Manuel Dahmen

1.4k total citations
46 papers, 942 citations indexed

About

Manuel Dahmen is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering and Control and Systems Engineering. According to data from OpenAlex, Manuel Dahmen has authored 46 papers receiving a total of 942 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Electrical and Electronic Engineering, 13 papers in Biomedical Engineering and 12 papers in Control and Systems Engineering. Recurrent topics in Manuel Dahmen's work include Advanced Combustion Engine Technologies (9 papers), Process Optimization and Integration (8 papers) and Energy Load and Power Forecasting (6 papers). Manuel Dahmen is often cited by papers focused on Advanced Combustion Engine Technologies (9 papers), Process Optimization and Integration (8 papers) and Energy Load and Power Forecasting (6 papers). Manuel Dahmen collaborates with scholars based in Germany, Switzerland and Netherlands. Manuel Dahmen's co-authors include Wolfgang Marquardt, Alexander Mitsos, Artur M. Schweidtmann, Jan G. Rittig, Andrea König, André Bardow, Martin Grohe, Manuel Hechinger, Stefan Pischinger and Jörn Viell and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Cleaner Production and Applied Energy.

In The Last Decade

Manuel Dahmen

44 papers receiving 919 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Manuel Dahmen Germany 18 315 261 195 177 156 46 942
Juan A. Lazzús Chile 23 600 1.9× 224 0.9× 151 0.8× 206 1.2× 41 0.3× 65 1.5k
L. Rajendran India 24 354 1.1× 67 0.3× 94 0.5× 495 2.8× 100 0.6× 238 2.2k
Niket S. Kaisare India 22 292 0.9× 511 2.0× 712 3.7× 154 0.9× 727 4.7× 94 1.9k
Michael Wulkow Germany 18 188 0.6× 147 0.6× 284 1.5× 101 0.6× 108 0.7× 38 1.5k
Norbert Asprion Germany 20 543 1.7× 142 0.5× 111 0.6× 67 0.4× 51 0.3× 49 1.5k
Jakob Burger Germany 21 666 2.1× 523 2.0× 981 5.0× 69 0.4× 133 0.9× 79 2.1k
Florence H. Vermeire Belgium 17 348 1.1× 76 0.3× 574 2.9× 65 0.4× 70 0.4× 49 1.3k
Guido Buzzi‐Ferraris Italy 15 164 0.5× 160 0.6× 155 0.8× 21 0.1× 209 1.3× 36 749
Christian Jallut France 22 196 0.6× 49 0.2× 231 1.2× 221 1.2× 87 0.6× 77 1.4k
Deoki N. Saraf India 25 443 1.4× 103 0.4× 255 1.3× 63 0.4× 86 0.6× 81 1.7k

Countries citing papers authored by Manuel Dahmen

Since Specialization
Citations

This map shows the geographic impact of Manuel Dahmen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Manuel Dahmen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manuel Dahmen more than expected).

Fields of papers citing papers by Manuel Dahmen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Manuel Dahmen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Manuel Dahmen. The network helps show where Manuel Dahmen may publish in the future.

Co-authorship network of co-authors of Manuel Dahmen

This figure shows the co-authorship network connecting the top 25 collaborators of Manuel Dahmen. A scholar is included among the top collaborators of Manuel Dahmen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Manuel Dahmen. Manuel Dahmen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Viell, Jörn, et al.. (2025). Energy and Process Network Flux Analysis: Optimization-Based Assessment of Sustainable Chemical Production Pathways. Industrial & Engineering Chemistry Research. 64(14). 7489–7506.
2.
Ackermann, Philipp, Bastian Lehrheuer, Karl Alexander Heufer, et al.. (2025). Computational Co-optimization of Fuel and Spark-Ignition Engine. Energy & Fuels. 39(8). 4079–4093.
3.
Rittig, Jan G., et al.. (2024). Fuel Ignition Delay Maps for Molecularly Controlled Combustion. Energy & Fuels. 38(14). 13264–13277. 1 indexed citations
4.
Witthaut, Dirk, et al.. (2024). Multivariate scenario generation of day-ahead electricity prices using normalizing flows. Applied Energy. 367. 123241–123241. 5 indexed citations
5.
Mitsos, Alexander, et al.. (2024). Physics-informed neural networks for dynamic process operations with limited physical knowledge and data. Computers & Chemical Engineering. 192. 108899–108899. 9 indexed citations
6.
Aßen, Niklas von der, et al.. (2024). Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response. Computers & Chemical Engineering. 185. 108656–108656. 7 indexed citations
7.
Mitsos, Alexander, et al.. (2024). End-to-end reinforcement learning of Koopman models for economic nonlinear model predictive control. Computers & Chemical Engineering. 190. 108824–108824. 2 indexed citations
8.
Schweidtmann, Artur M., Jan G. Rittig, Jana M. Weber, et al.. (2023). Physical pooling functions in graph neural networks for molecular property prediction. Computers & Chemical Engineering. 172. 108202–108202. 27 indexed citations
9.
Witthaut, Dirk, et al.. (2023). Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows. Applied Energy. 346. 121370–121370. 27 indexed citations
10.
Bardow, André, et al.. (2023). Demand response for flat nonlinear MIMO processes using dynamic ramping constraints. Computers & Chemical Engineering. 172. 108171–108171. 1 indexed citations
11.
Sun, Han, et al.. (2023). Demand response scheduling of copper production under short-term electricity price uncertainty. Computers & Chemical Engineering. 178. 108394–108394. 9 indexed citations
12.
Rittig, Jan G., et al.. (2023). Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids. Computers & Chemical Engineering. 171. 108153–108153. 48 indexed citations
13.
Rittig, Jan G., Artur M. Schweidtmann, Jana M. Weber, et al.. (2022). Graph machine learning for design of high‐octane fuels. AIChE Journal. 69(4). 22 indexed citations
14.
Mitsos, Alexander, et al.. (2022). Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators. Computers & Chemical Engineering. 166. 107923–107923. 9 indexed citations
15.
Shu, David Yang, André Xhonneux, Dirk Müller, et al.. (2021). COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization. arXiv (Cornell University). 42 indexed citations
16.
König, Andrea, Artur M. Schweidtmann, Jan G. Rittig, et al.. (2021). Designing production-optimal alternative fuels for conventional, flexible-fuel, and ultra-high efficiency engines. Chemical Engineering Science. 237. 116562–116562. 20 indexed citations
17.
Schweidtmann, Artur M., Jan G. Rittig, Andrea König, et al.. (2020). Graph Neural Networks for Prediction of Fuel Ignition Quality. Energy & Fuels. 34(9). 11395–11407. 92 indexed citations
18.
Holzhäuser, F. Joschka, Manuel Dahmen, Andrea König, et al.. (2019). Electrochemical cross-coupling of biogenic di-acids for sustainable fuel production. Green Chemistry. 21(9). 2334–2344. 31 indexed citations
19.
Dahmen, Manuel & Wolfgang Marquardt. (2015). A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons. Energy & Fuels. 29(9). 5781–5801. 86 indexed citations
20.
Dahmen, Manuel, et al.. (1990). KB-PROLOG, a PROLOG for very large knowledge bases. 163–184. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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