Korean Chemical Engineering Research

  • 제62권3호
  • /
  • Pages.214-224
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  • 2024
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  • 0304-128X(pISSN)
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  • 2233-9558(eISSN)
한국화학공학회 (The Korean Institute of Chemical Engineers)
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머신 러닝을 이용한 밸브 사이즈 및 종류 예측 모델 개발

Data-driven Modeling for Valve Size and Type Prediction Using Machine Learning

  • 김찬호 (연세대학교 화공생명공학과) ;
  • 최민식 (한국생산기술연구원 친환경재료공정연구그룹) ;
  • 주종효 (연세대학교 화공생명공학과) ;
  • 이아름 (삼성 E&A) ;
  • 윤건 (삼성 E&A) ;
  • 조성호 (삼성 E&A) ;
  • 김정환 (연세대학교 화공생명공학과)
  • Chanho Kim (Department of Chemical and Biomolecular Engineering, Yonsei University) ;
  • Minshick Choi (Green Materials and Processes R&D Group, Korea Institute of Industrial Technology) ;
  • Chonghyo Joo (Department of Chemical and Biomolecular Engineering, Yonsei University) ;
  • A-Reum Lee (Samsung E&A Co., Ltd.) ;
  • Yun Gun (Samsung E&A Co., Ltd.) ;
  • Sungho Cho (Samsung E&A Co., Ltd.) ;
  • Junghwan Kim (Department of Chemical and Biomolecular Engineering, Yonsei University)
  • 투고 : 2023.12.07
  • 심사 : 2024.05.22
  • 발행 : 2024.08.01
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초록

밸브는 유량과 압력 조절 등의 중요한 역할을 수행하며, 적절한 밸브 사이즈와 유형 선택이 필요하다. Engineering Procurement Construction (EPC) 산업에선 밸브 사이즈 계수(Cv)의 수식적 계산을 바탕으로 사이즈와 유형을 선정해왔으나 이러한 방식은 전문가의 많은 시간과 비용이 요구되어 비효율적이다. 본 연구는 이를 해결하기위해 머신 러닝기법을 이용한 밸브 사이즈 및 유형 예측 모델을 개발하였다. Artificial neural network (ANN), Random Forest, XGBoost, Catboost의알고리즘을 적용하여 모델들을 개발하였으며, 평가 지표로는 사이즈 예측에는 Normalized root mean squared error (NRMSE)와 R2를, 종류 예측에는 F1 score를 적용하였다. 또한, 유체 상에 따른 영향을 확인하고자 유체 전체, 액체, 기체, 스팀의 4개의 데이터 세트로 사례 연구를 진행하였다. 연구 결과, 사이즈의 경우 전체, 액체, 기체에선 Catboost(R2기준, 전체: 0.99216, 액체: 0.98602, 기체: 0.99300. NRMSE 기준, 전체: 0.04072, 액체: 0.04886, 기체: 0.03619)가, 스팀에선 Random Forest가(R2: 0.99028, NRMSE: 0.03493) 가장 뛰어난 모델임을 확인하였다. 종류의 경우 Catboost가 모든 데이터에서 가장 높은 성과를 제시하였다(F1 score 기준, 전체: 0.95766, 액체: 0.96264, 기체: 0.95770, 스팀: 1.0000). 본 연구에서 제안한 모델들을 적용할 경우, 주어진 조건에 따른 밸브 선택을 도와 의사결정 속도를 높여줄 것으로 기대된다.

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R2 score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R2, total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R2: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

키워드

과제정보

본 논문은 "AI 지능화기반 엔지니어링 예측 모델 개발(2023-11-0458)"의 지원으로 수행한 연구입니다.

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