Synthesis and efficacy of cactus-banana peels composite as a natural coagulant for water treatment
Bibliographic record
Abstract
Aluminium and ferric salts continue to be used as coagulants in drinking water treatment. Natural coagulants can be used for the same purpose because they are cheaper, locally accessible and environmentally friendly. However, low production yields and high operation costs affect commercial adoption of natural coagulants from individual plants, hence the exploration of performance of their composites. This study evaluated the performance of cactus-banana peels composite as natural coagulant for water treatment because of the low cost nature of the two plants. Design Expert Software was used to design jar test experiments for attainment of optimum mixing ratio of the composite for determining optimum dosage, pH and extraction time for development of performance models. Performance of the coagulant was evaluated based on removal efficiencies of turbidity, total suspended solids (TSS) and Escherichia coli (E.coli). The goodness of fit for developed models was evaluated using R2 values and adequate precision. The optimal composition of the composite was cactus to banana peels ratio 0.62:0.38. The optimally mixed powder had a bulk density of 590 kg/m3 while the extracted liquid coagulant had pH and electrical conductivity of 7.05 and 1123 μs/cm, respectively. The optimum dosage, pH and extraction time were 12.25 ml/l, 7.31 and 26.53 min, respectively. Turbidity, TSS and E. coli removal efficiencies were 87.13, 82.15 and 84.02%, respectively. These results indicated good performance of the composite coagulant in water treatment compared to 82–99% for alum, the most commonly used commercial coagulant.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".