第一原理と機械学習の組み合わせによる反応プロセスのバッチ毎特性のモデル化方式

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/09/01

タイトル(英語): Modeling Framework for Batch-dependent Dynamics of Reaction Process by Combining First Principles and Machine Learning

著者名: 石飛　太一（（株）日立製作所），河野　洋平（（株）日立製作所），望月　義則（（株）日立製作所）

著者名(英語): Taichi Ishitobi (Hitachi, Ltd.), Yohei Kono (Hitachi, Ltd.), Yoshinori Mochizuki (Hitachi, Ltd.)

キーワード: バッチプロセス，プロセスモデル，機械学習　　batch process，process model，machine learning

要約(英語): We propose a modeling framework for automating batch processes operation. Batch processes are often controlled by PID controllers, where engineers manually regulate their parameters and temporal patterns of reference signals. Therefore, it takes a long time for optimizing these parameters and temporal patterns. A possible solution for this is to apply so-called Model Predictive Control (MPC) technology to the tuning. Here, batch process dynamics depend on the types of products and of equipment, thereby forcing engineers to construct and maintain multiple models that correspond to the number of combinations of product types and equipment types. Thus, batch process modeling is a time-consuming and complicated task. To solve this problem, we propose a modeling framework; about a modeling target, the part applying commonly and parameters can be decided in advance are constructed by mathematical models, and the part that required experimentation for designing or tuning are constructed by machine learning models. We expect this framework can improving estimation accuracy and suppressing the number of model construction by separating model construction and combining the mathematical and machine learning models. In our simulation, we confirmed that our proposed model can suppress prediction error (RMSE) of reactor temperature under 1K. Furthermore, an optimization algorithm with our model can find a temporal pattern of a reference signal so as to reduce control error of reactor temperature under 1.99K.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.9 （2023） 特集：知能メカトロニクス分野と連携する知覚情報技術

本誌掲載ページ: 934-941 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/9/143_934/_article/-char/ja/