Optimization of sulfate removal from injection water using multi-walled carbon nanotubes by response surface methodology
Bibliographic record
Abstract
One of the major operators' concerns in the injected water into oil reservoirs is the water source itself because sulfate scale formation occurs during waterflooding due to the incompatibility of injected and produced waters. In this work, multiwalled carbon nanotubes (MWCNTs) were prepared by chemical vapor deposition of condensate-gas at 1000 °C and oxidized with an acid mixture at 115 °C for 2 h and then applied as adsorbent for sulfate-removal. Variables Effects such as temperature, adsorbent dose, time and stirring speed, and their interactions during the adsorption were determined and optimized by response surface methodology (RSM) via central composite factorial design (CCF). The experimental data were examined by variance analysis (ANOVA) and fitted to a second-order polynomial equation. The optimum conditions were initial concentration = 800 mg/L, adsorbent dose = 0.14447 g, pH = 7, and temperature = 74.21 °C, 530 rpm during 240 min, and maximum sulfate-removal of 96% was achieved. Isotherm models were investigated to describe the sulfate-adsorption data and a higher suitable to the Langmuir isotherm was found. Kinetic studies showed that the adsorption followed a pseudo-second-order reaction. The thermodynamic parameters indicated that adsorption was spontaneous and endothermic. Overall, MWCNTs are promising adsorbents for water treatment and have great potential application in oilfields to reduce scale ion content from the source.
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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) | 0.000 | 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) | 0.000 | 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".