Purification of Olive Mill Wastewater Using Microfiltration Membrane Technology
Bibliographic record
Abstract
Olive mill wastewater (OMWW), a by-product of the olive oil extraction process, is a severe polluting waste, but also a source of antioxidants; polyphenols, especially hydroxytyrosol. This study aimed at investigating the potential of microfiltration (MF) for separating the polyphenols from OMWW. OMWW treatment consisted of a preliminary centrifugation step, followed by MF for the separation of fats and polyphenols.Two types of ceramic MF membranes were used. MF flux ranged between 78 and 95 kg m-2 h-1, indicating the applicability of the described process on commercial scale. Better results were obtained with MF membrane of 50 nm pore size, due to its higher porosity compared to the membrane of 200 nm pore size. The optimum operative conditions were transmembrane pressure of 3.5 bar, flow rate of 10 m s-1, and temperature of approximately 55 °C. A 3-month storage of OMWW prior to treatment resulted in a 20% decrease in permeate flux, indicating that direct processing of the OMWW is necessary. Membrane pollution was not a problem for MF operation and did not affect membrane permeability significantly. Restoring the permeability of water to baseline levels after each use, confirmed the successful cleaning regime applied. The microfiltrate was an excellent antioxidant, which contained useful polyphenols, including hydroxytyrosol, tyrosol, p-coumaric acid, caffeic acid and catechin.
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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 | 0.001 |
| 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 | 0.001 | 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".