Concentration and Detoxification of Kraft Prehydrolysate by Combining Nanofiltration with Flocculation
Bibliographic record
Abstract
The prehydrolysate stream from a Kraft dissolving pulp mill can be valorized by fermentation of the hemicellulosic sugars into biofuels or bioproducts, such as ethanol or butanol, instead of the typical practice of combustion to produce energy. An obstacle facing the use of Kraft hemicelluloses prehydrolysate for biofuels production is the low sugar concentration and the presence of fermentation inhibitors that include organic acids, furans and phenolic compounds. A precondition to ensure the survival of the fermentation microorganisms and to have high fermentation yields is to remove the inhibitors. Concentration of the prehydrolysate is also necessary to reduce the size of the processing equipment and decrease the energy cost. The purpose of this study was to develop a strategy for the concentration and detoxification of hemicelluloses prehydrolysate prior to its conversion into biofuels. Experiments were conducted to screen and select suitable organic membranes among 7 samples of reverse osmosis, nanofiltration, and ultrafiltration membranes. Three membranes (Dow NF270, Trisep TS40, and Trisep XN45) showed the highest sugar retentions relative to inhibitors removal. They were however not efficient for the removal of the phenolic compounds. It was also found that flocculation with ferric sulfate as coagulant could be utilized as a secondary detoxification step that can be combined with nanofiltration. The optimization of the flocculation step with a jar test showed that the highest phenolics removal (∼80%) can be obtained when the ratio of ferric ions to phenols is 1 g/g, and the pH is between 6.5 and 7.5. A new process concept for the detoxification and concentration has been developed based on these experimental results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".