Dr.-Ing. Manuel Dahmen

Dr.-Ing. Manuel Dahmen studied Computational Engineering Science with a specialization in Energy and Process Systems Engineering at RWTH Aachen University and at Chalmers University of Technology in Gothenburg, Sweden. From 2011 to 2017, he was a research assistant at the Institute of Process Systems Engineering at RWTH Aachen University. During his doctoral studies, he worked in the Cluster of Excellence "Tailor-Made Fuels from Biomass" (TMFB) in the field of computer-aided design of tailor-made fuels employing data-driven modeling and numerical optimization. Since 2017, he has been working at the Institute of Climate and Energy Systems – Energy Systems Engineering (ICE-1) at Forschungszentrum Jülich GmbH, first as group leader Optimization Methods and since 2020 as head of the department Optimization and Machine Learning. He was instrumental in establishing the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) and has been one of the FZJ representatives for the Helmholtz Information & Data Science Incubator since 2018. He is also the head of the Industrial Energy Systems department at ICE-1.

Selected recent publications:

• Doncevic, D. T., Mitsos, A., Guo, Y., Li, Q., Dietrich, F., Dahmen, M., & Kevrekidis, I. G. (2024). A recursively recurrent neural network (R2N2) architecture for learning iterative algorithms. SIAM Journal on Scientific Computing, 46(2), A719-A743, doi:10.1137/22M1535310

• Germscheid, S. H., Nilges, B., von der Assen, N., Mitsos, A., & Dahmen, M. (2024). Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response. Computers & Chemical Engineering, 185, 108656, doi:10.1016/j.compchemeng.2024.108656

• Cramer, E., Witthaut, D., Mitsos, A., & Dahmen, M. (2023). Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows. Applied Energy, 346, 121370, doi:10.1016/j.apenergy.2023.121370

• Germscheid, S. H., Röben, F. T., Sun, H., Bardow, A., Mitsos, A., & Dahmen, M. (2023). Demand response scheduling of copper production under short-term electricity price uncertainty. Computers & Chemical Engineering, 178, 108394, doi:10.1016/j.compchemeng.2023.108394

• Langiu, M., Dahmen, M., & Mitsos, A. (2022). Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points. Computers & Chemical Engineering, 161, 107745, doi:10.1016/j.compchemeng.2022.107745

• Langiu, M., Shu, D. Y., Baader, F. J., Hering, D., Bau, U., Xhonneux, A., Müller, D., Bardow, A., Mitsos, A. & Dahmen, M. (2021). COMANDO: A next-generation open-source framework for energy systems optimization. Computers & Chemical Engineering, 152, 107366, doi:10.1016/j.compchemeng.2021.107366

• Röben, F. T., Schöne, N., Bau, U., Reuter, M. A., Dahmen, M., & Bardow, A. (2021). Decarbonizing copper production by power-to-hydrogen: A techno-economic analysis. Journal of Cleaner Production, 306, 127191, doi:10.1016/j.jclepro.2021.127191

• Schweidtmann, A. M., Rittig, J. G., Konig, A., Grohe, M., Mitsos, A., & Dahmen, M. (2020). Graph neural networks for prediction of fuel ignition quality. Energy & Fuels, 34(9), 11395-11407, doi:10.1021/acs.energyfuels.0c01533