Johannes Schilling
Johannes Schilling
Energy and Process Systems Engineering, ETH Zurich
Bestätigte E-Mail-Adresse bei
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Zitiert von
Computer-aided molecular design in the continuous-molecular targeting framework using group-contribution PC-SAFT
M Lampe, M Stavrou, J Schilling, E Sauer, J Gross, A Bardow
Computers & Chemical Engineering 81, 278-287, 2015
1-stage CoMT-CAMD: An approach for integrated design of ORC process and working fluid using PC-SAFT
J Schilling, M Lampe, J Gross, A Bardow
Chemical Engineering Science 159, 217-230, 2017
From molecules to dollars: Integrating molecular design into thermo-economic process design using consistent thermodynamic modeling
J Schilling, D Tillmanns, M Lampe, M Hopp, J Groß, A Bardow
Molecular Systems Design & Engineering 2 (3), 301 - 320, 2017
A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
B Winter, C Winter, J Schilling, A Bardow
Digital Discovery 1 (6), 859-869, 2022
Towards optimal mixtures of working fluids: Integrated design of processes and mixtures for Organic Rankine Cycles
J Schilling, M Entrup, M Hopp, J Gross, A Bardow
Renewable and Sustainable Energy Reviews 135, 110179, 2021
Integrated design of working fluid and organic Rankine cycle utilizing transient exhaust gases of heavy-duty vehicles
J Schilling, K Eichler, B Kölsch, S Pischinger, A Bardow
Applied energy 255, 113207, 2019
Beyond temperature glide: the compressor is key to realizing benefits of zeotropic mixtures in heat pumps
D Roskosch, V Venzik, J Schilling, A Bardow, B Atakan
Energy Technology 9 (4), 2000955, 2021
Integrating superstructure‐based design of molecules, processes, and flowsheets
J Schilling, C Horend, A Bardow
AIChE Journal 66 (5), e16903, 2020
Integrated design of ORC process and working fluid using process flowsheeting software and PC-SAFT
J Schilling, J Gross, A Bardow
Energy Procedia 129, 129-136, 2017
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
B Winter, C Winter, T Esper, J Schilling, A Bardow
Fluid Phase Equilibria 568, 113731, 2023
Toward the Integrated Design of Organic Rankine Cycle Power Plants: A Method for the Simultaneous Optimization of Working Fluid, Thermodynamic Cycle, and Turbine
M Lampe, C De Servi, J Schilling, A Bardow, P Colonna
Journal of Engineering for Gas Turbines and Power 141 (11), 2019
Rx‐COSMO‐CAMPD: Enhancing reactions by integrated computer‐aided design of solvents and processes based on quantum chemistry
C Gertig, L Fleitmann, J Schilling, K Leonhard, A Bardow
Chemie Ingenieur Technik 92 (10), 1489-1500, 2020
Dielectric constant of mixed solvents based on perturbation theory
L Neumaier, J Schilling, A Bardow, J Gross
Fluid Phase Equilibria 555, 113346, 2022
Integrated design of ORC process and working fluid for transient waste-heat recovery from heavy-duty vehicles
J Schilling, K Eichler, S Pischinger, A Bardow
Computer Aided Chemical Engineering 44, 2443-2448, 2018
Integrated design of ORC process and working fluid using PC-SAFT and Modelica
D Tillmanns, C Gertig, J Schilling, A Gibelhaus, U Bau, F Lanzerath, ...
Energy Procedia 129, 97-104, 2017
Integrating working fluid design into the thermo-economic design of ORC processes using PC-SAFT
J Schilling, D Tillmanns, M Lampe, M Hopp, J Gross, A Bardow
Energy Procedia 129, 121-128, 2017
ORC on tour: Integrated design of dynamic ORC processes and working fluids for waste-heat recovery from heavy-duty vehicles
D Tillmanns, J Petzschmann, J Schilling, C Gertig, A Bardow
Computer Aided Chemical Engineering 46, 163-168, 2019
The thermo‐economic potential of ORC‐based pumped‐thermal electricity storage: insights from the integrated design of processes and working fluids
D Tillmanns, D Pell, J Schilling, A Bardow
Energy Technology 10 (7), 2200182, 2022
Shedding Light on the Stakeholders' Perspectives for Carbon Capture
C Charalambous, E Moubarak, J Schilling, ES Fernandez, JY Wang, ...
Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning
L Fleitmann, P Ackermann, J Schilling, J Kleinekorte, JG Rittig, ...
Energy & Fuels 37 (3), 2213-2229, 2023
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