Utilizing biochar from dairy sludge for effective dairy wastewater treatment: a sustainable approach
Bibliographic record
Abstract
This study explores the potential of dairy processing sludge (DPS) as a resource for producing biochar to treat dairy wastewater. DPS contains hazardous pollutants such as antibiotics, hormones, pesticides, disinfectants, and microplastics, posing environmental risks. Through pyrolysis at 250°C–350°C, DPS was converted into biochar using an innovative, low-cost, small-scale system called BioCharan, which utilises a modified mustard oil tin for on-site production. The resulting biochar displayed favourable characteristics, including 40.067% carbon, 5.354% hydrogen, and 2.743% nitrogen content, confirmed through Fourier-transform infrared and X-ray diffraction analysis. A filtration system combining this biochar with river sand was developed and tested for its pollutant removal efficiency. The system effectively removed total suspended solids (83.71%), oil and grease (66.82%), chemical oxygen demand (42.86%), biochemical oxygen demand (31.55%), sulphate (30.77%), phosphate (26.67%), nitrate (25%), total Kjeldahl nitrogen (23.53%), total dissolved solids (11.72%), and fluoride (1.72%), while also improving pH from 6.30 to 6.74. Key parameters like porosity and surface area supported the biochar’s strong adsorptive capabilities. This research highlights a sustainable approach by converting sludge waste into an effective treatment medium, offering an eco-friendly solution for both waste disposal and water purification, thus presenting a circular strategy in dairy wastewater management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".