Version 1
: Received: 6 June 2023 / Approved: 7 June 2023 / Online: 7 June 2023 (13:07:38 CEST)
How to cite:
Belding, J.; Hannoun, D. Exploration of a Filtration Model Variable Space using Machine Learning. Preprints2023, 2023060555. https://doi.org/10.20944/preprints202306.0555.v1
Belding, J.; Hannoun, D. Exploration of a Filtration Model Variable Space using Machine Learning. Preprints 2023, 2023060555. https://doi.org/10.20944/preprints202306.0555.v1
Belding, J.; Hannoun, D. Exploration of a Filtration Model Variable Space using Machine Learning. Preprints2023, 2023060555. https://doi.org/10.20944/preprints202306.0555.v1
APA Style
Belding, J., & Hannoun, D. (2023). Exploration of a Filtration Model Variable Space using Machine Learning. Preprints. https://doi.org/10.20944/preprints202306.0555.v1
Chicago/Turabian Style
Belding, J. and Deena Hannoun. 2023 "Exploration of a Filtration Model Variable Space using Machine Learning" Preprints. https://doi.org/10.20944/preprints202306.0555.v1
Abstract
Within this paper, a machine learning algorithm is used to investigate the importance of certain setpoints and parameters in the filtration processes of a large-scale water treatment facility. Previously, a model for the filtration process based on Run-to-Run Control was proposed and tested against sample data from the treatment plant, but it was quickly found that such a model was incompatible for successfully computing setpoints of operation which minimize the energy cost of running the filtration systems. The machine learning model described herein is an attempt to elucidate the importance of the available data on the filtration systems and to identify the most important variables that influence the filtration run time.
Keywords
Filtration, Water Treatment, Water Management, Machine Learning, Run-to-Run Control
Subject
Environmental and Earth Sciences, Water Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
