Optimal Intermittent Ultrasound-Assisted Latex Particle Fouling Remediation in Ultrafiltration
Bibliographic record
Abstract
The ultrafiltration process is a promising technology and considered the most effective method for diversified water and wastewater treatment. However, commercialization for this process has been limited due to the decline of the permeation flux. This decline is the result of membrane fouling that is an accumulation of suspended particles on the surface of the membrane. These particles will hinder the permeate solution that passes through the porous membrane. The objective of this study was to evaluate the effect of ultrasound (US) on the ultrafiltration process. Ultrasound has a cavitation effect that facilitates continuous membrane cleaning; collapse of cavitation bubbles produced can give the energy to remove particles from the membrane. The size and velocity of bubbles are determined by the US frequency and power, respectively. This research focused on using different frequencies (20 kHz, 28 kHz, and 40 kHz) and different power intensities (50 W, 60W, and 100 W). It was found that the flux enhancement at 20 kHz and 28 kHz were significant, while the flux enhancement at 100 kHz was not noticeable. Intermittent ultrasound was tested as to lower the cost of production and increase the lifetime of the membrane. Intermittent ultrasound was controlled by a timer with different time intervals (30 seconds on and 60 seconds off, 60 seconds on and 60 seconds off, and lastly, 90 seconds on and 90 seconds off,).The feed solid concentration used in the experiments was at 0.5wt% of latex paint. Ultrasound irradiation affected the cake layer resistance and concentration polarization which led to the reduction in the total resistance and increase in the permeate flux. Simultaneously, the flux at 20kHzand 28 kHz was higher than that at 40 kHz. Experimental results demonstrate the frequency at 28 kHz can produce better results in terms of enhancing the permeate flux. Overall, utilizing low frequency and low pressure can produce better results for enhancing the permeate flux and lowering the concentration of polarization and cake layer resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".