• Proceedings of the Korean Magnestics Society Conference
• /
• 2006.11a
• /
• pp.52-53
• /
• 2006

• Proceedings of the Korean Magnestics Society Conference
• /
• 2007.05a
• /
• pp.91.2-91.2
• /
• 2007

• Proceedings of the Korean Magnestics Society Conference
• /
• 2005.12a
• /
• pp.213-213
• /
• 2005

• Proceedings of the Korean Magnestics Society Conference
• /
• 2005.12a
• /
• pp.215-215
• /
• 2005

• Proceedings of the Korean Magnestics Society Conference
• /
• 2005.12a
• /
• pp.115-115
• /
• 2005

• Proceedings of the Materials Research Society of Korea Conference
• /
• 2005.05a
• /
• pp.180-180
• /
• 2005

• Proceedings of the Korean Magnestics Society Conference
• /
• 2001.04a
• /
• pp.130-131
• /
• 2001

• Proceedings of the Korean Magnestics Society Conference
• /
• 2001.10a
• /
• pp.74-74
• /
• 2001

No Abstract, See Full-text

• Korean Chemical Engineering Research
• /
• v.62 no.3
• /
• pp.214-224
• /
• 2024

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.

• 유체기계공업학회:학술대회논문집
• /
• 2000.12a
• /
• pp.366-372
• /
• 2000

In a power plant, it is generally accepted that a turbine governor system is necessary to control amount of steam supply toward the turbine system. There are many kinds of trouble at this governor system, which is recognized one of the most sensitive systems in the power plant. Especially we have experienced the internal leakage of motorization oil of servo valve. In the study, we investigated the mechanism of an internal leakage such as erosion by foreign materials and corrosion by chemical reaction between chloric healed oil and motorization oil. A precautionary measures is also performed to help the field service engineers.