Removal of petroleum hydrocarbons from contaminated groundwater by the combined technique of adsorption onto perlite followed by the O<sub>3</sub>/H<sub>2</sub>O<sub>2</sub>process
Bibliographic record
Abstract
Groundwater contaminated with petroleum hydrocarbons was treated using a combined system of adsorption onto powdered expanded perlite (PEP) followed by the O3/H2O2 process. The pretreatment investigations indicated a high capacity for PEP to remove petroleum hydrocarbons from the contaminated water. An experimental total petroleum hydrocarbon (TPH) adsorption capacity of 275 mg/g PEP was obtained at the natural pH of water. The experimental data fit best with the Freundlich isotherm model and pseudo-second-order adsorption model. The second phase of the experiment evaluated the performance of the O3/H2O2 process in the removal of residual TPH from pretreated water and compared the results with that of raw water. The O3/H202 process attained a maximum TPH removal rate for the pretreated water after 70 min, when 93% of the residual TPH in the effluent of the adsorption system was removed. Overall, the combination of adsorption onto PEP for 100 min and the subsequent treatment with the O3/H2O2 process for 70min eliminated over 99% of the TPH of highly petroleum-contaminated groundwater, with initial values of 162 mg/L. Therefore, we can conclude that the developed treatment system is an appropriate method of remediation for petroleum-contaminated waters.
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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 | 0.000 |
| 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".