Finite Element Model to Predict the Bioconversion Rate of Glucose to Fructose using <i>Escherichia coli</i> K12 for Sugar Production from Date
Bibliographic record
Abstract
Abstract In this study, Bioconversion process of glucose to fructose from date syrup using Escherichia coli K12 is modeled using a commercial computational fluids dynamics (CFD) code fluent FLUENT 6.3.23 [8] which we implemented a user-defined functions (UDF) to simulate the interrelationships at play between various phases. A two phases CFD model was developed using an Eulerian – Eulerian approach to calculate the fructose volume fraction produced during time. The bioconversion process was studied as function of three initial concentration of glucose (0.14, 0.242 and 0.463gL –1 ), three induction time (60, 120 and 180 mn) and three inoculum volume (100, 120 and 150mL). The numerical results are compared with experimental data for bioconversion rate and show good agreement (R 2 = 0.894). The optimal condition of diffusion was obtained by applying an initial concentration of glucose less than 0.2gL –1 and induction time great than 100 minutes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".