The influence of multiple fouling and cleaning cycles upon the performance of polyethersulphone membrane filters during coffee extract decaffeination
Bibliographic record
Abstract
Filtration processes have been applied widely in food processing industry over the recent decades and could offer a viable alternative to the use of solvents in the decaffeination process, comprising low operational costs, high selectivities and mild processing conditions. This paper reports the development and evaluation of the fouling occurring in multiple filtration cycles during the selective reduction of caffeine from coffee brews comparing the performance of (i) a commercially available synthetic tight ultrafiltration (TUF) polyethersulphone (PES) membrane (GP95PP – Alfa Laval) and (ii) a self-made mixed matrix (MMMs) PES membranes (PSCD) fabricated in-house. The effectiveness and performance of the PES MMMs was benchmarked against the commercial 2 kDa PES membranes showing promising results. A cross-flow rig was operated at transmembrane pressures of 2–9 bar and cross-flow velocities (CFV) of 0.04–0.1 m/s at 25 °C. The flux decline and recovery along with changes in the key component rejection and resistances are reported for multiple fouling and cleaning cycles. PSCD exhibited a higher permeate flux of ca. 10.5 L m-2 h-1 compared to the GR95PP membranes, which exhibited a permeate flux of 6.1 L m-2 h-1 over the 29 h filtration period selected, at 9 bar and a CFV of 0.04 m/s. The rejection ratios of the GR95PP and PSCD membranes were monitored for three consecutive filtration cycles, and showed values ca. 30% and 35% for caffeine,> 90% and ∼90% for both polyphenols & proteins, & ∼ 80% for melanoidins, respectively. An effective cleaning protocol was reported, comprised of 0.5 wt% NaOH at 50 °C, exhibiting cleaning efficiencies (CE)> 99%. FT-IR data indicated the presence of key compounds residuals after membrane cleaning. Modification to the membrane surface occurs due to fouling, altering the hydrophobicity. Differences in filtration performance and cleanability between the two classes of membranes are identified and linked to variations in surface and structure properties.
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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 | 0.001 |
| 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 | 0.001 | 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".