed as a model to predict fuel gas consumption from latent variables and other characteristics. In addition, we attempted to optimize aluminum melting process by simulation using the data-driven model.</p><p>  Although the aluminum melting process was complicated, we were able to construct a highly accurate prediction model (R2 = 0.69). Furthermore, the characteristics of the fuel gas flow rate in the case of high melting efficiency were determined by simulation. In fact, the results of modifying the operating conditions of melting furnace based on the knowledge obtained confirmed a significant improvement in melting efficiency. These results indicate that the data analysis method used in this study is effective for process optimization.</p>

収録刊行物

  • 鋳造工学

    鋳造工学 95 (10), 539-545, 2023-10-25

    公益社団法人 日本鋳造工学会

詳細情報 詳細情報について

  • CRID
    1390297979838265728
  • DOI
    10.11279/jfes.95.539
  • ISSN
    21855374
    13420429
  • 本文言語コード
    ja
  • データソース種別
    • JaLC
  • 抄録ライセンスフラグ
    使用不可

問題の指摘

ページトップへ