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 선정해

왔으나 이러한 방식은 전문가의 많은 시간과 비용이 요구되어 비효율적이다. 본 연구는 이를 해결하기위해 머신 러닝

기법을 이용한 밸브 사이즈 및 유형 예측 모델을 개발하였다. Artificial neural network (ANN), Random Forest, XGBoost,

Catboost의 알고리즘을 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(R2

기준, 전체: 0.99216, 액체: 0.98602, 기체: 0.99300. NRMSE 기준, 전체: 0.04072, 액체: 0.04886, 기체: 0.03619)가, 스팀에

선 Random Forest가(R2: 0.99028, NRMSE: 0.03493) 가장 뛰어난 모델임을 확인하였다. 종류의 경우 Catboost가 모

든 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 기

대된다.

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.

Engineering valve, Valve size, Valve type, Data-driven modeling, Machine learning

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