Impact of iron-(hydr)oxide functionalized cellulose fibers on petrochemical wastewater treatment
Bibliographic record
Abstract
Petrochemical wastewater is characterized by high levels of suspended solids, dissolved organic matter, and toxic compounds such as naphthenic acids (NAs), which pose significant challenges for conventional treatment technologies. In this study, a flocculation system was developed by combining polyacrylamide (PAM)-based flocculants with iron-grafted cellulose fibers. Jar tests were systematically conducted to evaluate the performance of different PAM types (three cationic and one anionic) at various concentrations (0.5–5 mg/L), both with and without the addition of non-grafted and iron-grafted fibers (50–300 mg/L). Flocculation performance was assessed based on turbidity reduction, total organic carbon (TOC) removal, and NA removal measured by fluorescence. The results demonstrate that iron-grafted fibers significantly enhanced treatment by promoting floc bridging and adsorption. Optimal removal was achieved using 1–2 mg/L of cationic PAM3 combined with 200–250 mg/L of iron-grafted fibers, resulting in up to 66 % TOC removal and over 60 % NA removal after only 3 min of settling. Mechanical pressing of the flocs through a 100 µm mesh further improved water clarity and sludge compactness. Comparisons showed that non-grafted fibers provided moderate improvement, while systems without fibers performed poorly, especially when anionic polymers were used. This study highlights the importance of optimizing the fiber-to-polymer ratio and introduces iron-grafted cellulose as a sustainable and effective flocculation aid for complex industrial effluents. These findings offer new insights into designing low-chemical, coagulant-free, high-efficiency treatment systems suited for decentralized or resource-limited applications and highly contaminated wastewater.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".