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DTSTART;VALUE=DATE:20260817
DTEND;VALUE=DATE:20260822
DTSTAMP:20260915T103005Z
CREATED:20260915T103005Z
LAST-MODIFIED:20260915T103005Z
UID:10001980-1786924800-1787356799@ddsa.dk
SUMMARY:2026 Summer School Course at SDU in Green Hydrogen and Powerto- X
DESCRIPTION:Responsible:\n Ramkrishan Maheshwari\, University of Southern Denmark\, Sønderborg\, Denmark\n Tel: (+45) 6550 1686\, e-mail: ramkrishan@sdu.dk \n Lecturer: Ramkrishan Maheshwari\, Thomas Ebel\, Yohanes Kristianto Nugroho\, Hossein Nami\, Ali Khosravi\,\n Niels Gorm Maly Rytter\, Simon Skovlund Rytter\, Lars Yde\, Soumyabrata Patra\, Navid Bayati/Mohammed Ali\n Khan. \n Teaching language: English \n Type of course:\n PhD-course in Green Hydrogen and Power-to-X \n Workload and Credits:\n One-week full time\, five days participation at least 7 hours/day (8:30 AM – 16:00 PM). \n Total credits: 5 ECTS \n Prerequisites:\n • BSc degree in Electrical/Electronic/Mechatronic/Energy Engineering or similar.\n • Complete MSc degree in relevant scopes.\n • PhD students within Energy Systems.\n • Industry experience. \n Learning objectives – Knowledge\n • Electrolysis principles and hydrogen production technologies including Alkaline\, PEM\, AEM\, and\n SOEC\, and their application in Power‑to‑X pathways.\n • Systems‑level understanding of Power‑to‑Gas\, Power‑to‑Chemicals\, Power‑to‑Fuel\, and\n sector‑coupled PtX configurations such as methanol\, ammonia\, and methane production.\n • Subsystems and critical components of PtX plants such as electrolyzers\, separators\, purification\n units\, biomass utilization modules\, renewable power interfaces\, and offshore hydrogen systems.\n • Regulatory frameworks\, carbon markets\, infrastructural requirements\, safety and risk aspects\n associated with PtX plants and energy systems.\n • Strategies for design\, operation\, and maintenance of PtX plants\, including planned and predictive\n maintenance\, condition monitoring\, and reliability evaluation.\n • The role of Digital Twins\, digitalization tools\, modeling approaches (AC/DC modelling\, multiphysics\n modeling)\, and simulation software such as Aspen Plus and Aspen HYSYS.\n • System‑level integration of PtX technologies with renewable energy sources\, power markets\, sector\n coupling\, and existing energy infrastructure. \n Learning objectives – Skills\n The students are able to\n • Analyze and compare electrolysis technologies and evaluate their suitability for different PtX\n applications.\n • Interpret and assess the performance of PtX subsystems\, including ammonia/methanol\n production chains\, biomass‑to‑fuel pathways\, renewable integration\, and power supplies.\n • Apply process modelling\, simulation\, and optimization tools (e.g.\, Aspen Plus\, Aspen HYSYS) to\n evaluate electrolyzer operation and PtX plant performance.\n • Utilize Digital Twin concepts and data‑driven methods for forecasting\, planning\, and optimizing\n PtX plant operations.\n • Evaluate regulatory\, safety\, environmental\, and economic dimensions of PtX projects based on\n real‑world.\n • Apply maintenance\, reliability assessment\, and predictive monitoring strategies to\n electrolyzers and other critical components. \nLearning objectives – Competences\n The students are able to\n • Integrate technical\, operational\, economic\, and environmental knowledge to assess and design\n complete Power‑to‑X value chains.\n • Identify and analyze interdisciplinary challenges related to implementing PtX technologies in\n integrated energy systems\, including scalability\, regulatory constraints\, and energy market\n interactions.\n • Independently select and apply simulation\, optimization\, and digitalization tools to support\n strategic decision‑making in PtX system design and operation.\n • Work across disciplines (electronics\, operations management\, process engineering\, energy\n systems) to develop efficient\, reliable\, and safe PtX infrastructures.\n • Critically evaluate dynamic system behaviours\, operational constraints\, and long‑term system\n reliability in real‑world PtX applications. \n Evaluation:\n A report based on the assignment during the lecture needs to be submitted. The oral exam is \n supposed to be based on the report submitted. The organizers will assign 5 ECTS credits on a \n pass/fail basis. At least 80% participation is required. \n Teaching method:\n Teaching is a mixture of lectures\, Software simulation\, Demonstration of Electrolyser Plant Working \n in Physical. \nNo. of Participant (Max): 50 \n Venue: University of Southern Denmark\, Sønderborg Campus.\n . Period:\n This five-day program will take place during 17-21 August 2026.\n Time: 8:30-16:00 \n Registration:\n For information on the exact time and place and how to sign up check here:\n https://event.sdu.dk/ptxsummerschool2026/programme  \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/2026-summer-school-course-at-sdu-in-green-hydrogen-and-powerto-x/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240501
DTEND;VALUE=DATE:20240601
DTSTAMP:20240424T104758Z
CREATED:20240424T081035Z
LAST-MODIFIED:20240424T104758Z
UID:10001184-1714521600-1717199999@ddsa.dk
SUMMARY:Data-Driven Robot Control
DESCRIPTION:Title: Data-Driven Robot Control\n The Maersk Mc Kinney Moller Institute\, SDU Robotics\n Teaching language: English\n Teachers: Christoffer Sloth chsl@mmmi.sdu.dk / Inigo Iturrate inju@mmmi.sdu.dk\n ECTS: 2.5 ECTS\n Period: May 2024\n Offered in: Odense \n Prerequisites\n It is recommended that students participating in the course have: \na.                         basic knowledge in control of robots \nb.                         basic knowledge in optimization \nc.                          basic knowledge in machine learning \n Content \nData-driven methods\, such as Gaussian processes\, make it possible to obtain models of unknown processes with uncertainty quantifications\, and have found widespread applications in recent years. This course gives an introduction to data-driven methods for robot control. \nThe course will start with a general introduction on the theory of Gaussian Process Regression [1]\, which will serve as a backbone for the remaining topics. \nThe theory will subsequently be exemplified through three use-cases: identification of inverse dynamics models of robotic manipulators [2]\, safety guarantees for uncertain dynamical systems [4]\, and learning of robot trajectories based on a given set of demonstrations [4]. \n[1] Wang\, Jie. “An intuitive tutorial to Gaussian processes regression.” Computing in Science & Engineering\, 2023. \n[2] J. S. de la Cruz\, W. Owen and D. Kulíc\, “Online learning of inverse dynamics via Gaussian Process Regression\,” 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems\, Vilamoura-Algarve\, Portugal\, 2012\, pp. 3583-3590\, doi: 10.1109/IROS.2012.6385817. \n[3] Y. Kim\, I. Iturrate\, J. Langaa and C. Sloth\, “Safe Robust Adaptive Control under Both Parametric and \nNon-Parametric Uncertainty”\, Advanced Robotics\, 2024. \n[2] M. Arduengo\, A. Colomé\, J. Lobo-Prat\, L. Sentis and Carme Torras\, “Gaussian-process-based robot learning from demonstration\,” J Ambient Intell Human Comput. 2023. https://doi-org.proxy1-bib.sdu.dk/10.1007/s12652-023-04551-7 \n Learning outcomes \nThe aim of the course is\, to give the student knowledge about: \n\nGaussian Process Regression and its application to robotics\n\nBe able to work with the following skills: \n\nQuantify the uncertainties of a dynamical system trajectories based on data\n\nAnd have the competences to: \n\n      Design controllers for uncertain dynamical systems using data-driven methods\n 	 \n\nTime of classes \nThe course will start in May 2024 and will have five sessions. \nThe course will last 5 days\, i.e.\, 40 hours. \n2.5 ECTS = 67.5 h (40 h teaching\, 15 h preparation\, 12.5 h hand-in)\n   \nMore information and registration:\n Via email to Christoffer Sloth (chsl@mmmi.sdu.dk) or Pia Mønster (pmkr@mmmi.sdu.dk).\n Deadline: 1 week before the classes start.\n   \nForm of instruction\n The teaching is a mixture of lectures and exercises\, where the students can apply the theory in practice.\n   \nExamination conditions\n Following is a prerequisite to attend the final project exam: \n\nParticipation in 80 % of the classes.\n\n Evaluation \nInternal examination with no co-examiner based on a submitted report with solutions to problems addressed during the classes. The assessment will be pass/fail.\n   \nPrice\n No charge \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/__trashed-9/
LOCATION:Campusvej 55\, Odense M
CATEGORIES:PhD Course
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