Industrial wastewater treatment by combining two systems of adsorption column and reverse osmosis
Bibliographic record
Abstract
Due to the extension of cities and industries and population growth, environmental pollution has become noteworthy. Heavy metals are pollutants produced by industrial factories. Therefore, wastewater should be purified and treated, and returned to natural water circulation. This research investigated the removal of heavy metal zinc (Zn) at high concentrations in Esfarayen Steel Industrial Complex and the performance of the integrated system including activated carbon adsorption column (as pretreatment) and reverse osmosis membrane system under different operating conditions. The variable parameters are pH (4.5, 6.5, 7, and 9), pressure (5, 7, 9, and 11 bar), and zinc concentration (30, 50, and 70 mg/l) to obtain zinc removal efficiency, turbidity dissolved solids (TDS), electrocoagulation (EC), and turbidity (TU) at constant temperature and flow rate. The results show that the integrated system efficiency at 9 bar pressure and pH of 7.5 is optimal as to water outlet quality. The removal efficiencies are 98.1%, TDS; EC, 97.4%; Zinc, 100%; and TU, 95.3%, which is desirable. Moreover, system efficiency at a high concentration of zinc is evaluated. According to the results, the integrated system is resistant to probable shocks, high concentration and has desirable efficiency with all parameters almost above 90%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".