Publications of Edgar Ivan Sanchez Medina

Peterson, L., Forootani, A., Sanchez Medina, E. I., Gosea, I. V., Sundmacher, K., & Benner, P. (2025)Towards Digital Twins for Power-to-X Processes: Comparing Surrogate Models for a Catalytic CO2 Methanation ReactorIEEE Transactions on Automation Science and Engineeringdoi: 10.1109/TASE.2025.3564637.

König-Mattern, L., Sanchez Medina, E. I., Komarova, A. O., Linke, S., Rihko-Struckmann, L., Luterbacher, J. S., & Sundmacher, K. (2024)Machine learning-supported solvent design for lignin-first biorefineries and lignin upgradingChemical Engineering Journal, 495, 153524doi: 10.1016/j.cej.2024.153524.

Sanchez Medina, E. I., Kunchapu, S., & Sundmacher, K. (2023)Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite DilutionThe Journal of Physical Chemistry A, 127, 9863–9873doi: 10.1021/acs.jpca.3c05892.

Sanchez Medina, E. I., Linke, S., Stoll, M., & Sundmacher, K. (2023)Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilutionDigital Discovery, 2 (3), 781–798doi: 10.1039/D2DD00142J.

Sanchez Medina, E. I., Linke, S., Stoll, M., & Sundmacher, K. (2022)Graph neural networks for the prediction of infinite dilution activity coefficientsDigital Discovery, 1 (3), 216–225 doi: 10.1039/D1DD00037C.

McBride, K., Sanchez Medina, E. I., & Sundmacher, K. (2020)Hybrid Semi‐parametric Modeling in Separation Processes: A ReviewChemie Ingenieur Technik, 92 (7), 842–855-855doi: 10.1002/cite.202000025.

Kay, S., Sanchez Medina, E. I., Sundmacher, K., & Zhang, D. (2024)A symbolic regression based methodology for the construction of interpretable and predictive thermodynamic modelsIn: 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering, 2701–2706doi: 10.1016/B978-0-443-28824-1.50451-8.

Sanchez Medina, E. I. & Sundmacher, K. (2023)Solvent pre-selection for extractive distillation using Gibbs-Helmholtz Graph Neural NetworksIn: 33rd European Symposium on Computer Aided Process Engineering, 2037–2042doi: 10.1016/B978-0-443-15274-0.50324-3.

Sanchez Medina, E. I., Linke, S., & Sundmacher, K. (2021)Prediction of Bioconcentration Factors (BCF) using Graph Neural NetworksIn: 31st European Symposium on Computer Aided Process Engineering, 991–997doi: 10.1016/B978-0-323-88506-5.50153-4.

Sanchez Medina, E. I., Vallejo, D. R., Chachuat, B., Sundmacher, K., Petsagkourakis, P., & del Rio-Chanona, E. A. (2021)Acyclic modular flowsheet optimization using multiple trust regions and Gaussian process regressionIn: 31st European Symposium on Computer Aided Process Engineering, 1117–1123doi: 10.1016/B978-0-323-88506-5.50172-8.

Peterson, L., Forootani, A., Sanchez Medina, E. I., Gosea, I. V., Benner, P., & Sundmacher, K.Comparing Surrogate Models for Real-Time Dynamic Reactor SimulationsPresented at: Digital Twins in Engineering 2025Paris, France, February 17, 2025.

Kay, S., Sanchez Medina, E. I., Sundmacher, K., & Zhang, D.A symbolic regression based methodology for the construction of interpretable and predictive thermodynamic modelsPresented at: ESCAPE-34Florence, Italy, June 2, 2024.

König-Mattern, L., Sanchez Medina, E. I., Komarova, A. O., Linke, S., Rihko-Struckmann, L., Luterbacher, J. S., & Sundmacher, K.PSEvolve: A graph-based solvent design frameworkPresented at: ESCAPE34 Florence, Italy, June 2, 2024.

Sanchez Medina, E. I., Minor, A.-J., & Sundmacher, K.A Comparison of the UNIFAC Model vs. Graph Neural Network-based Models for the Prediction of Binary Vapor-Liquid EquilibriaPresented at: 33rd European Symposium on Applied Thermodynamics 2024Edinburgh, UK, June 9, 2024.

König-Mattern, L., Sanchez Medina, E. I., Komarova, A. O., Linke, S., Rihko-Struckmann, L., Luterbacher, J., & Sundmacher, K.Tailored solvent design for lignin dissolution using graph neural networksPresented at: ECCE 14 & ECAB 7: 14th European Congress of Chemical Engineering and 7th European Congress of Applied BiotechnologyBerlin, Germany, September 17, 2023.

Sanchez Medina, E. I., Ganzer, C., Antonio , del R. C., Matar, O., & Sundmacher, K.An introductory course of machine learning for chemical engineering students: a prototypePresented at: WCCE11 - 11th WORLD CONGRESS OF CHEMICAL ENGINEERINGBuenos Aires, Argentina , June 4, 2023.

Sanchez Medina, E. I., Kunchapu, S., & Sundmacher, K.Predicting activity coefficients at infinite dilution of polymer solutions using Graph Neural NetworksPresented at: WCCE11 - 11th WORLD CONGRESS OF CHEMICAL ENGINEERINGBuenos Aires, Argentina , June 4, 2023.

Sanchez Medina, E. I. & Sundmacher, K.Solvent pre-selection for extractive distillation using Gibbs-Helmholtz Graph Neural NetworksPresented at: ESCAPE-33Athens, Greece, June 18, 2023.

Sanchez Medina, E. I., Rodriguez-Vallejo, D. F., del Rio-Chanona, E. A., Sundmacher, K., & Petsagkourakis, P.Multi-Objective Bayesian optimization of process flowsheets using trust regions and quality set metrics.Presented at: 2021 AIChE Annual MeetingVirtual, November 15, 2021.

Griehl, C., Sanchez Medina, E. I., & Sundmacher, K.BOHO CAT - Bayesian Optimization for Low Throughput Ligand Selection in Homogeneous CatalysisPresented at: Cambridge ELLIS Unit Summer School 2024Cambridge, UK, July 15, 2024.