Improving Sugar Yields and Reducing Enzyme Loadings in the Deacetylation and Mechanical Refining (DMR) Process through Multistage Disk and Szego Refining and Corresponding Techno-Economic Analysis
Bibliographic record
Abstract
Abstract Deacetylation and mechanical refining (DMR) has the potential to be a highly efficient biochemical conversion process for converting biomass to low toxicity, high concentration sugar streams. To increase the cost-effectiveness of the DMR process, improvements in enzymatic sugar yields are needed, in addition to reducing the refining energy consumed, and decreasing the enzyme usage. In this study, a second refining step utilizing a Szego mill was introduced, resulting in significant improvements in sugar yields in enzymatic hydrolysis at equivalent or lower refining energy inputs. The multistage DMR process increased the monomeric glucose and xylose yields to approximately 90% and 84%, respectively, with an energy consumption of 200 kWh/ODMT. SEM imaging revealed that Szego milling caused significant surface disruption and severe maceration and delamination of the biomass structure. Our results show that the DMR process is a very promising process for the biorefinery industry in terms of economic feasibility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".