Application of a generalized hybrid machine learning model for the prediction of H2S and VOCs removal in a compact trickle bed bioreactor (CTBB).
Chemosphere
; 360: 142181, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-38685329
ABSTRACT
This study presents a generalized hybrid model for predicting H2S 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 <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model. The results from classification model simulation showed high goodness of fit for the classification models to predict the removal of H2S and VOCs (SPEC = 0.94-0.99, SENS = 0.96-0.99). It was shown that the hybrid K-NN model applied for the "Klimzowiec" WWTP, including the pilot plant, can also be applied to the "Urbanowice" WWTP. The hybrid machine learning model enables the development of a universal system for monitoring the removal of H2S and VOCs from WWTP facilities.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Reactores Biológicos
/
Compuestos Orgánicos Volátiles
/
Aprendizaje Automático
/
Sulfuro de Hidrógeno
Idioma:
En
Revista:
Chemosphere
Año:
2024
Tipo del documento:
Article
País de afiliación:
Polonia