Benefits and Limitations of Artificial Neural Networks in Process Chromatography Design and Operation

Due to the progressive digitalization of the industry, more and more data is available not only as digitally stored data but also as online data via standardized interfaces. This not only leads to further improvements in process modeling through more data but also opens up the possibility of linking...

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Bibliographic Details
Published in:Processes 2023-04, Vol.11 (4), p.1115
Main Authors: Mouellef, Mourad, Vetter, Florian Lukas, Strube, Jochen
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial neural networks
Availability
Chromatography
Design of experiments
Digital technology
Digital twins
Digitization
Fluid dynamics
Machine learning
Neural networks
Optimization
Parameter estimation
Partial differential equations
Training
Twins
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Summary:Due to the progressive digitalization of the industry, more and more data is available not only as digitally stored data but also as online data via standardized interfaces. This not only leads to further improvements in process modeling through more data but also opens up the possibility of linking process models with online data of the process plants. As a result, digital representations of the processes emerge, which are called Digital Twins. To further improve these Digital Twins, process models in general, and the challenging process design and development task itself, the new data availability is paired with recent advancements in the field of machine learning. This paper presents a case study of an ANN for the parameter estimation of a Steric Mass Action (SMA)-based mixed-mode chromatography model. The results are used to exemplify, discuss, and point out the effort/benefit balance of ANN. To set the results in a wider context, the results and use cases of other working groups are also considered by categorizing them and providing background information to further discuss the benefits, effort, and limitations of ANNs in the field of chromatography.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr11041115