LAB NOTICE
- [춘계화학공학회 구두발표 4편발표] 우태용, 정찬혁, 전나영, 김상윤 학생
- 4월 22-24일 제주도에서 열린 춘계화학공학회 구두발표 4편발표하였습니다.2026년도 한국화학공학회 봄 총회 및 학술대회-일시: 4월 22-24일-장소: 제주도-구두 발표 : 우태용, 정찬혁, 전나영, 김상윤 학생 1. 우태용, 김상윤, 정찬혁, 전나영, 유창규*, 물리적 설계 매개변수를 고려한 Parametric W-SFILES 기반 하수처리장 자율 flowsheet autocompletion 방법론 개발, 2026년도한국화학공학회 봄 총회 및 학술대회 초록집, 구두 발표, ICC JEJU, pp 189 (2026.04.22.)(Ack: NRF-RS-2026-25471555 & BK21 FOUR)2. 정찬혁, 김상윤, 우태용, 전나영, 허성구, 유창규*, 바이오가스 생산 향상을 위한 혐기성 소화 pH 제어: 도메인 특화 보상 설계 기반 오프라인 강화학습, 2026년도 한국화학공학회 봄 총회 및 학술대회 초록집, 구두 발표, ICC JEJU, pp 189 (2026.04.22.) (Ack: NRF-RS-2026-25471555 & BK21 FOUR)3. 전나영, 김상윤, 우태용, 정찬혁, 김진수, 유창규*, 현실적 DAC 운전을 위한 TVSA 공정 계절-적응형 스케줄링: 반복 동적 계획법 기반 에너지 및 비용 최적화, 2026년도 한국화학공학회 봄 총회 및 학술대회 초록집, 구두 발표, ICC JEJU, pp 188 (2026.04.22.) (Ack: NRF-RS-2026-25471555 & BK21 FOUR)4. 김상윤, 전나영, 우태용, 정찬혁, 김진수, 유창규*, 다중스케일 물리정보 능동 학습 Closed-Loop: 통합 MOF 선별 및 DAC 운전 최적화, 2026년도 한국화학공학회 봄 총회 및 학술대회 초록집, 구두 발표, ICC JEJU, pp 195 (2026.04.24.) (Ack: RS-2025-25433584) 연구주제 1: 시스템 insight 및 물리변수 포함한 공정 자율 flowsheet autocompletion: Input (유입-유출)==> AI 학습 ==> outputs(Autonomous PFD Candidates 자율 생성)연구주제 2: 혐기성 소화조 운전시 pH 저해작용 시스테 기작, 모델링 및 Data-RL 강화학습 제어: 유입수 변동에 따른 2상 혐기조내 미생물 최적 성장을 위한 pH 최적 조건 자율 선정연구주제 3: DAC용 TVSA 흡착-탈착 공정 모델링 & 계절-적응형 탈착온도 RL-PPO 설정치 : DAC 외기조건에 따른 최적 탈착 온도 설정치 탐색 ==> 확장 예정(Full TVSAS cycle modeling(CSS/cooling step), 유입/외기에 따른 흡착/탈착 운전 자율 스케쥴링)연구주제 4: DAC 시스템의 MOF 분자-TVSA공정-운전 다중스케일 Screeing-Operation Active Learning기반 Closed-Loop : 지역-유입-외기 조건과 공정별 최적 재료 스크리닝,공정선택,운전최적의 폐루프 시스템 , 예정 (탄소-외기변화에 따른 공정 흡/탈착 다이나믹스 추가 및 Closed-Loop 재 연동) •Limitations & Future works
- Dynamic simulation for
VTSA
process considering kinetic parameters
to obtain realistic KPIs.
2026-04-25
- [Lecture:LCA-SignaPro][Environmental Impact Assessment of Electricity Mix in Italy using SimaPro | ReCiPe Endpoint]
- Environmental Impact Assessment of Electricity Mix in Italy using SimaPro | ReCiPe Endpoint Environmental Impact Assessment of Electricity Mix in Italy using SimaPro | ReCiPe Endpoint In this video, I present a detailed and practical methodology for evaluating the environmental impacts of electricity generation based on the energy mix of a country, using Italy as a case study.
The main objective of this work is to demonstrate how the percentage contribution of different electricity generation sources can be systematically translated into environmental pollution indicators using a robust Life Cycle Assessment (LCA) framework.
The modeling has been performed using SimaPro, one of the most advanced and widely used LCA software tools in academic and industrial research. The electricity mix includes:
Fossil fuels (Natural Gas, Coal)
Renewable energy sources (Hydropower, Solar, Wind, Bioenergy, Geothermal)
Imported electricity (mainly Nuclear)
The analysis is based on a functional unit of 100 kWh electricity production, which enables a normalized and consistent comparison of environmental impacts across different energy sources.
For impact assessment, I applied the ReCiPe Endpoint (H) method, which evaluates environmental burdens at the damage level, offering a more interpretable and decision-oriented perspective. The Endpoint (H) (Hierarchist) approach considers a balanced set of assumptions and time horizons, making it highly suitable for policy and sustainability studies.
The results are categorized into three key damage areas:
Human Health (e.g., emissions affecting mortality and morbidity)
Ecosystem Quality (e.g., biodiversity loss and ecosystem degradation)
Resource Scarcity (e.g., depletion of fossil fuels and minerals)
In this tutorial, you will learn:
How to convert electricity mix percentages into an LCA model
How each energy source contributes to total environmental impacts
How to implement and analyze results in SimaPro
How to interpret Endpoint results for sustainability decision-making
The importance of transitioning to cleaner and renewable energy systems
This video is highly beneficial for:
Researchers and academics in energy and environment
Engineers working on sustainability and decarbonization
Students learning Life Cycle Assessment (LCA)
Professionals interested in energy system optimization
This work also reflects real-world applications in energy policy, environmental engineering, and sustainable system design, bridging the gap between theoretical analysis and practical modeling.
2026-04-25
PUBLICATIONS
- Sustainable energies and machine learning: an organized review of recent applications and challenges
- Pouya Ifaei, Morteza Nazari-Heris, Amir Saman Tayerani Charmchi, Somayeh Asadi, ChangKyoo Yoo*, Sustainable energies and machine learning: an organized review of recent applications and challenges, Energy (ISSN: 0360-5442, SCI, Top 5% journal – THERMODYNAMICS), 266(1), pp.126432 (2023.3) (Ack 3개: Brain Pool Program2019H1D3A1A02071051 & NRF중견2021R1A2C2007838 & Collabo R&D of SMEs and Startups in 2022.(Project No. S3301144)
- Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach
- Shahzeb Tariq+, Jorge Loy-Benitez+, KiJeon Nam, SangYoun Kim, MinJeong Kim*, ChangKyoo Yoo*, Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach, Tunnelling and underground space (ISSN: 0886-7798, SCIE, JCR Top 10%-ENGINEERING, CIVIL, DOI), Elsevier, 131(1), pp.104843 (Ack: 2021R1A2C2007838 & Fine Dust Reduction Technology(21QPPW-B152306-03)
- Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving
- Abdulrahman H. Ba-Alawi, KiJeon Nam, SungKu Heo, TaeYong Woo, Hanaa Aamer, and Chang Kyoo Yoo*, Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving, Chemical Engineering Journal (IF>16.4, JCR TOP 3% journal/Top 1 journal-Chemical Engineering), 452(1), pp.139220 (2023.1) (Ack 3개:NRF2021R1A2C2007838, “Prospective green technology innovation project (2020003160009), SMEs and Startups in 2022 (Project No. S3301144)
