Spremljaj
Jan G. Rittig
Jan G. Rittig
RWTH Aachen University, Aachener Verfahrenstechnik, Lehrstuhl für Systemverfahrenstechnik
Preverjeni e-poštni naslov na rwth-aachen.de
Naslov
Navedeno
Navedeno
Leto
Graph neural networks for prediction of fuel ignition quality
AM Schweidtmann, JG Rittig, A König, M Grohe, A Mitsos, M Dahmen
Energy & Fuels 34 (9), 11395-11407, 2020
1052020
Summit: benchmarking machine learning methods for reaction optimisation
KC Felton, JG Rittig, AA Lapkin
Chemistry‐Methods 1 (2), 116-122, 2021
772021
Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids
JG Rittig, KB Hicham, AM Schweidtmann, M Dahmen, A Mitsos
Computers & Chemical Engineering 171, 108153, 2023
252023
Designing production-optimal alternative fuels for conventional, flexible-fuel, and ultra-high efficiency engines
A König, M Siska, AM Schweidtmann, JG Rittig, J Viell, A Mitsos, ...
Chemical Engineering Science 237, 116562, 2021
142021
Physical pooling functions in graph neural networks for molecular property prediction
AM Schweidtmann, JG Rittig, JM Weber, M Grohe, M Dahmen, ...
Computers & Chemical Engineering 172, 108202, 2023
122023
Graph machine learning for design of high‐octane fuels
JG Rittig, M Ritzert, AM Schweidtmann, S Winkler, JM Weber, P Morsch, ...
AIChE Journal 69 (4), e17971, 2023
92023
Graph neural networks for the prediction of molecular structure-property relationships
JG Rittig, Q Gao, M Dahmen, A Mitsos, AM Schweidtmann
arXiv preprint arXiv:2208.04852, 2022
92022
Gibbs–Duhem-informed neural networks for binary activity coefficient prediction
JG Rittig, KC Felton, AA Lapkin, A Mitsos
Digital Discovery 2 (6), 1752-1767, 2023
32023
ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction
K Felton, L Rasßpe-Lange, J Rittig, K Leonhard, A Mitsos, ...
22023
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
22023
Graph Neural Networks for Surfactant Multi-Property Prediction
C Brozos, JG Rittig, S Bhattacharya, E Akanny, C Kohlmann, A Mitsos
arXiv preprint arXiv:2401.01874, 2024
2024
Parameter estimation and dynamic optimization of an industrial fed-batch reactor
JG Rittig, JC Schulze, L Henrichfreise, S Recker, R Feller, A Mitsos, ...
Computer Aided Chemical Engineering 52, 1175-1180, 2023
2023
Computer-Aided Fuel Design with Generative Graph Machine Learning
JG Rittig, M Ritzert, AM Schweidtmann, S Winkler, JM Weber, P Morsch, ...
2022 AIChE Annual Meeting, 2022
2022
Predicting Temperature-Dependent Activity Coefficients of Ionic Liquid-Solute Systems through Graph-Based Machine Learning
JG Rittig, KB Hicham, AM Schweidtmann, M Dahmen, A Mitsos
2022 AIChE Annual Meeting, 2022
2022
Molecular design of spark-ignition fuels by combining predictive thermodynamics and machine learning
LHJ Fleitmann, P Ackermann, J Schilling, J Kleinekorte, JG Rittig, ...
32nd European Symposium on Computer-Aided Process Engineering (ESCAPE-32), 2022
2022
Molecular Design of Spark-Ignition Engine Fuels for Maximal Engine Efficiency
P Ackermann, LHJ Fleitmann, J Schilling, J Kleinekorte, JG Rittig, ...
10th International Conference “Fuel Science-From Production to Propulsion”, 2022
2022
Fuel Design by Combined Machine Learning and Predictive Thermodynamics
L Fleitmann, P Ackermann, JG Rittig, AM Schweidtmann, A König, ...
9th International Conference Fuel Science-From Production to Propulsion …, 2021
2021
Physical Graph Neural Networks for Prediction of Fuel Ignition Quality
AM Schweidtmann, JG Rittig, A König, M Grohe, A Mitsos, M Dahmen
2020 Virtual AIChE Annual Meeting, 2020
2020
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