| Hybrid semi‐parametric modeling in separation processes: a review K McBride, EI Sanchez Medina, K Sundmacher Chemie Ingenieur Technik 92 (7), 842-855, 2020 | 36 | 2020 |
| Graph neural networks for the prediction of infinite dilution activity coefficients EIS Medina, S Linke, M Stoll, K Sundmacher Digital Discovery 1 (3), 216-225, 2022 | 31 | 2022 |
| Impacts of antiscalants on the formation of calcium solids: Implication on scaling potential of desalination concentrate T Jain, E Sanchez, E Owens-Bennett, R Trussell, S Walker, H Liu Environmental Science: Water Research & Technology 5 (7), 1285-1294, 2019 | 31 | 2019 |
| Understanding the dynamic behaviour of semicontinuous distillation PB Madabhushi, EIS Medina, TA Adams II Computer Aided Chemical Engineering 43, 845-850, 2018 | 8 | 2018 |
| Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution EIS Medina, S Linke, M Stoll, K Sundmacher Digital Discovery 2 (3), 781-798, 2023 | 6 | 2023 |
| Prediction of bioconcentration factors (bcf) using graph neural networks EIS Medina, S Linke, K Sundmacher Computer Aided Chemical Engineering 50, 991-997, 2021 | 2 | 2021 |
| Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution EI Sanchez Medina, S Kunchapu, K Sundmacher The Journal of Physical Chemistry A 127 (46), 9863-9873, 2023 | 1 | 2023 |
| Solvent pre-selection for extractive distillation using Gibbs-Helmholtz Graph Neural Networks EIS Medina, K Sundmacher Computer Aided Chemical Engineering 52, 2037-2042, 2023 | 1 | 2023 |
| Multi-Objective Bayesian optimization of process flowsheets using trust regions and quality set metrics. EI Sanchez Medina, DF Rodriguez-Vallejo, EA del Rio-Chanona, ... 2021 AIChE Annual Meeting, 2021 | 1 | 2021 |
| Acyclic modular flowsheet optimization using multiple trust regions and Gaussian process regression EIS Medina, DR Vallejo, B Chachuat, K Sundmacher, P Petsagkourakis, ... Computer Aided Chemical Engineering 50, 1117-1123, 2021 | 1 | 2021 |
| Graph Neural Networks for CO2 Solubility Predictions in Deep Eutectic Solvents EIS Medina, GH Morales, A Jiménez-Gutiérrez, VM Zavala | | 2024 |
| Machine learning-based solvent screening for lignocellulose biorefineries and lignin upgrading L König-Mattern, EI Sanchez Medina, L Rihko-Struckmann, ... BioSPRINT Spring School: Opportunities and challenges of process …, 2024 | | 2024 |
| An introductory course of machine learning for chemical engineering students: a prototype EI Sanchez Medina, C Ganzer, RC Antonio, O Matar, K Sundmacher WCCE11-11th WORLD CONGRESS OF CHEMICAL ENGINEERING, 2023 | | 2023 |
| Tailored solvent design for lignin dissolution using graph neural networks L König-Mattern, EI Sanchez Medina, AO Komarova, S Linke, ... ECCE 14 & ECAB 7: 14th European Congress of Chemical Engineering and 7th …, 2023 | | 2023 |
| Predicting activity coefficients at infinite dilution of polymer solutions using Graph Neural Networks EI Sanchez Medina, S Kunchapu, K Sundmacher WCCE11-11th WORLD CONGRESS OF CHEMICAL ENGINEERING, 2023 | | 2023 |
| Predicting Activity Coefficients at Infinite Dilution Using Hybrid Residual Graph Neural Networks EIS Medina, S Linke, M Stoll, K Sundmacher 2022 AIChE Annual Meeting, 2022 | | 2022 |
| RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs A Iravanizad, EIS Medina, M Stoll arXiv preprint arXiv:2109.07555, 2021 | | 2021 |
| Machine Learning-Supported Solvent Design for Lignin-First Biorefineries and Lignin Upgrading L König-Mattern, E Sanchez Medina, AO Komarova, S Linke, ... Available at SSRN 4796907, 0 | | |
Kai SundmacherProfessor of Process Systems Engineering, Max Planck Institute for Dynamics of Complex TechnicalVerified email at mpi-magdeburg.mpg.de
