Enhancing starch hydrolysate syrup purification: long-term ultrafiltration membrane performance under industrial conditions
Bibliographic record
Abstract
Ultrafiltration (UF) membranes based on Polyethersulfone (PES) are extensively used in food and bioprocessing industries for their good mechanical robustness and chemical resistance. In starch and sweetener production, these membranes play a vital role in clarifying and purifying carbohydrate-rich syrups. However, sustaining membrane performance under industrial conditions, characterised by elevated temperatures, high pressures, and chemically complex feed solutions, remains a significant challenge due to progressive fouling, membrane ageing, and irreversible performance decline. This study investigates the long-term performance (32h) of the GR90PP tight PES UF membrane (5 kDa cut-off) from Alfa Laval, operated under representative industrial conditions (60 °C, 8 bar) and using a real DE40 starch hydrolysate syrup. The study focuses on quantifying operational stability and fouling–cleaning behaviour across four filtration-cleaning cycles, including analysis of flux evolution, cleaning efficiency, solute rejection, and membrane integrity. Results revelaled a progressive Relative Flux Reduction (RFR), averaging 44 %, alongside a significant Flux Recovery Ratio (FRR) after cleaning, peaking at 80 % after the last alkaline rinse. Scanning Electron Microscopy (SEM) analysis confirmed a gradual build-up of fouling layer, predominantly organic in nature, contributing to reduced permeability and moderate irreversible fouling (average 21 %). Alkaline cleaning proved significantly more effective than acidic cleaning, highlighting the dominance of organic fouling mechanisms. Overall, the GR90PP membrane demonstrated strong operational stability, chemical compatibility, and cleaning resilience under extended high-temperature UF operation, supporting its suitability for processing of carbohydrate-rich products. These findings provide practical insights for optimising cleaning protocols, defining cleaning frequency, and adjusting operational parameters to sustain membrane performance in industrial food applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".