Optimization and modelling of bioethanol production by the fermentation of <scp>CCN</scp> ‐51 cocoa mucilage using the <i>sequential simplex</i> method and the <i>modified Gompertz</i> model
Bibliographic record
Abstract
Abstract This work deals with the optimization of bioethanol production through a fermentation process of CCN‐51 cocoa mucilage, based on increased concentrations of the Saccharomyces cerevisiae yeast. Cocoa mucilage, considered biomass waste, was selected for its high productivity and the large volumes generated in the cocoa industrial chain in Ecuador. The optimization of the fermentation process was performed using the sequential simplex method with two variables, and the results were experimentally confirmed by quantifying bioethanol through the microdiffusion method. The best operational conditions corresponded to a temperature of 35°C and a pH of 4. Regarding the concentration of yeast, it was found that the optimal value was 8 g/L, since lower concentrations led to low productivities, while higher concentrations resulted in inadequate functioning of the bioreactor. The best results reached a productivity of 1.35 ± 0.04 g/L · h and a maximum bioethanol concentration of 28.3 ± 0.8 g/L for a processing time of 21 h. The production of bioethanol was modelled using the modified Gompertz equation and simulated in MATLAB®, yielding a bioethanol production rate of 2.42 g/L · h with a correlation coefficient ( R 2 ) of 0.95. These results contribute to the knowledge of bioethanol production using cocoa mucilage and seek to add a positive value to this residue, whose management and final disposition have both undesirable environmental and economic effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".