Utilization of zeolite industrial wastewater for removal of copper and zinc from copper-brass pipe industrial wastewater
Bibliographic record
Abstract
Utilization of zeolite industrial wastewater as a sorbent and (or) precipitant to remove Cu and Zn from copper-brass pipe industrial wastewater was conducted. These wastewaters were sampled and values for pH, temperature, biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total solids (TS), total dissolved solids (TDS), total suspended solids (TSS), and heavy metals were determined. In addition, the sorption isotherms of Cu and Zn in copper-brass pipe industrial wastewater onto solids of zeolite industrial wastewater at various dilutions of copper-brass pipe industrial wastewater were explored. The relationship between Cu and Zn concentrations and their removal efficiencies under different conditions of wastewater pH, contact times, and ratios between copper-brass pipe industrial wastewater and zeolite industrial wastewater was examined. Zeolite industrial wastewater contained various carbonate compounds that contributed to high pH and TDS values, and low heavy metals contamination whereas copper-brass pipe industrial wastewater had a low pH value and was contaminated with heavy metals, especially Cu and Zn. Application of zeolite industrial wastewater significantly increased the pH of copper-brass pipe industrial wastewater and consequently removed Cu and Zn. The increase in pH of the wastewater mixture significantly enhanced the heavy metals removal. The Langmuir equation described sorption isotherms of Cu and Zn onto solids of zeolite industrial wastewater at neutral pH (6 – 7) while the Freundlich equation fitted well at pH > 12. The maximum Cu (97% – 98%) and Zn (92% – 96%) removal efficiencies occurred at the original pH 12·8 of zeolite industrial wastewater, at the ratio of copper-brass pipe industrial wastewater to zeolite industrial wastewater 3:1 (vol.:vol.) and at 30 min contact time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".