Study of Separation and Fouling of Reverse Osmosis Membranes during Model Hydrolysate Solution Filtration
Bibliographic record
Abstract
Prehydrolysate, a dilute solution consisting mainly of pentoses, hexoses, and lesser quantities of organic acids, furfural and phenolics, is generated in the Kraft dissolving pulp process. An obstacle facing the valorization of the solution in hemicellulose biorefineries, by conversion of the sugars into bioproducts such as furfural, is the low sugar concentration. Membrane filtration is typically proposed in several hemicellulose based biorefineries for concentrating the solution, although they are usually generated using different wood species, pretreatment methods, and operating conditions. However, the chemical composition of the solutions is generally not considered. Also, the combined effect of composition and operating conditions is rarely investigated for biorefinery applications. The purpose of this work was to determine the impact of the prehydrolysate composition and operating parameters on the component separation and permeate flux during membrane filtration. Using model prehydrolysate solutions, two commercial reverse osmosis (RO) membranes were screened, and one was selected for use, based on its higher sugar and acetic acid retention. A Taguchi L18 experimental design array was then applied to determine the dominant parameters and limiting factors. Results showed that the feed pressure and temperature have the highest impact on permeate flux, but the least effect on sugar retention. Further experiments to quantify flux decline, due to fouling and osmotic pressure, showed that furfural has the highest membrane fouling tendency, and can limit the lifetime of the membrane. Regeneration of the membrane by cleaning with a sodium hydroxide solution is also effective for reversing fouling. It has been demonstrated that RO can efficiently and sustainably concentrate wood prehydrolysate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".