A5025785632- Hybrid Renewable Energy Systems
- Energy Load and Power Forecasting
- Solar Radiation and Photovoltaics
- Process Optimization and Integration
- Energy and Environment Impacts
- Integrated Energy Systems Optimization
- Biofuel production and bioconversion
- Catalysts for Methane Reforming
- Microbial Metabolic Engineering and Bioproduction
- Anaerobic Digestion and Biogas Production
- Electric Vehicles and Infrastructure
- Environmental Impact and Sustainability
- Extraction and Separation Processes
- Wastewater Treatment and Nitrogen Removal
- Energy, Environment, and Transportation Policies
- Fault Detection and Control Systems
- Electric Power System Optimization
- Carbon Dioxide Capture Technologies
- Catalysis for Biomass Conversion
- Thermochemical Biomass Conversion Processes
- Sustainable Industrial Ecology
- Energy Efficiency and Management
- Advanced Control Systems Optimization
- Adsorption and Cooling Systems
- Enzyme Catalysis and Immobilization
Gyeongsang National University
2020-2025
Technical University of Denmark
2018-2021
Kyung Hee University
2017-2019
Pohang University of Science and Technology
2016-2017
This study aims to demonstrate the application of deep learning quantitatively describe long-term full-scale data observed from wastewater treatment plants (WWTPs) perspectives process modeling, analysis, and forecasting modeling. Approximately, 750,000 measurements including influent flow rate, air temperature, ammonium, nitrate, dissolved oxygen, nitrous oxide (N2O) collected for more than a year Avedøre WWTP located in Denmark are utilized develop neural network (DNN) through supervised...
Correction for ‘Environmentally friendly process design furan-based long-chain diester production aiming bio-based lubricants’ by Hye Jin Lee et al. , Green Chem. 2025, 27 607–622, https://doi.org/10.1039/D4GC04191G.
Abstract Monte Carlo (MC) methods employ a statistical approach to evaluate complex mathematical models that lack analytical solutions and assess their uncertainties. To this end, techniques such as Markov chain (MCMC), bootstrap, sequential MC repeat the same operations over specified range of conditions. Consequently, both frequentist Bayesian approaches are computationally intensive, depending on problem formulation. Improving sampling identifying sources error reduce computational demand...
This study proposed an optimal hybrid renewable energy system (HRES) to sustainably meet the dynamic electricity demand of a membrane bioreactor. The model-based HRES consists solar photovoltaic panels, wind turbines, and battery banks with grid connectivity. Three scenarios, 101 sub-scenarios, three management cases were defined optimally design using novel dual-scale optimization approach. At scale, power-pinch analysis was applied minimize both size components outsourced needed (NE) from...