Modelling of drinking water treatment and disinfection by-product formation with artificial neural networks
Notice bibliographique
Résumé
The source water for Coquitlam Water Treatment Plant (CWTP) originates from a watershed located in the mountains north of the City of Vancouver (BC, Canada), providing approximately 20% of the water demand for the metropolitan area. Treatment at CWTP consists of ozonation, followed by UV for primary disinfection and chlorination for secondary disinfection. Ozone is used to increase the UV transmittance (UVT) of the water, and to reduce the formation of chlorinated disinfection by-products (DBP) in the distribution system. Ozone addition at the CWTP is currently dosed proportionally to the flow being treated. This approach does not take into consideration the complex interactions that exist between varying raw water characteristics and ozone, and how these impact changes in UVT or DBP formation. Advanced numerical computational techniques, such as artificial neural networks (ANN), are increasingly being used to objectively identify optimal operating setpoints for complex systems. In the present study, two sets of ANN models were developed to optimize ozone addition for effective UV treatment and control of DBP formation. The first, the treatment system operation models, were used to predict pre-chlorination UVT, used as surrogate for the DBP formation potential, based on raw water characteristics and CWTP operational setpoints. The second, the distribution system models were used to predict the formation of total haloacetic acids (HAA), total trihalomethanes (THM) and two HAA fractions (DCAA and TCAA) in the distribution system based on raw and treated water characteristics. The treatment models could accurately predict pre-chlorination UVT. A moderate correlation was also observed between the measured and predicted DBP concentrations using the distribution system models, even though significant scatter was observed. This was likely due to the small available dataset and lack of reliable estimations of retention time and chlorine concentration in the distribution system. Scenario analyses with selected models were performed to investigate possible operational benefits of the implementation of these machine learning algorithms models to control ozone dosing. These suggested that savings could be achieved and high and constant level of pre-chlorination UVT could be maintained if ozone was dosed using these artificial neural network models.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».