Removal of Model Aromatic Hydrocarbons from Aqueous Media with a Ferric Sulfate–Lime Softening Coagulant System
Bibliographic record
Abstract
The removal of model hydrocarbon oil systems (4-nitrophenol (PNP) and naphthalene) from laboratory water was evaluated using a ferric sulfate and a lime-softening coagulant system. This study addresses the availability of a methodology that documents the removal of BTEX related compounds and optimizes the ferric-based coagulant system in alkaline media. The Box–Behnken design with Response Surface Methodology enabled the optimization of the conditions for the removal (%) of the model compounds for the coagulation process. Three independent variables were considered: coagulant dosage (10–100 mg/L PNP and 30–100 mg/L naphthalene), lime dosage (50–200%), and initial pollutant concentration (1–35 mg/L PNP and 1–25 mg/L naphthalene). The response optimization showed a 28% removal of PNP at optimal conditions: 74.5 mg/L ferric sulfate, 136% lime dosage, and initial PNP concentration of 2 mg/L. The optimal conditions for naphthalene removal were 42 mg/L ferric sulfate, 50% lime dosage, and an initial concentration of naphthalene (16.3 mg/L) to obtain a 90% removal efficiency. The coagulation process was modeled by adsorption isotherms (Langmuir for PNP; Freundlich for Naphthalene). The surface properties of flocs were investigated with pHpzc, solid-state UV absorbance spectra, and optical microscopy to gain insight into the role of adsorption in the ferric coagulation process.
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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) | 0.001 | 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 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".