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Energy AI & Computational Science Laboratory

The Energy AI and Computational Science Center conducts collaborative research with other departments to accelerate and improve the quality of research and development in the energy field. To cover various aspects of energy research, the center is divided into specialized areas, including atomistic computational simulation, process systems and engineering, computational fluid dynamics (CFD), and AI & Data Science, and is responsible for areas ranging from reaction mechanisms of materials to economic feasibility analysis, modeling of hydrodynamic phenomena, and data analysis using artificial intelligence. This integrated and multidisciplinary approach is driving research and innovation in the energy sector to the next level.

Representative performance

  • 01 Development of AI-Based Lifespan Prediction Technology and Research Data Collection for Commercial Lithium Battery Cells and Materials
  • 02 New Materials Design by the First-Principles Calculation
  • 03 Techno-economic analysis of green hydrogen system based on multi-objective optimization of economics and productivity
  • 04 Design of the demo plant for MAB based CO2 capture process
  • 05 Pilot Scale Validation by Computational Fluid Dynamics
  • 06 Computational Fluid Dynamics Based Optimization on Dry CO2 Capture Pilot Plant
  • 07 Photovoltaic Mass Production In-line Quality Control System
  • 08 Web Platform Development for Computational Materials Science
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Major research fields

  • Atomic Scale Computational Materials Science: New Materials Design and Mechanism Study
  • Process Systems & Engineering: Large-scale Process Concept Design, Simulation, Optimization and Techno-Economic Analysis
  • Computational Fluid Dynamics: Heat & Flow/Chemical Reactions/Particle Behavior Dynamic Simulation and System optimization
  • AI·Data Science: Data Driven Research Acceleration and Insight Derivation

Major research results

  • Techno-economic analysis of green hydrogen production system based on renewable energy sources
  • Conceptual design and techno-economic assessment for the sorption enhanced blue hydrogen process with low CO2 emission
  • Determination of kinetic factors of CO2 mineralization reaction for reducing CO2 emissions in cement industry and verification using CFD modeling
  • Development of an AI model for predicting the lifespan and remaining capacity of commercial lithium battery cells, coupled with extensive data collection and database construction focused on key research aspects of battery cells and materials
  • Photovoltaic potential estimation using machine learning with urban buildings data
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