Applied Chemistry for Engineering (공업화학)

The Korean Society of Industrial and Engineering Chemistry (한국공업화학회)
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Development of Machine Learning Model for Predicting Distillation Column Temperature

증류공정 내부 온도 예측을 위한 머신 러닝 모델 개발

  • Kwon, Hyukwon (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • Oh, Kwang Cheol (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • Chung, Yongchul G. (School of Chemical & Biomolecular Engineering, Pusan National University) ;
  • Cho, Hyungtae (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • Kim, Junghwan (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology)
  • 권혁원 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 오광철 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 정용철 (부산대학교 화공생명공학부) ;
  • 조형태 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 김정환 (한국생산기술연구원 친환경재료공정연구그룹)
  • Received : 2020.07.24
  • Accepted : 2020.08.26
  • Published : 2020.10.12
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Abstract

In this study, we developed a machine learning-based model for predicting the production stage temperature of distillation process. It is necessary to predict an accurate temperature for control because the control of the distillation process is done through the production stage temperature. The temperature in distillation process has a nonlinear complex relationship with other variables and time series data, so we used the recurrent neural network algorithms to predict temperature. In the model development process, by adjusting three recurrent neural network based algorithms, and batch size, we selected the most appropriate model for predicting the production stage temperature. LSTM128 was selected as the most appropriate model for predicting the production stage temperature. The prediction performance of selected model for the actual temperature is RMSE of 0.0791 and R2 of 0.924.

본 연구에서는 증류공정의 제품 생산단 온도 예측을 위한 머신러닝 기반 모델을 개발하였다. 증류공정의 제어는 제품 생산단의 온도를 통해 이루어지고 있어 제어를 위해 정확한 온도 예측이 필요하다. 증류공정에서 온도는 다양한 변수들과 복잡한 비선형의 관계를 형성하고 있으며 시계열 데이터의 특성이 있어 이를 예측하기 위해 순환신경망 기반 알고리즘을 이용하였다. 모델 개발 과정에서 적절한 예측 알고리즘을 선정하기 위해 세 가지 순환신경망 기반 알고리즘과 배치 사이즈 조절하여 제품 생산단 온도를 예측하기 위한 가장 적저한 모델을 선정하였다. LSTM128 모델이 제품 생산단 온도를 예측하기 위한 가장 적절한 모델로 선정되었다. 선정된 모델을 활용하여 실제 공정 운전데이터에 적용한 결과 RMSE 0.0791, R2 0.924의 성능을 보였다.

Keywords

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  1. 머신 러닝과 데이터 전처리를 활용한 증류탑 온도 예측 vol.59, pp.2, 2020, https://doi.org/10.9713/kcer.2021.59.2.191