Efficient Removal of Ammonium, Heavy Metals, and Scale-Forming Cations from Oilfield Produced Water by Sulfonated Biochar
Bibliographic record
Abstract
The treatment of oilfield produced water (OPW), which contains complex pollutants, such as scale-forming cations, heavy metals, ammonia, and organic matter, presents a significant challenge. In this study, we utilized low-cost sulfonated biochar (SBC), produced through in situ sulfonation of alkylated waste sulfuric acid with waste rice husk, to remove these pollutants from actual OPW. Treatment with 60 g/L of SBC achieved removal rates of 65.7% for COD, 66.1% for DOC, 89.6% for NH 4 + –N, and over 96.3% for scale-forming cations (Ba 2+, Ca 2+, and Mg 2+ ), with heavy metals (Cd 2+ and Cu 2+ ) being completely removed (100%). SBC effectively adsorbed cationic pollutants via surface functional groups (−SO 3 H, −OH, and −COOH) and removed diverse organic pollutants (highly unsaturated compounds, polyphenols, and polycyclic aromatics) through pore-filling, electrostatic interactions, and π–π interaction mechanisms. SBC released substantial quantities of sulfur- and oxygen-containing compounds, inducing a pH decrease in OPW from 6.97 to 3.05. Approximately 30% of these compounds are microbially bioactive molecules (H/C ≥ 1.5). Moreover, SBC proved effective for OPW treatment, maintaining a stable adsorption performance through six consecutive cycles. These findings suggest that SBC holds great potential for large-scale applications as an adsorbent in the pretreatment stages of OPW treatment processes.
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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) | 0.000 | 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".