Optimization studies on turbidity removal from cosmetics wastewater using aluminum sulfate and blends of fishbone
Bibliographic record
Abstract
Abstract This research is centered on the optimization of coagulation–flocculation treatment of cosmetic wastewater. It analyzes blends of fishbone (BFB) and aluminum-based coagulant (ABC) to determine the efficacy of BFB as a potential coagulant–flocculants aid at optimum conditions using response surface methodology (RSM). The experiment was carried out employing the standard nephelometric procedure at 1000 rpm stirring rate. The central composite design (CCD) was used to examine the interactions of pH, dosage, and settling time to maximize the turbidity removal efficiency of the ABC- and BFB-driven coag–flocculation. The optimal pH, dosage, and settling time for ABC were obtained as 10, 0.1 g/L, and 2 min, while pH 6, 0.4 g/L, and settling time of 4 min were recorded for BFB following the established quadratic model of the RSM. The removal efficiency of ABC and BFB plots 80% and 88%, respectively; this corresponds to 262 NTU and 288 NTU of removal from the wastewater at optimal conditions. The kinetics result indicated that the rate constant (Kf) 3 × 10−3 (L/g min) of BFB surpassed 5 × 10−5 (L/g min) recorded for ABC following second-order coag–flocculation reaction, with correlation coefficients (R2) values of 0.999 and 0.9985, respectively. The research also applied cost–benefit analysis for the determination of the efficacy of BFB. The figure obtained shows that the benefit of using BFB will save $5.50 compared to ABC based on this work. At optimal conditions, BFB satisfied the environmental protection agency pH standard for industrial wastewater discharge, promising coagulant–flocculants aid for industrial wastewater purification purpose and the preservation of the environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".