Kinetic modeling of biosurfactant production by <i>Bacillus subtilis</i> N3-1P using brewery waste
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 Costs associated with production of favorable biologically produced surfactants continue to be a significant obstacle to large scale application. Using industrial wastes and by-products as substrate and optimization of cultural conditions are two strategies of producing biosurfactants with a reasonable price. Also, modeling the biosurfactant production bioprocess improves the commercial design and monitoring of biomass growth, biosurfactant production, and substrate utilization. In this study, the indigenous Bacillus subtilis N3-1P strain and a local brewery waste as the carbon source were used to produce a biosurfactant. The batch cultivation was performed under the optimum conditions. Models describing the biomass growth, biosurfactant production, and substrate utilization were developed by fitting the experimental data to the logistic, Contois and Luedeking-Piret models using MATLAB software and regression analysis. The kinetic parameters including the maximum specific growth rates ( µ max ), the Contois constant ( K ), parameters of the Luedeking-Piret modelswere calculated. Yields including Y X / S , and Y P / X were found to be 0.143 g X/ g S , and 0.188 g P/ g X , respectively. The experimental and predicted model showed good agreement. The developed models are a key step in designing reactors for scale up of biosurfactant production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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