Models to optimise bioengineering of banana wastes to animal feeds and fertilisers
Bibliographic record
Abstract
Microbial growth models are usually employed to describe the growth kinetics of microorganisms under optimised operational conditions. Much time and resources are involved in running central composite or Box–Behnken full factorial designs of the experiment (DoEs) before the optimum conditions are attained. In this study, a Taguchi model (two-level) was used for DoEs and kinetic parameters derived from the mathematical models were used for process optimisation. This resulted in running less ‘robust’ anaerobic digestion experiments for the varying selected parameters – that is, pH and temperature. Chapman–Richards model results were considered to represent experimental data and were compared with the kinetic parameters derived from the Bergter, Andrews and Contois models. Also, a pure population of Saccharomyces cerevisiae (Saf-instant) was used, unlike in many studies where mixed populations are employed for anaerobic digestion. From the model results, a pH of 4·6–5·6 and a temperature of 25–30°C were considered optimum for S. cerevisiae anaerobic growth on banana waste. Chemical characterisation of the digested banana wastes under optimised conditions indicated an increase in proteins and lipids by 7 and 5%, respectively, and an approximately 8% mineral content increase was also noted. The waste could also be used as a nutrient-rich fertiliser and chicken feed supplement.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".