Optimization of indirect wastewater characterization using led spectrophotometry: a comparative analysis of regression, scaling, and dimensionality reduction methods.
Chemical Oxygen of Demand
Comparison between characterization techniques
LED spectrophotometer
Total suspended solids
Wastewater characterization
Wastewater quality
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
28 Aug 2024
28 Aug 2024
Historique:
received:
30
04
2024
accepted:
11
08
2024
medline:
28
8
2024
pubmed:
28
8
2024
entrez:
28
8
2024
Statut:
aheadofprint
Résumé
LED spectrophotometry is a robust technique for the indirect characterization of wastewater pollutant load through correlation modeling. To tackle this issue, a dataset with 1300 samples was collected, from both raw and treated wastewater from 45 wastewater treatment plants in Spain and Chile collected over 4 years. The type of regressor, scaling, and dimensionality reduction technique and nature of the data play crucial roles in the performance of the processing pipeline. Eighty-four pipelines were tested through exhaustive experimentation resulting from the combination of 7 regression techniques, 3 scaling methods, and 4 possible dimensional reductions. Those combinations were tested on the prediction of chemical oxygen demand (COD) and total suspended solids (TSS). Each pipeline underwent a tenfold cross-validation on 15 sub-datasets derived from the original dataset, accounting for variations in plants and wastewater types. The results point to the normalization of the data followed by a conversion through the PCA to finally apply a Random Forest Regressor as the combination which stood out These results highlight the importance of modeling strategies in wastewater management using techniques such as LED spectrophotometry.
Identifiants
pubmed: 39196326
doi: 10.1007/s11356-024-34714-8
pii: 10.1007/s11356-024-34714-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Ministerio de Ciencia, Innovación y Universidades
ID : TED2021-132098B-C21
Organisme : Xunta de Galicia
ID : ED431C 2022/46
Informations de copyright
© 2024. The Author(s).
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