Artur M. Schweidtmann
Artur M. Schweidtmann
Other namesArtur Maria Schweidtmann, Artur Schweidtmann
Delft University of Technology, Department of Chemical Engineering
Verified email at - Homepage
Cited by
Cited by
Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives
AM Schweidtmann, AD Clayton, N Holmes, E Bradford, RA Bourne, ...
Chemical Engineering Journal 352, 277-282, 2018
Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm
E Bradford, AM Schweidtmann, A Lapkin
Journal of Global Optimization 71 (2), 407-438, 2018
Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm
E Bradford, AM Schweidtmann, A Lapkin
Journal of Global Optimization 71 (2), 439-440, 2018
Deterministic global optimization with artificial neural networks embedded
AM Schweidtmann, A Mitsos
Journal of Optimization Theory and Applications 180 (3), 925-948, 2019
Machine learning in chemical engineering: A perspective
AM Schweidtmann, E Esche, A Fischer, M Kloft, JU Repke, S Sager, ...
Chemie Ingenieur Technik 93 (12), 2029-2039, 2021
Automated self-optimisation of multi-step reaction and separation processes using machine learning
AD Clayton, AM Schweidtmann, G Clemens, JA Manson, CJ Taylor, ...
Chemical Engineering Journal 384, 123340, 2020
Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis
Y Amar, AM Schweidtmann, P Deutsch, L Cao, A Lapkin
Chemical science 10 (27), 6697-6706, 2019
Graph neural networks for prediction of fuel ignition quality
AM Schweidtmann, JG Rittig, A Konig, M Grohe, A Mitsos, M Dahmen
Energy & fuels 34 (9), 11395-11407, 2020
Model-based bidding strategies on the primary balancing market for energy-intense processes
P Schäfer, HG Westerholt, AM Schweidtmann, S Ilieva, A Mitsos
Computers & Chemical Engineering 120, 4-14, 2019
Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes
E Bradford, AM Schweidtmann, D Zhang, K Jing, EA del Rio-Chanona
Computers & Chemical Engineering 118, 143-158, 2018
Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks
AM Schweidtmann, WR Huster, JT Lüthje, A Mitsos
Computers & Chemical Engineering 121, 67-74, 2019
Rational design of ion separation membranes
D Rall, D Menne, AM Schweidtmann, J Kamp, L von Kolzenberg, A Mitsos, ...
Journal of Membrane Science 569, 209-219, 2019
Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning
D Rall, AM Schweidtmann, M Kruse, E Evdochenko, A Mitsos, M Wessling
Journal of Membrane Science 608, 118208, 2020
Obey validity limits of data-driven models through topological data analysis and one-class classification
AM Schweidtmann, JM Weber, C Wende, L Netze, A Mitsos
Optimization and engineering 23 (2), 855-876, 2022
Deterministic global optimization with Gaussian processes embedded
AM Schweidtmann, D Bongartz, D Grothe, T Kerkenhoff, X Lin, J Najman, ...
Mathematical Programming Computation 13 (3), 553-581, 2021
Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation
WR Huster, AM Schweidtmann, A Mitsos
Optimization and Engineering 21 (2), 517-536, 2020
Simultaneous rational design of ion separation membranes and processes
D Rall, AM Schweidtmann, BM Aumeier, J Kamp, J Karwe, K Ostendorf, ...
Journal of Membrane Science 600, 117860, 2020
A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables‐Based Processes
D Helmdach, P Yaseneva, PK Heer, AM Schweidtmann, AA Lapkin
ChemSusChem 10 (18), 3632-3643, 2017
Techno-economic Optimization of a Green-Field Post-Combustion CO2 Capture Process Using Superstructure and Rate-Based Models
U Lee, J Burre, A Caspari, J Kleinekorte, AM Schweidtmann, A Mitsos
Industrial & Engineering Chemistry Research 55 (46), 12014-12026, 2016
Chemical data intelligence for sustainable chemistry
JM Weber, Z Guo, C Zhang, AM Schweidtmann, AA Lapkin
Chemical Society Reviews 50 (21), 12013-12036, 2021
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