Strategies to increase the sugar concentration and overall sugar recovery from steam pretreated wheat straw and corn stover
Bibliographic record
Abstract
Wheat straw is available in western Canada and it is a potential feedstock for bio-ethanol production as it can be effectively fractionated into simple sugars using acid catalyzed, steam pretreatment followed by enzymatic hydrolysis. Steam pretreatment is usually a compromise whereby conditions that facilitate effective enzymatic hydrolysis at low enzyme loadings usually sacrifice the recovery of the hemicellulose component. Previous work that tried to optimize the pretreatment to maximize hemicellulose recovery was usually done at the expense of using unacceptably high enzyme loadings to hydrolyze the cellulosic fraction. The goal in this thesis was to determine the highest possible amount of sugar that could be solubilized after both pretreatment and enzymatic hydrolysis while using low enzyme loadings and high solids concentration. It was anticipated that the optimum conditions for maximizing the total soluble sugar yield would still result in the degradation of a portion of the hemicellulose. The biomass handling conditions were first investigated to identify the best possible conditions to maximize sugar recovery. An optimized moisture content combined with the explosive decompression resulted in the highest xylose recovery. It was also found that H₂SO₄ could be used at a loading of 1.5% w/w to produce a substrate with similar chemical composition, sugar recovery and ease of enzymatic hydrolysis to what was obtained when using 3% SO₂ as the catalyst. The pretreatment conditions were then varied to determine the effect of pretreatment severity on the recovery of total soluble sugars. The highest soluble sugar yield of 75% was obtained after pretreatment at 190°C, 8 min and 1.5% H₂SO₄. This is among the highest sugar yields that have been reported and comparable to those reported when using a three-fold higher enzyme loading. However, at these conditions only 52% of the original xylan was recovered. A less severely treated substrate with 70% xylan recovery achieved a total soluble sugar yield of 72% when the “cellulase mixture” was supplemented with xylanases. Thus, pretreatments at lower severities followed by enzymatic hydrolysis using a “cellulase mixture” with xylanase supplementation may be an effective approach to improve the total soluble sugar yield when processing wheat straw.
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 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 | 0.001 | 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".