Alexander Mitsos

9.9k total citations · 1 hit paper
311 papers, 7.2k citations indexed

About

Alexander Mitsos is a scholar working on Control and Systems Engineering, Mechanical Engineering and Biomedical Engineering. According to data from OpenAlex, Alexander Mitsos has authored 311 papers receiving a total of 7.2k indexed citations (citations by other indexed papers that have themselves been cited), including 133 papers in Control and Systems Engineering, 58 papers in Mechanical Engineering and 58 papers in Biomedical Engineering. Recurrent topics in Alexander Mitsos's work include Process Optimization and Integration (83 papers), Advanced Control Systems Optimization (73 papers) and Advanced Optimization Algorithms Research (36 papers). Alexander Mitsos is often cited by papers focused on Process Optimization and Integration (83 papers), Advanced Control Systems Optimization (73 papers) and Advanced Optimization Algorithms Research (36 papers). Alexander Mitsos collaborates with scholars based in Germany, United States and United Kingdom. Alexander Mitsos's co-authors include Dominik Bongartz, Paul I. Barton, Artur M. Schweidtmann, Amin Ghobeity, Adel Mhamdi, Corey J. Noone, Adrian Caspari, Jörn Viell, Pascal M. Schäfer and N. D. Mancini and has published in prestigious journals such as SHILAP Revista de lepidopterología, Energy & Environmental Science and Bioinformatics.

In The Last Decade

Alexander Mitsos

298 papers receiving 7.0k citations

Hit Papers

Process systems engineering – The generation next? 2021 2026 2022 2024 2021 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexander Mitsos Germany 45 2.1k 1.7k 1.5k 1.3k 1.3k 311 7.2k
Iftekhar A. Karimi Singapore 51 3.1k 1.5× 2.4k 1.4× 696 0.4× 1.1k 0.9× 539 0.4× 262 8.9k
Moonyong Lee South Korea 57 3.7k 1.8× 4.1k 2.4× 1.3k 0.9× 2.2k 1.7× 1.6k 1.2× 476 11.5k
Pródromos Daoutidis United States 50 4.1k 2.0× 934 0.5× 410 0.3× 1.1k 0.8× 970 0.8× 283 7.9k
Lei Zhang China 41 1.2k 0.6× 1.1k 0.6× 1.8k 1.2× 892 0.7× 2.5k 1.9× 295 6.4k
Ali Elkamel Canada 52 1.9k 0.9× 1.7k 1.0× 1.2k 0.8× 1.6k 1.2× 3.4k 2.6× 472 10.5k
Mahmoud M. El‐Halwagi United States 61 7.1k 3.4× 2.7k 1.6× 1.7k 1.1× 3.2k 2.5× 800 0.6× 439 14.8k
Christos T. Maravelias United States 53 3.6k 1.7× 1.6k 1.0× 1.6k 1.0× 4.3k 3.3× 607 0.5× 260 11.8k
Thomas F. Edgar United States 53 5.3k 2.6× 2.6k 1.5× 944 0.6× 1.1k 0.8× 2.1k 1.6× 428 11.2k
M.A. Hussain Malaysia 40 2.5k 1.2× 1.1k 0.6× 638 0.4× 1.1k 0.8× 1.6k 1.3× 265 7.0k
ChangKyoo Yoo South Korea 50 3.9k 1.9× 3.3k 1.9× 903 0.6× 1.0k 0.8× 1.5k 1.1× 387 10.9k

Countries citing papers authored by Alexander Mitsos

Since Specialization
Citations

This map shows the geographic impact of Alexander Mitsos'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 Alexander Mitsos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Mitsos more than expected).

Fields of papers citing papers by Alexander Mitsos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexander Mitsos. 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 Alexander Mitsos. The network helps show where Alexander Mitsos may publish in the future.

Co-authorship network of co-authors of Alexander Mitsos

This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Mitsos. A scholar is included among the top collaborators of Alexander Mitsos 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 Alexander Mitsos. Alexander Mitsos 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.
Bâldea, Michael, et al.. (2025). Dynamic Scheduling: A Comparison of High-Fidelity Models with Local Optimization versus Surrogate Models with Global Optimization. Industrial & Engineering Chemistry Research. 64(45). 21641–21657.
3.
Mitsos, Alexander, et al.. (2024). Optimal sizing and operation of electrochemical hydrogen compression. Chemical Engineering Science. 293. 120031–120031. 8 indexed citations
4.
Mitsos, Alexander, et al.. (2024). Model-based evaluation of ammonia energy storage concepts at high technological readiness level. Applied Energy. 377. 124495–124495. 12 indexed citations
5.
Mitsos, Alexander, et al.. (2024). Cost-optimal design and operation of hydrogen refueling stations with mechanical and electrochemical hydrogen compressors. Computers & Chemical Engineering. 192. 108862–108862. 8 indexed citations
6.
Mitsos, Alexander, et al.. (2024). Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions. BMC Bioinformatics. 25(1). 155–155. 1 indexed citations
7.
Mitsos, Alexander, et al.. (2024). A branch-and-bound algorithm with growing datasets for large-scale parameter estimation. European Journal of Operational Research. 316(1). 36–45. 2 indexed citations
8.
Ackermann, Philipp, et al.. (2024). Identifying key environmental objectives for integrated process and fuel design. Sustainable Energy & Fuels. 8(9). 1966–1982. 2 indexed citations
9.
Rittig, Jan G., et al.. (2024). Fuel Ignition Delay Maps for Molecularly Controlled Combustion. Energy & Fuels. 38(14). 13264–13277. 1 indexed citations
10.
Schweidtmann, Artur M., et al.. (2023). Data-driven product-process optimization of N-isopropylacrylamide microgel flow-synthesis. Chemical Engineering Journal. 479. 147567–147567. 8 indexed citations
11.
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
12.
Witthaut, Dirk, et al.. (2023). Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows. Applied Energy. 346. 121370–121370. 27 indexed citations
13.
Burre, Jannik, Dominik Bongartz, Sarah Deutz, et al.. (2021). Comparing pathways for electricity-based production of dimethoxymethane as a sustainable fuel. Energy & Environmental Science. 14(7). 3686–3699. 21 indexed citations
14.
Gjerga, Enio, Panuwat Trairatphisan, Attila Gábor, et al.. (2020). Converting networks to predictive logic models from perturbation signalling data with CellNOpt. Bioinformatics. 36(16). 4523–4524. 22 indexed citations
15.
Schweidtmann, Artur M., et al.. (2020). Globally optimal working fluid mixture composition for geothermal power cycles. Energy. 212. 118731–118731. 10 indexed citations
16.
Caspari, Adrian, et al.. (2019). Dynamic Optimization of a Fed-Batch Microgel Synthesis. IFAC-PapersOnLine. 52(1). 394–399. 4 indexed citations
17.
Caspari, Adrian, et al.. (2019). Set-Membership Parameter Estimation: Improved Understanding of Microgel Polymerization. IFAC-PapersOnLine. 52(1). 580–585. 1 indexed citations
18.
Mhamdi, Adel, et al.. (2018). Analysis and improvement of dynamic heat exchanger models for nominal and start-up operation. Energy. 169. 1191–1201. 16 indexed citations
19.
Mitsos, Alexander, et al.. (2017). Concentrating Solar Thermal Overview. RWTH Publications (RWTH Aachen). 3 indexed citations
20.
Melas, Ioannis N., et al.. (2012). Construction of large signaling pathways using an adaptive perturbation approach with phosphoproteomic data. Molecular BioSystems. 8(5). 1571–1584. 14 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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