Data-driven modeling of multimode chemical process: Validation with a real-world distillation column

Yeongryeol Choi, Bhavana Bhadriaju, Hyungtae Cho, Jongkoo Lim, In Su Han, Il Moon, Joseph Sang Il Kwon, Junghwan Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Real-world industrial processes frequently operate in different modes such as start-up, transient, and steady-state operation. Since different operating modes are governed by different process dynamics, deriving a single data-driven model representing the entire operation of such multimode processes is not a viable option. A reasonable solution to this problem is to develop a separate model for each operating mode, which requires the extraction of data for each operating mode from raw data. Based on this viewpoint, this work develops a data-driven modeling approach using clustering and featuring selection techniques to improve the quality of raw data and develop a predictive model for a multimode industrial process. In particular, the developed method focuses on training a steady-state predictive model as monitoring steady-state conditions is crucial for achieving the desired product quality. Firstly, K-means clustering is performed to extract data describing the steady-state operation mode from the available raw data. Next, feature selection is applied to the clustered data using Pearson's correlation coefficient to identify input features relevant to target features. Finally, an LSTM model is trained using the clustered data and identified features to predict the steady-state operation. The validity and effectiveness of the developed method are demonstrated using a real-world 2,3-Butanediol distillation process dataset.

Original languageEnglish
Article number141025
JournalChemical Engineering Journal
Volume457
DOIs
Publication statusPublished - 2023 Feb 1

Bibliographical note

Funding Information:
This work was supported by the Korean Institute of Industrial Technology within the framework of the project: Development of AI Platform Technology for Smart Chemical Process (grant number: JH-22–0004) , the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (grant number: P0017304 , Human Resource Development Program for Industrial Innovation), Texas A&M Energy Institute, USA , and the Artie McFerrin Department of Chemical Engineering, USA .

Publisher Copyright:
© 2022 The Author(s)

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Environmental Chemistry
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

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