1 citations · 2 across the 5 of their papers we have counts for
5 papers
A Machine Learning-Fueled Modelfluid for Flowsheet Optimization
Martin Bubel, Tobias Seidel, Michael Bortz
Process optimization in chemical engineering may be hindered by the limited availability of reliable thermodynamic data for fluid mixtures. Remarkable progress is being made in pre…
Reusable Surrogate Models for Distillation Columns
Martin Bubel, Tobias Seidel, Michael Bortz
Surrogate modeling is a powerful methodology in chemical process engineering, frequently employed to accelerate optimization tasks where traditional flowsheet simulators are comput…
An equation-based batch distillation simulation to evaluate the effect of multiplicities in thermodynamic activity coefficients
Jennifer Werner, Jochen Schmid, Lorenz T. Biegler +1
In this paper, we investigate the influence of multiplicities in activity coefficients on batch distillation processes. In order to do so, we develop a rigorous simulation of batch…
Influences of Uncertainties in Thermodynamic Models on Pareto-optimized Dividing Wall Columns for Ideal Mixtures
Lea Trescher, David Mogalle, Patrick Otto Ludl +3
This article examines the effect of individual and combined uncertainties in thermodynamic models on the performance of simulated, steady-state Pareto-optimized Dividing Wall Colum…
Cubature-based uncertainty estimation for nonlinear regression models
Martin Bubel, Jochen Schmid, Maximilian Carmesin +3
Calibrating model parameters to measured data by minimizing loss functions is an important step in obtaining realistic predictions from model-based approaches, e.g., for process op…