LAB NOTICE
- [Energy, JCR5%][Moosazadeh교수,김상윤/Mahmoud/전나영학생]DAC 리뷰 논문 : 저탄소에너지, 신재생통합, 탄소저장 (DAC review :low-carbon energy systems:
- [DGU Moosazadeh교수, 김상윤, Mahmoud, 전나영학생][Energy, JCR5%] 저탄소 DAC 리뷰 : Hi emsel,A great journal.연구실 졸업생 동국대 Moosazadeh교수, 김상윤,Mahmoud,전나영 학생박사 논문이 리뷰 논문 JCR TOP 5% Energy 저널에 게재되었습니다.연구 주제는 저탄소 시스템을 위한 Keywords; Regeneration energy,Low-carbon energy systems,Renewable energy integrationMohammad Moosazadeh, Shadfar Davoodi, Shahabaldin Rezania, Adrian Chun MinhLoy, Lalit Goswami, SangYoun Kim, Mahmoud Kiannejad Amiri, NaYoung Cheon, Gopa Nandikes, Anamika Kushwaha, Soheil Mohtaram, Jinwoo Park⁎, ChangKyoo Yoo*, Direct air capture in low-carbon energy systems: Regeneration penalties, renewable integration, and carbon storage pathways, Energy(SCI, ISSN: 0360-5442, JCR TOP 5%), 364(11), pp.142390 (2026.11) (Ack 4개: DGU2개, KHU2개-NRF 도약과제, BRL과제)https://www.sciencedirect.com/science/article/pii/S0360544226024977?dgcid=author Future research priorities and international coordination-Capture materials, regeneration, and data-driven discovery-Renewable-energy integration and flexible operation-Scale-up, harmonized assessment, and verified net removal-International benchmarking and coordination
2026-09-10
- [github] OpenPyTEA: TEA of chemical and energy systems
- github codehttps://github.com/pbtamarona/<wbr>openpyteaOpenPyTEA is an open-source Python toolkit for performing techno-economic assessment (TEA) of chemical and energy systems. It was created to address a persistent gap in the TEA workflow: while process simulators model mass and energy balances, researchers often lack an equally transparent and flexible way to evaluate the economic feasibility of their designs. Commercial tools remain black-box tools, and many academic TEA implementations are process-specific, undocumented, or difficult to reproduce.OpenPyTEA provides a fully open, modular, and traceable framework that brings TEA into the Python ecosystem. By integrating equipment cost estimation, capital and operating expenditure modeling, cash-flow analysis, cost breakdowns, sensitivity evaluation, and Monte Carlo uncertainty propagation, the toolkit enables users to perform end-to-end TEA with clarity and reproducibility.Beyond its functionality, OpenPyTEA is designed as a community-driven TEA platform. Users can contribute new equipment cost correlations, improve economic models, report issues, and expand the toolkit’s capabilities over time. This collaborative approach helps build a shared, transparent, and continually improving TEA resource—similar to the open-source progress seen in the LCA community.Whether used for early-stage process design, technology screening, or teaching, OpenPyTEA makes TEA more accessible, consistent, and aligned with FAIR research principles (Findable, Accessible, Interoperable, and Reusable).For a full walkthrough of the features and usage of OpenPyTEA, refer to the <code style="font-family: ui-monospace, sfmono-regular, " sf="" mono",="" menlo,="" consolas,="" "liberation="" monospace;="" font-size:="" 14.28px;="" background-color:="" rgba(129,="" 139,="" 152,="" 0.12);="" border-radius:="" 6px;="" margin:="" 0px;="" padding:="" 0.2em="" 0.4em;"="">walkthrough.ipynb</code> notebook<wbr>:https://github.com/pbtamarona/<wbr>OpenPyTEA/blob/main/<wbr>walkthrough.ipynbFor the full documentation of the package, visit the ReadTheDocs page:https://openpytea.readthedocs.<wbr>ioFor some case-study examples, please check the <code style="font-family: ui-monospace, sfmono-regular, " sf="" mono",="" menlo,="" consolas,="" "liberation="" monospace;="" font-size:="" 14.28px;="" background-color:="" rgba(129,="" 139,="" 152,="" 0.12);="" border-radius:="" 6px;="" margin:="" 0px;="" padding:="" 0.2em="" 0.4em;"="">examples</code> folder: https://<wbr>github.com/pbtamarona/<wbr>OpenPyTEA/tree/main/examples
2026-09-08
- [github] Public Chemical Manufacturing Datasets(P&IDs, Time series process)
- Public Chemical Manufacturing Datasets https://github.com/ScottDuncanAI/public-chemical-manufacturing-datasets By Scott Duncan. Reach me on LinkedIn here: https://www.linkedin.com/in/s-r-duncan/ Chemical manufacturing data is notoriously difficult to find because companies rarely make process data or engineering drawings public. This repository brings together useful, publicly accessible datasets for developing and testing AI, analytics, and engineering applications. Two sections: P&IDs: actual piping & instrumentation diagrams from real permitted facilities.Time series: process datasets for fault detection, anomaly detection, and soft sensor work.Inspired by awesome-industrial-datasets. P&IDsEvery one of these is a water treatment facility, which is not a coincidence. If you discharge treated water to a river or the ground you need a permit, and the permit application goes into a public docket along with the supporting engineering drawings. Dataset Facility Page #'s containing P&IDs Format Size Other info in documentHaile Gold Mine CWTP Gold mine contact water treatment in South Carolina 47–60 Digital PDF 13 MB Process flow diagram, permit application, design basis narrative, safety datasheetsPahala WWTP Municipal wastewater treatment plant in Hawaii 9–25 Digital PDF 7 MB Legend, abbreviation and symbol sheets, process schematic, site and civil plansVirden WWTF Municipal wastewater treatment plant in Manitoba, Canada 5–14 Scanned images 3 MB Drawing set onlySalina WWTP BDR Municipal wastewater treatment plant in Kansas 114, 117, 119, 121, 126, 127, 132, 134 Digital PDF, mixed text layer 33 MB Full basis of design report, electrical diagrams, existing plant condition assessment, alternatives evaluation, cost estimatesRuttan Mine WTP Mine water treatment in Manitoba, Canada 50–52, 62–63 Digital PDF 18 MB Plant operating manual, electrical drawings, structural drawings, mechanical drawings, water quality monitoring data
2026-09-07
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)
