Recovery approaches for sulfuric acid from the concentrated acid hydrolysis of lignocellulosic feedstocks: A mini-review
Bibliographic record
Abstract
The two-step concentrated acid hydrolysis approach is a very efficient method for the generation of monomeric sugars from lignocellulosic biomass, due to its ability to generate near-theoretical sugar yields, at moderate processing times. Sulfuric acid is the most frequently employed acid due to its combination of low cost, high efficiency, and low toxicity and corrosivity compared to other acids. A challenge with this approach is the ability to recover and re-use the acid catalyst in order to make the process economical and reduce waste streams. This paper reviews different acid recovery techniques found in literature and compare them based on performance. The three most investigated and best performing approaches are ion exclusion chromatography, solvent extraction and electrodialysis. Of these, ion exclusion chromatography is the most investigated, and also applied at bigger scale for recovering acid from the final sugar hydrolysate stream. Solvent extraction is a popular option when acid recovery is performed on the intermediate product stream in the two-step process. The studied techniques achieve high acid recovery performance and acid-sugar separation efficiencies, with acid recoveries of 90–99% reported, with low loss of sugars (higher than 90% glucose yields). More research is required into the impact on process performance of re-using the acid catalyst after recovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".