Membrane Fouling Prevention and Control Strategies in Pulp and Paper Industry Applications: A Review
Bibliographic record
Abstract
Membrane separation technologies have recently received much attention, from lab-scale studies to full-scale operations, in the forest industry. However, membrane fouling comprises a signifcant obstacle to their broad application. Thus, to ensure cost-effective operation of membrane separation processes, the improved elucidation of membrane fouling and establishment of effective fouling control strategies are critical. Membrane fouling decreases process performance efciency, thereby increasing operational and maintenance costs. Although much research has been conducted in this feld, a review has not yet been undertaken to address membrane fouling in pulp and paper mill applications. Further, membrane fouling is still not fully understood. In this review, a survey of the present state of fouling management strategies in the pulp and paper industry (PPI) is presented alongside the latest advances and innovative methods in fouling management. This article also discusses the key parameters that affect membrane fouling, fouling characterization techniques, as well as fouling mechanisms and strategies that are employed for fouling control. In addition, future research opportunities related to membrane fouling control strategies in both low-pressure membranes and MBRs are proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".