Korean Chemical Engineering Research

The Korean Institute of Chemical Engineers (한국화학공학회)
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Development of Machine Learning-Based Platform for Distillation Column

증류탑을 위한 머신러닝 기반 플랫폼 개발

  • Oh, Kwang Cheol (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • Kwon, Hyukwon (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • Roh, Jiwon (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • Choi, Yeongryeol (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • Park, Hyundo (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • 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)
  • 오광철 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 권혁원 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 노지원 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 최영렬 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 박현도 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 조형태 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 김정환 (한국생산기술연구원 친환경재료공정연구그룹)
  • Received : 2020.03.02
  • Accepted : 2020.05.30
  • Published : 2020.11.01
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Abstract

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

본 연구는 증류탑 분리공정 시스템 최적화를 위하여 인공지능 머신러닝이 적용된 소프트웨어 플랫폼을 개발하였다. 증류탑 분리공정은 석유화학 산업의 대표적이고 핵심적인 공정이다. 하지만 다양한 운전조건과 연속식공정 특성으로 인하여 안정적인 운전이 어려우며 운전자 숙련도에 의하여 공정효율에 차이가 발생된다. 이를 해결하기 위하여 이론적 시뮬레이션을 활용한 제어방법이 개발되어 사용되고 있지만 특수하거나 복잡한 반응이 포함된 공정에는 적용이 어려우며, 거대한 시스템에 대하여 분석이 이루어질 경우 계산비용 증대로 인하여 실시간 제어와 연동이 어려운 한계점을 지니고 있다. 따라서 본 연구에서는 이러한 문제점을 해결하기 위하여 머신러닝을 기반으로 한 경험적 시뮬레이션 모델을 개발하고 이를 통하여 최적의 공정운영방법을 제시하고자 한다. 경험적 시뮬레이션 개발은 실제 공정에서 수집된 빅 데이터, 데이터마이닝을 통한 특성추출, 공정을 대표하는 데이터 선별, 화학공정 특성에 맞는 모델 선정으로 이루어졌으며, 현장검증 및 테스트를 통하여 증류탑 분리공정 플랫폼이 개발되었다. 최종적으로 개발된 플랫폼을 통하여 운전 조작변수의 예측이 가능하며, 최적화된 운전조건을 제공하여 효율적인 공정운영을 달성할 수 있다. 본 논문은 머신러닝 기법을 화학공정에 적용한 기초연구로서 이후 다양한 공정에 적용하여 4차 산업의 스마트 팩토리의 초석이 되어 널리 활용될 수 있을 것이라 판단된다.

Keywords

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