Optimisation of orthophosphate and turbidity removal using an amphoteric chitosan-based flocculant–ferric chloride coagulant system
Bibliographic record
Abstract
Environmental context The fate and build-up of phosphate nutrients in aquatic environments is an urgent environmental problem affecting global water security. This study, guided by a statistical design method, optimises the flocculation properties of a biopolymer for removing orthophosphate from water. This improved technology has potential widespread applications for removal of orthophosphate from water and wastewater treatment systems. Abstract A coagulation-flocculation process was employed to remove turbidity (Ti) and orthophosphate (Pi) in aqueous media using a ferric chloride (FeCl3) and a grafted carboxymethyl chitosan (CMC) flocculant system. The amphoteric CMC-CTA flocculant was synthesised by grafting 3-chloro-2-hydroxypropyl trimethylammonium chloride (CTA) onto the biopolymer backbone of CMC. Here, CMC-CTA denotes the covalent grafting of CTA onto CMC. Optimisation of the variables for Pi and Ti removal was conducted using a jar test system based on the experimental design obtained from the response surface methodology (RSM). The Box–Behnken design was used to evaluate the individual and interactive effects of four independent variables: CMC-CTA dosage, FeCl3 dosage, pH and settling time. The RSM analysis showed that the experimental data followed a quadratic polynomial model with the following optimal conditions: [CMC-CTA] = 3.0 mg L-1, [FeCl3] = 10.0 mg L-1, pH 6.8 and settling time = 35 min. Optimum conditions led to a Pi removal of 96.4 % and turbidity removal of 96.7 % based on the RSM optimisation, in good agreement with experimental results with an initial concentration of 30.0 mg Pi L-1. The coagulation-flocculation process is characterised by a combination of electrostatic charge neutralisation, polymer bridging and a polymer adsorption mechanism.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".