Experimental Analyses and Modeling of Ultrasound-Assisted Fouling Control in Ultrafiltration
Notice bibliographique
Résumé
<p dir="ltr">Membrane fouling presents a prevalent challenge in filtration processes, frequently requiring cleaning procedures that can result in system downtime, lost productivity, and escalating operational costs. Ultrasonic (US) irradiation is an alternative technique, applied to the feed solution passing across the membrane surface, to either prevent fouling formation (fouling control) or dislodge the foulants (surface cleaning). In the present work, in-situ ultrasound was employed in an ultrafiltration process of a skimmed milk solution. Through the implementation of ultrasound to alleviate fouling formation, the primary goal of this investigation is to extend the operational life of the filtration system and diminish the frequency of expensive cleaning procedures. As a consequence, heightened production efficiency can be achieved, as system downtime will be eliminated. For the first time, a novel synchrotron X-ray high-contrast imaging technique available at the Canadian Light Source (CLS) was employed for the real-time visualization of US-generated microbubbles and their influence on fouling control. The obtained results showed that the number of microbubbles increased with increasing US power (50 W to 100 W) and frequency (20 KHz to 40 kHz). Larger microbubbles were captured at the higher US power (100 W) and lower frequency (20 kHz). To examine the influence of operational parameters on the filtration performance, the response surface methodology (RSM) was employed; and it was observed that the permeate flux was improved at higher US power (100 W) and feed flow rate (3.0 L/min), lower US frequency (20 kHz) and feed solid concentration (0.10 wt.%). The maximum flux improvement of 67% was obtained at 0.50 wt.%, 1.0 L/min, 28 kHz, and 50 W. A mathematical model was also generated under RSM using Design Expert software. A set of data employed in RSM (Design Expert software) were also used to train an Artificial Neural Network (ANN), which better fitted the experimental data and showed a stronger predictive ability (R<sup>2</sup><sub>ANN</sub> = 0.99 and MAE = 1.18, R<sup>2</sup><sub>RSM</sub> = 0.93 and MAE = 2.84). Last but not least, the fouling mechanisms responsible for the filtration flux decline were evaluated using Hermia’s model and fouling mechanisms, of which cake filtration appeared to be the predominant fouling type along with the intermediate blocking model as the second-best fit to the experimental data. Accordingly, Bolton’s combined cake-intermediate model was employed and modified using the correction for the cake resistance and available frontal area. The new modified cake-intermediate model exhibited excellent predictive accuracy and good agreement with US-assisted ultrafiltration results (P-value <i>avg </i>= 1.3800e-05, 𝑅<sup>2</sup><i>avg </i>=0.9890). </p><p dir="ltr">In summary, by employing ultrasound to mitigate fouling, this study sought to extend the operational lifespan of the filtration system and minimize the need for frequent cleaning, resulting in increased overall production efficiency and reduced downtime. Moreover, incorporating statistical and ANN modeling allow to optimize the filtration process, further enhancing the system's efficiency and economic viability. The potential economic advantages and savings resulting from this research make it a crucial endeavor in advancing sustainable and cost-effective separation/filtration technologies for various industries and applications.</p>
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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».