13 citations · 20 across the 11 of their papers we have counts for
11 papers
Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles
Kai Dahms, Eilien Heinrich, Jochen Schmid +3
The development of nanotherapeutics often involves extensive empirical optimization due to the sensitivity of nanoparticle properties, such as size and polydispersity index (PDI),…
Deep Learning for Model Calibration in Simulation of Itaconic Acid Production
Daria Fokina, Marco Baldan, Constantin Romankiewicz +3
In this study, deep learning is used to estimate kinetic parameters for modeling itaconic acid production based on real batch experiments conducted at different agitation speeds an…
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…
Adaptive discretization algorithms for locally optimal experimental design
Jochen Schmid, Philipp Seufert, Michael Bortz
We develop adaptive discretization algorithms for locally optimal experimental design of nonlinear prediction models. With these algorithms, we refine and improve a pertinent state…
Sequential optimal experimental design for vapor-liquid equilibrium modeling
Martin Bubel, Jochen Schmid, Volodymyr Kozachynskyi +2
We propose a general methodology of sequential locally optimal design of experiments for explicit or implicit nonlinear models, as they abound in chemical engineering and, in parti…
A Decomposition Method for the Hybrid Quantum-Classical Solution of the Number Partitioning Problem
Zongji Li, Tobias Seidel, Michael Bortz +1
Current quantum computers can only solve optimization problems of a very limited size. For larger problems, decomposition methods are required in which the original problem is brok…
An Optimization Case Study for solving a Transport Robot Scheduling Problem on Quantum-Hybrid and Quantum-Inspired Hardware
Dominik Leib, Tobias Seidel, Sven Jäger +5
We present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-t…