Intermittent Ultrasound-Assisted Ceramic Membrane Fouling Control in Ultrafiltration
Bibliographic record
Abstract
Ultrafiltration is one of the most promising membrane technologies for liquid purification due to its high economic efficiency in the industries. However, it has been faced with a critical problem, called fouling. The contaminants in feed solution tend to accumulate on the membrane surface, hindering permeate solution to pass through the porous spaces. Among the various solutions, application of ultrasound has been considered as the most popular method since it does not suffer a disadvantage of downtime and the filtration process does not need to be stopped for the removal of foulants. In this study, control of ceramic membrane fouling by an on-line intermittent ultrasound system was being investigated. The experiment focused on obtaining optimal operating ultrasonic condition. Frequency (20, 28, and 40 kHz), power intensity (1.44, 2.88, and 5.76W/cm2 ), and time interval of intermittent ultrasound (1, 1.5, and 2 minutes) were the parameters of interest. The effect of feed concentration was also analyzed at optimal ultrasonic condition. The quality and flow rate of the permeate streams were monitored for the evaluation of the process performance. The optimal condition of intermittent ultrasound was found at the frequency of 28 kHz and the power intensity of 2.88 W/cm2 ; and then, the application of intermittent ultrasound with short time interval successfully reduced the operating cost of ultrafiltration process while maintaining acceptable quality and flow rate of permeate solution. There was increase in efficiency of intermittent ultrasound at lower feed concentration.
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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".