LAB NOTICE
- A lecture notes-share(advanced process control)
- https://tiominho.github.io/teaching/e7225/ This course introduces optimization-based methods to automatically operate process systems. The main goal is to learn how to combine numerical optimization and dynamical systems theory to design digital solutions (i.e., feedback controllers) to control dynamical systems to optimally achieve high-level objectives. The focus is on model-based and receding-horizon control methods, with application domains in chemical and biochemical engineering.Learning OutcomesUpon completing the course, the student should be able toSimulate the transient behavior of process systems, using both nonlinear and linear(ized) modelsUnderstand key concepts and solution methods in nonlinear programmingFormulate and solve optimal control problems (incl. constrained systems)Implement/code optimization-based control algorithmsThe course is graded based on 5 weekly homeworks (40%) and 1 final assignment (60%). There is no final exam.ProgramL01Introduction[Notes]L02Dynamic Systems - Models and simulation[Notes][Homework]L03Nonlinear Programming (A)[Notes]L04Nonlinear Programming (B)[Notes][Homework]L05Model Predictive Control - Introduction (A)[Notes]L06Model Predictive Control - Introduction (B)[Notes][Homework]L07Model Predictive Control - Constraints[Notes][Homework]L08Model Predictive Control - Noise/Disturbances[Notes]L09Model Predictive Control - State estimation[Notes][Homework]L10Model Predictive Control - Output feedback[Notes]L11(Seminar) - Model Predictive Control of Water Resource Recovery Facilities[Slides]A01Final Assignment [Assignment]The homeworks are accompanied by warm-up exercises implemented in Python (required packages: [requirements.txt]).Further Reading and Cool LinksThe material is mostly based on the following textbooks:Model Predictive Control - Theory, Computation, and Design (2024) by Rawlings, Mayne, and DiehlPredictive Control for Linear and Hybrid Systems (2017) by Borelli, Bemporad, and MorariConvex Optimization (2004) by Boyd and Vandenberghe
2026-07-11
- [An International workshop of Industrial Decarbonization] Prof. Dominic Foo
- An
One-day Hands-on Course on Industrial DecarbonizationJuly 8, 2026,
Kyung Hee University (Global
Campus), Eng. Building 507Instructor:
Prof. Dominic Foo (the
University of Nottingham)Organizer:
Prof. ChangKyoo
Yoo (Kyung Hee University)Date:
8 July, 2026 1Location: Room #507, Eng. Building, Global Campus, Kyung Hee UniversityContent: CO2 pinch analysis techniques, and integrated with basic optimisation tools. Learning outcomes<o:p></o:p> · Calculation of CO2 emissions of a manufacturing process.<o:p></o:p> · Assess strategies for CO2 emissions reduction.<o:p></o:p> · Basic techno-economic analysis for CO2 emissions reduction alternatives.<o:p></o:p> · Multi-period deployment planning of CO2 emissions reduction alternatives.
Organized
BK21
Education and Research Center for Intelligent Multi-dimensional Printing
Materials and Systems Convergence Technology (i-MPrinting) and NRF Basic
Research Lab Program of Laboratory for selective concentration,
regeneration, and AI-based low-energy whole-lifecycle dynamic membrane
systems for PFAS in industrial wastewater
(RS-2026-25504128)
2026-07-10
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)
