Application of a generalized hybrid machine learning model for the prediction of H 2 S and VOCs removal in a compact trickle bed bioreactor (CTBB)

This study presents a generalized hybrid model for predicting H S and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification m...

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Bibliographic Details
Published in:Chemosphere (Oxford) 2024-04, Vol.360, p.142181
Main Authors: Barbusiński, Krzysztof, Szeląg, Bartosz, Parzentna-Gabor, Anita, Kasperczyk, Damian, Rene, Eldon R
Format: Article
Language:English
Online Access:Get full text
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Summary:This study presents a generalized hybrid model for predicting H S and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K
ISSN:1879-1298