The Effect of Fed-Batch Operation and Rotational Speed on High-Solids Enzymatic Hydrolysis of Hardwood Substrates
Bibliographic record
Abstract
Enzymatic hydrolysis of hardwood substrates yields soluble sugars, which are a carbon source for producing ethanol and butanol. However, as the substrate and water are mixed, a viscous, heterogeneous slurry forms. The result is reduced mixing performance, mass transfer, and enzyme-substrate contact, all of which negatively affect sugar titers. In this study, enzymatic hydrolysis of a hardwood substrate at 20 wt% was conducted in a 10L stirred tank reactor. We investigated the dynamic changes in slurry behavior, as well as the interactions among mixer design/operation and fed-batch strategies. The effects of the number and frequency of substrate additions, impeller configuration, and high and moderate agitation speeds were evaluated using torque measurements and in terms of glucan-to-glucose conversion. Fewer additions corresponds to batch operation and negatively impacted solids distribution, accurate torque readings, and glucan conversion. However, as the number of additions incrementally increased, glucan conversion increased, torque readings captured the resistance of the fluid to flow, and the solids distribution within the reactor improved. Moderate agitation speeds of 60 revolutions per minute (rpm), combined with smaller and more frequent fiber additions, led to increased glucan conversion and less torque resistance compared to similar experiments conducted at high rotational speeds (150 rpm), and the use of different impeller sizes and configurations impacted the distribution of the slurry. This work illustrates that the implementation of different fed-batch additions coupled with different impeller types and configurations promotes hardwood slurry blending, accommodates the increase in viscosity, and enables quantification of energy and power consumption.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".