LAB NOTICE
- [송희주 석사과정, J. Water Proc Eng, JCR6%] 데이타 활용 Water 예측 모니터링- 하수처리장 계절별 미생물 동특성 (Data-Informed Predictive Water Monitoring)
- [송희주 석사과정, J. Water Proc Eng, JCR6%] 데이타 활용 Water 예측 모니터링- 하수처리장 계절별 미생물 동특성 (Data-Informed Predictive Water Monitoring)Good afternoon and good news.Happy to share this paper acceptance with emsel/Congratulations HeeJu (MSc student) for 1st paper acceptance of JCR TOP 10% journal 1st author : HeeJu Song (MSc student) eq. 1st author : TaeYong Woo (Ph.D. student)Co-corresponding Author : SungKu Heo (Associate Professor, Kangwon National University, Emsel Ph.D.)Title : Predictive monitoring of wastewater treatment performance: Seasonal microbial activity and data-informed water quality model(Short title: Data-Informed Predictive Water Monitoring)Authors : HeeJu Song+, TaeYong Woo+, SangYoun Kim, ChanHyeok Jeong , MinHan Kim, SungKu Heo**, ChangKyoo Yoo*좋은 소식 공유합니다.연구실 석사 학생인 송희주 학생 첫논문이 JCR TOP 6%저널인 Journal of Water Process Engineering 게재승인되었습니다. 공동제1저자 : 우태용 석박사통합과정공동교신저자 : 강원대 허성구 허성구 교수공저자 : 김상윤 박사과정, 정찬혁 박사과정첫 논문이 좋은 저널에 논문 게재승인 축하합니다. 더 좋은 저널에 제출한 2번쨰 논문과 PRES25학회 archival paper 좋은 소식을 기대하고 보니 꾸준히 한 2년 가까이 데이타와 공정 모델링, 소프트웨어 학습하고 노력하면 결과가 나오는듯 합니다.-주제: 데이타 활용 Water 예측 모니터링- 하수처리장 계절별 미생물 동특성 (Data-Informed Predictive Water Monitoring)특허 출원 예정HeeJu Song+, TaeYong Woo+, SangYoun Kim, ChanHyeok Jeong , MinHan Kim, SungKu Heo**, ChangKyoo Yoo*, Predictive monitoring of wastewater treatment performance: Seasonal microbial activity and data-informed water quality model, Journal of Water Process Engineering (ISSN: 2214-7144, SCIE, JCR TOP 10%: Water Resources), Elsevier, 73(5), pp.107718(2025.5) (Ack:NRF중견과제)
2025-04-10
- [Semiconductor PFAS Consortium] PFAS 컨소시엄-반도체 식각용 Coolant
- Semiconductor PFAS Consortium: PFAS 컨소시엄-반도체 식각용 Coolanthttps://www.semiconductors.org/pfas/ Semiconductor PFAS Consortium: Industry Approach to PFAS • The Semiconductor PFAS Consortium, organized by the Semiconductor Industry Association (SIA), aims to develop a science-based industry approach to per- and poly-fluoroalkyl substances (PFAS).• The consortium gathers and vets technical data on PFAS use in semiconductor manufacturing, analyzes socioeconomic impacts, and identifies research needs.• Their work includes developing technical reports and papers on PFAS uses, functionality, alternative availability, and release pathways.• The consortium has published numerous papers on PFAS in semiconductor manufacturing, including those focusing on specific applications and socioeconomic consequences of PFAS restrictions.• Membership includes semiconductor manufacturers and supply chain members, focusing on data collection and analysis rather than advocacy.https://www.semiconductors.org/pfas/ New PFAS removal process aims to stamp out pollution ahead of semiconductor industry growthCHAMPAIGN, Ill. - A University of Illinois Urbana-Champaign study is the first to describe an electrochemical strategy to capture, concentrate and destroy mixtures of diverse chemicals known as PFAS - including the increasingly prevalent ultra-short-chain PFAS - from water in a single process. This new development is poised to address the growing industrial problem of contamination with per- and polyfluoroalkyl substances, particularly in semiconductor manufacturing.A previous U. of I. study showed that short- and long-chain PFAS can be removed from water using electrochemically driven adsorption, referred to as electrosorption, but this method is ineffective for ultra-short-chain molecules because of their small size and different chemical properties. The new study, led by Illinois chemical and biomolecular engineering professor Xiao Su, combines a desalination filtration technology, called redox electrodialysis, with electrosorption in a single device to address the problems associated with capturing the complete PFAS size spectrum.The study findings are published in the journal Nature Communications. http://m.chinafepresin.com/news/should-the-use-of-pfas-in-the-semiconductor-in-73047330.htmlhttps://renewableenergyfollowers.org/4338https://renewableenergyfollowers.tistory.com/4061해외연구보고서] “반도체 산업 오염 근절하는 새로운 PFAS 제거 프로세스 개발”미국 일리노이대 샤오수 교수 연구팀, 물에서 PFAS 포집·농축·파괴하는 전기화학적 전략을 최초로 연구 완전한 PFAS 크기 스펙트럼을 포착 관련된 문제 해결하기 위해 산화환원 전기투석 담수화 여과기술을 단일 장치에 결합 국제학술지 『네이처 커뮤니케이션즈(Nature Communications)』에 게재2024-11-11 워터저널 https://www.google.com/amp/s/www.waterjournal.co.kr/news/articleViewAmp.html%3fidxno=78253 샤오수 교수는 “다양한 장치 구성을 실험한 끝에 마침내 PFAS에 오염된 물을 담수화하여 초단사슬 분자를 제거하고, 동시에 탄소 전극이 나머지 단사슬 및 장사슬 분자를 제거하는 시스템에 정착했다”라면서 “이 과정은 또한 모든 PFAS를 집중시켜 한 번 포획되면 더 쉽게 파괴할 수 있다”고 강조했다.마지막으로, 산화환원 전기투석에 내재된 전기화학적 산화공정은 포집된 PFAS를 불소 이온으로 전환하여 파괴하는데, 이는 환경에서 이러한 지속적인 오염물질을 제거하기 위한 핵심 단계이다.
2025-04-05
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)
