Experimental Analyses and Modeling of Ultrasound-Assisted Fouling Control in Ultrafiltration
Bibliographic record
Abstract
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 (R2ANN = 0.99 and MAE = 1.18, R2RSM = 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 avg = 1.3800e-05, 𝑅2avg =0.9890). 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".