Evaluation of Industrial Wastewaters as Low-Cost Resources for Sustainable Enzyme Production by Bacillus Species
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
The increasing demand for industrial enzymes calls for cost-effective and sustainable production strategies. This study investigates the potential of industrial wastewater as an alternative fermentation medium for enzyme synthesis, aligning with the principles of the circular bioeconomy. Four wastewater types from Québec, Canada—beverage wastewater (BW), pulp and paper mill activated sludge (PPMS), food industry wastewater (FIW), and starch industry wastewater (SIW)—were evaluated for their potential to support protease, amylase, and lipase production using Bacillus licheniformis, Bacillus amyloliquefaciens, and Bacillus megaterium. Initial screening identified SIW as optimal for amylase production with B. amyloliquefaciens, and PPMS for protease production with B. megaterium. Optimization using the Box–Behnken design was then performed, followed by scale-up experiments in 5 L bioreactors. B. amyloliquefaciens achieved 5.73 ± 0.01 U/mL of amylase at 48 h under 40 g/L total solids, 30 °C, and a 2% inoculum size, while B. megaterium produced the highest protease of 55.41 ± 3.54 U/mL at 24 h. Lipase production remained negligible across all media and strains. These findings demonstrate the feasibility of the potential of wastewater-based enzyme production, reducing reliance on expensive synthetic substrates, mitigating environmental burdens, and contributing to the transition to a circular bioeconomy.
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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 | 0.001 | 0.002 |
| 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