On the Performance of Machine Learning Methods for Breakthrough Curve
Prediction
- URL: http://arxiv.org/abs/2204.11719v1
- Date: Mon, 25 Apr 2022 15:27:03 GMT
- Title: On the Performance of Machine Learning Methods for Breakthrough Curve
Prediction
- Authors: Daria Fokina (1 and 2), Oleg Iliev (1 and 2 and 3), Pavel Toktaliev (1
and 2), Ivan Oseledets (4), Felix Schindler (5) ((1) Fraunhofer ITWM, (2)
Technische Universit\"at Kaiserslautern, (3) Institute of Mathematics and
Informatics, Bulgarian Academy of Sciences, (4) Skolkovo Institute of Science
and Technology, (5) Westf\"alische Wilhelms-Universit\"at M\"unster)
- Abstract summary: In connection with reactive flows in porous media, the term breakthrough curve is used to denote the time dependency of the outlet concentration.
In this work we apply several machine learning methods to predict breakthrough curves from the given set of parameters.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reactive flows are important part of numerous technical and environmental
processes. Often monitoring the flow and species concentrations within the
domain is not possible or is expensive, in contrast, outlet concentration is
straightforward to measure. In connection with reactive flows in porous media,
the term breakthrough curve is used to denote the time dependency of the outlet
concentration with prescribed conditions at the inlet. In this work we apply
several machine learning methods to predict breakthrough curves from the given
set of parameters. In our case the parameters are the Damk\"ohler and Peclet
numbers. We perform a thorough analysis for the one-dimensional case and also
provide the results for the three-dimensional case.
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