Sejong University · Department of Energy Resources & Geosystems Engineering

📢 News & Updates

📰 Awards

🏆  SMART-X Lab Wins 1st Place at the 2025 Mine-Tech Festa  (2025.09.03)

We are proud to announce that SMART-X Lab has achieved first place (Gold Prize) at the prestigious 2025 Mine-Tech Festa, held on September 3, 2025 at the Korea Mine Reclamation and Mineral Resources Corporation (KOMIR). This national competition recognizes outstanding innovation in the mining and resource industries.

Our winning team, GeoInpainter, presented a groundbreaking technology titled:

"High-Precision Digital Terrain Modeling of Open-Pit Mines Powered by AI Inpainting"

This system utilizes advanced deep learning algorithms (YOLOv8, SAM, LaMa, etc.) to automatically detect and remove large machinery from drone imagery. It then reconstructs the terrain using inpainting and generates a high-precision, equipment-free DEM (Digital Elevation Model). The entire process is fully automated via a user-friendly GUI platform, enabling non-experts to produce clean and accurate DEMs for mining applications.

🧠 Technological Impact

• Removes equipment-induced terrain distortions from drone imagery

• Enhances the accuracy and reliability of open-pit DEMs

• Facilitates digital twin implementation in smart mining

• Applicable to slope stability analysis, blasting design, and volume estimation
  
  

🥉 Additional Recognition: 3rd Place for Team LoRa

Another SMART-X Lab team, LoRa, was also selected as a finalist and received the Bronze Prize (3rd Place) for their innovative system titled:

"LoRa-based Real-Time Truck Monitoring and Production Management System for Open-Pit Mines"

This low-power, cost-effective system integrates LoRa, GPS, BLE, and Starlink technologies to enable real-time truck tracking and production data monitoring in mining environments with limited communication infrastructure. The system has been successfully field-tested at a tin mine in Mongolia, demonstrating its practical value and scalability for resource-constrained mining sites.

🎓 Recognition for Research Leadership

In recognition of this achievement, Professor Yosoon Choi, director of SMART-X Lab, received the Ministerial Award for Outstanding Research Supervision from the Ministry of Trade, Industry and Energy. This marks a remarkable three-year winning streak for Professor Choi, whose teams have consecutively led innovations in mine digitalization since 2023.

📌 About Mine-Tech Festa

The Mine-Tech Festa is Korea’s premier innovation contest in the mining sector, featuring top university-industry teams across fields such as exploration, resource processing, mine safety, sustainability, and digital mining.

📰 New Publications

📰 New Publication Alert (2025.10.18)
 

Comparative performance of monofacial and bifacial PV–wind–flywheel systems for hydrogen refueling stations

Prof. Yosoon Choi and Dr. Shubhashish Bhakta have published a new study in Energy Conversion and Management (Elsevier, 2026), titled:
“Comparative performance of monofacial and bifacial PV–wind–flywheel systems for hydrogen refueling stations.”

💡 What’s the innovation?
This study introduces a novel hybrid renewable energy system integrating photovoltaic (PV), wind, and flywheel (FW) technologies to power hydrogen refueling stations (HRSs).
The research uniquely compares monofacial and bifacial PV configurations under real Korean climate and grid conditions, assessing their impact on hydrogen cost, energy efficiency, and CO₂ mitigation.

The study:

• Develops an integrated SAM–HOMER Pro simulation framework for dynamic modeling of bifacial PV–wind–flywheel hybrid systems.
  
  

• Quantifies how bifacial PV modules enhance energy yield and reduce system-level hydrogen production cost.
  
  

• Analyzes grid-tied and standalone HRS configurations, evaluating techno-economic performance and environmental benefits.
  
  

🌍 Why it matters
Hydrogen refueling stations are vital to achieving carbon-free mobility, yet they face challenges from renewable intermittency and energy cost fluctuations.
By integrating bifacial PV and flywheel storage, this research:

• Reduces the Levelized Hydrogen Cost (LHC) to 3.62 $/kg—1.9% lower than the monofacial system.
  
  

• Lowers electricity cost (LEC) by 2.7% and improves overall system efficiency by nearly 5%.
  
  

• Demonstrates an annual CO₂ reduction of 342,495 kg, highlighting strong environmental potential for renewable-based HRS deployment.
  
  

🧪 How was it validated?
The team simulated both monofacial and bifacial systems using real meteorological and tariff data from Korea, considering hourly variations in irradiance and wind speed.
Results showed that:

• The bifacial PV–wind–flywheel configuration generated 4.87% more PV energy and maintained >94% renewable fraction.
  
  

• Hydrogen output reached 22,278 kg/year, fully meeting station demand.
  
  

• Flywheel storage improved short-term stability and grid exchange management, enabling continuous hydrogen production.
  
  

🔗 Publication Details
Choi, Y.*, Bhakta, S. (2026). Comparative performance of monofacial and bifacial PV–wind–flywheel systems for hydrogen refueling stations.
Energy Conversion and Management, 348, 120634.
https://doi.org/10.1016/j.enconman.2025.120634 (SCIE)

📰 New Publication Alert (2025.09.25)

Performance prediction of wind energy-driven green hydrogen production: A case study based on optimized machine learning models for abandoned mine sites.

Prof. Yosoon Choi and his team have published a new study in International Journal of Hydrogen Energy (Elsevier, 2025), titled:
“Performance prediction of wind energy-driven green hydrogen production: A case study based on optimized machine learning models for abandoned mine sites.”

💡 What’s the innovation?

This research pioneers a machine learning–based framework for predicting the performance of green hydrogen production systems powered by wind energy. The study:

• Employs multiple machine learning models—including Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Machines (GBM)—to forecast hydrogen production efficiency.

• Uses real wind speed data combined with electrolysis system performance metrics to build robust prediction models.

• Identifies key input variables (wind characteristics, system design parameters, and operational conditions) that most strongly influence hydrogen yield.

🌍 Why it matters

Wind-to-hydrogen systems are central to a sustainable energy transition, but their performance is highly variable due to wind intermittency and system nonlinearities. By applying advanced machine learning techniques, this study:

• Enhances the accuracy of hydrogen production forecasts.

• Enables optimized planning and operation of renewable-driven hydrogen plants.

• Contributes to improving the economic feasibility and scalability of green hydrogen as a cornerstone of carbon-neutral energy systems.

🧪 How was it validated?

The framework was tested using case study data from real-world wind energy systems coupled with electrolysis units. Results showed that:

• Gradient Boosting achieved the highest prediction accuracy among the tested models.

• Feature importance analysis revealed wind speed variability and system load as dominant factors for hydrogen output.

• The machine learning–driven predictions significantly outperformed conventional linear regression approaches.

🔗 Publication Details

Choi, Y.*, Bhakta, S. (2025) Performance prediction of wind energy-driven green hydrogen production: A case study based on optimized machine learning models for abandoned mine sites. International Journal of Hydrogen Energy 179, 151646. https://doi.org/10.1016/j.ijhydene.2025.151646 (SCIE)

📰 New Publication Alert (2025.08.13)

Profit-driven Scheduling of CO₂ Storage and Reuse in Salt Caverns for Solar-to-X Applications

Prof. Yosoon Choi and his team have published a novel study in Solar Energy (Elsevier, 2025), titled:
“Profit-driven scheduling of CO₂ storage and reuse in salt caverns for Solar-to-X applications: A two-stage optimization approach.”

💡 What’s the innovation?

This research proposes a profit-maximizing scheduling framework for the cyclic storage and reuse of CO₂ in underground salt caverns, specifically tailored for solar-to-X systems. The framework:

• Integrates CO₂ storage dynamics with Power-to-Gas (PtG) and methanation processes.

• Optimizes operational decisions considering electricity market prices, CO₂ availability, and cavern capacity.

• Balances economic returns with energy system stability in renewable-rich grids.

🌍 Why it matters

The intermittency of solar power creates challenges for continuous synthetic methane production. By strategically storing captured CO₂ during low-demand or low-price periods, and reusing it when solar generation is high, this approach:

• Enhances the economic feasibility of Solar-to-X pathways.

• Supports long-term CO₂ utilization and sequestration strategies.

• Contributes to carbon neutrality goals through integrated energy–carbon management.

🧪 How was it validated?

The proposed scheduling model was tested using real-world data from electricity markets and CO₂ supply scenarios. Key findings include:

• Profit improvements compared to conventional operation strategies.

• Increased utilization of renewable power for CO₂-based synthetic fuel production.

• Operational flexibility enabling both grid services and low-carbon fuel output.

🔗 Publication Details

Khavari, F., Choi, Y.* (2025) Profit-driven scheduling of CO2 storage and reuse in salt caverns for Solar-to-X applications: A two-stage optimization approach. Solar Energy 300, 113855. https://doi.org/10.1016/j.solener.2025.113855

📰 New Publication Alert (2025.07.14)

Database-Driven Solar Parking Optimization for SEVs
Prof. Yosoon Choi and his team have published a cutting-edge paper in Solar Energy Materials & Solar Cells (Elsevier, 2025), titled: 
“Design and implementation of a solar access database for optimizing parking of solar electric vehicles.”

🚗 What’s the innovation?
This study introduces a Solar Access database (SA-DB) that:

• Quantifies real-time and seasonal solar access across 1,388 parking bays,

• Eliminates the need for on-site 360° imaging or specialized devices,

• Provides optimal SEV parking recommendations through a mobile app,

• Enables time- and season-specific parking decisions to maximize solar energy gain.

🌞 Why it matters
SEVs spend most of their time parked, making solar access at the parking spot a crucial factor for energy efficiency. This work:

• Bridges data-driven GIS modeling with solar PV integration,

• Enhances smart mobility by informing SEV users where and when to park for maximum charge,

• Assists general drivers in selecting shaded spots to reduce vehicle cabin temperatures in summer.

🧪 How was it validated?
The SA-DB was constructed using fisheye sky imagery and QGIS, covering all parking lots at the Daeyeon Campus of Pukyong National University. It was further integrated into a mobile app that slashes solar analysis time from 125s to just 5s, enabling real-time decision-making without additional hardware.

🔗 Publication Details
Hong, J., & Choi, Y. (2025). Design and implementation of a solar access database for optimizing parking of solar electric vehicles. Solar Energy Materials and Solar Cells, 293, 113841. https://doi.org/10.1016/j.solmat.2025.113841

📰 New Publication Alert (2025.06.28)

Advanced Solar Parking Guidance Using VR and Solar Forecasting

Prof. Yosoon Choi and his team have published an innovative paper in Applied Energy (Elsevier, 2025), titled:
“Advanced parking assistance system for solar electric vehicles using 360° virtual reality imaging and real-time solar radiation forecasting.”

🚗 What’s the innovation?
The team developed an AI-powered smart parking assistant for solar electric vehicles (SEVs) that:

• Visualizes real-time solar conditions via 360° virtual reality imaging.

• Predicts solar radiation levels using advanced forecasting models.

• Guides drivers to optimal parking spots to maximize solar charging.

🌞 Why it matters
This system bridges mobility and renewable energy, solving a key challenge for SEVs: where to park for the best solar gain. The proposed framework integrates:

• Real-time weather data,

• Solar simulation based on shadow detection, and

• A user-friendly 3D interface for intuitive decision-making.

🧪 How was it validated?
The system was tested in an urban outdoor parking lot using UAV aerial imagery and virtual simulation tools. The study demonstrated how real-time solar optimization could significantly improve SEV energy self-sufficiency.

🔗 Publication Details
Hong, J., & Choi, Y. (2025). Advanced parking assistance system for solar electric vehicles using 360° virtual reality imaging and real-time solar radiation forecasting. Applied Energy, 355, 123064. https://doi.org/10.1016/j.apenergy.2024.123064

📰 New Publication Alert (2025.06.27)

A Novel KPI Framework for Haulage System Evaluation in Open-Pit Mining

We are pleased to announce the latest publication by Prof. Yosoon Choi and collaborators in the Ain Shams Engineering Journal (Elsevier, 2025). The study, titled "A novel integrated key performance indicator for evaluating open-pit mine haulage systems: application of GMG standards", proposes a groundbreaking approach to performance evaluation in open-pit mining.

🔍 What’s it about?
The research introduces an integrated KPI framework that combines nine individual metrics—based on standards by the Global Mining Guidelines Group (GMG)—into a single, comprehensive performance indicator. This methodology enhances decision-making by offering a more intuitive and actionable assessment of system performance.

📍 Where was it applied?
The framework was validated using real haulage operation data from Sungshin Cement’s limestone mine in South Korea. The study analyzed truck and loader performance across availability, utilization, and effectiveness, revealing key inefficiencies and offering practical optimization strategies.

💡 Why it matters
This integrated KPI simplifies complex operational data into a unified metric, enabling faster issue diagnosis, better equipment management, and smarter planning. It is a valuable tool for advancing sustainability and efficiency in the mining sector.

📘 Citation
Park, S., Jung, D., & Choi, Y. (2025). A novel integrated key performance indicator for evaluating open-pit mine haulage systems: application of GMG standards. Ain Shams Engineering Journal, 16, 103589. https://doi.org/10.1016/j.asej.2025.103589

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