Bacillus licheniformis proteases as high value added products from fermentation of wastewater sludge: pre-treatment of sludge to increase the performance of the process
Bibliographic record
Abstract
Wastewater sludge is a complex raw material that can support growth and protease production by Bacillus licheniformis. In this study, sludge was treated by different thermo-alkaline pre-treatment methods and subjected to Bacillus licheniformis fermentation in bench scale fermentors under controlled conditions. Thermo-alkaline treatment was found to be an effective pre-treatment process in order to enhance the proteolytic activity. Among the different pre-treated sludges tested, a mixture of raw and hydrolysed sludge caused an increase of 15% in the protease activity, as compared to the untreated sludge. The benefit of hydrolysis has been attributed to a better oxygen transfer due to decrease in media viscosity and to an increase in nutrient availability. Foam formation was a major concern during fermentation with hydrolysed sludge. The studies showed that addition of a chemical anti-foaming agent (polypropylene glycol) during fermentation to control foam could negatively influence the protease production by increasing the viscosity of sludge.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".