Enhancing biomass hydrolysis for biofuel production through hydrodynamic modeling and reactor design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A computational fluid dynamics model was developed to represent high‐solids enzymatic hydrolysis. This model accounted for the transient and multiphase (solids‐slurry) nature of the high‐solids enzymatic hydrolysis process. The model investigated the effect of slurry viscosity, rotational speed, and two impeller configurations on the distribution of insoluble solids. Initial CFD results identified segregation of the velocity contours for the non‐Newtonian slurry, which could potentially affect the reactor performance. The multiphase, transient CFD simulations showed that the first impeller configuration delayed the distribution of solids, and compartmentalized mixing in the reactor. The second impeller configuration, meanwhile, improved solids mixing and hydrolysis, while using lower rotational speeds (and thus, energy). The second impeller configuration also expanded the size of the pseudo‐cavern between impellers, which is critical for better dispersion of the solids. The CFD trends of the second impeller configuration were experimentally verified by conducting fed‐batch, high‐solids enzymatic hydrolysis trials with pretreated lignocellulose. The experimental results showed that the second impeller configuration provided better mixing of the non‐Newtonian slurry and enhanced solids‐enzyme interactions, leading to improved glucan‐to‐glucose conversion. This work illustrates that a transient multiphase CFD model can provide valuable insights into the design and optimization of high‐solids enzymatic hydrolysis reactors. The CFD model has identified pathways to improve the distribution of solids while reducing the energy needed for mixing. The CFD model can also guide experimental and design work to scale up these reactors from the laboratory to pilot and commercial scale.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it