107 citations · 186 across the 17 of their papers we have counts for
18 papers
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
Jennifer Werner, Justus Arweiler, Indra Jungjohann +4
Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rare…
On Computing and Pricing of Adjustable Robust Chemical Process Designs
Jan Schwientek, Katrin Teichert, Jan Schröder +6
Model-based process simulation can be used to derive designs and operating conditions of chemical processes that optimally balance multiple objectives, such as quality, costs, or e…
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
DiffStyleTS: Diffusion Model for Style Transfer in Time Series
Mayank Nagda, Phil Ostheimer, Justus Arweiler +13
Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning mo…
Pareto-optimal treatment of uncertainties in model-based process design and operation
Jan Schwientek, Katrin Teichert, Jan Schröder +5
Model-based process design and operation involves here-and-now and wait-and-see decisions. Here-and-now decisions include design variables like the size of heat exchangers or the h…
Multi-Criteria Shape Optimization of Flow Fields for Electrochemical Cells
Sebastian Blauth, Marco Baldan, Sebastian Osterroth +7
We consider the shape optimization of flow fields for electrochemical cells. Our goal is to improve the cell by modifying the shape of its flow field. To do so, we introduce simula…
Deep Anomaly Detection on Tennessee Eastman Process Data
Fabian Hartung, Billy Joe Franks, Tobias Michels +15
This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tenn…
Making Thermodynamic Models of Mixtures Predictive by Machine Learning: Matrix Completion of Pair Interactions
Fabian Jirasek, Robert Bamler, Sophie Fellenz +4
Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (c…
Computing T-optimal designs via nested semi-infinite programming and twofold adaptive discretization
David Mogalle, Philipp Seufert, Jan Schwientek +2
Modeling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested,…
Calibrated simplex-mapping classification
Raoul Heese, Jochen Schmid, Michał Walczak +1
We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers…
Two-phase approaches to optimal model-based design of experiments: how many experiments and which ones?
Charlie Vanaret, Philipp Seufert, Jan Schwientek +5
Model-based experimental design is attracting increasing attention in chemical process engineering. Typically, an iterative procedure is pursued: an approximate model is devised, p…
An Adaptive Algorithm based on High-Dimensional Function Approximation to obtain Optimal Designs
Philipp Seufert, Jan Schwientek, Michael Bortz
Algorithms which compute locally optimal continuous designs often rely on a finite design space or on repeatedly solving a complex non-linear program. Both methods require extensiv…
Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification
Raoul Heese, Michael Bortz
We present a novel adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. Our method i…
CupNet -- Pruning a network for geometric data
Raoul Heese, Lukas Morand, Dirk Helm +1
Using data from a simulated cup drawing process, we demonstrate how the inherent geometrical structure of cup meshes can be used to effectively prune an artificial neural network i…
Simulation and optimal control of the Williams-Otto process using Pyomo
Jochen Schmid, Katrin Teichert, Moncef Chioua +2
We illustrate the advantages the high-level open-source software package Pyomo has in rapidly setting up and solving dynamic simulation and optimization problems. In order to do so…
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Fabian Jirasek, Rodrigo A. S. Alves, Julie Damay +6
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibr…
The Good, the Bad and the Ugly: Augmenting a black-box model with expert knowledge
Raoul Heese, Michał Walczak, Lukas Morand +2
We address a non-unique parameter fitting problem in the context of material science. In particular, we propose to resolve ambiguities in parameter space by augmenting a black-box…
Optimized data exploration applied to the simulation of a chemical process
Raoul Heese, Michal Walczak, Tobias Seidel +2
In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class…