INVESTIGATION OF ORGANIC, INORGANIC AND SYNTHETIC ADSORBENTS FOR THE PRETREATMENT OF LANDFILL LEACHATE
Bibliographic record
Abstract
An investigation into the use of organic, inorganic and synthetic adsorbents for the pretreatment of landfill leachate, generated by the City of Ottawa Trail Road Landfill, was carried out. The purpose of this project was to reduce the concentration of contaminants in order to meet the local Sewer Use By-Laws, prior to transporting the leachate from the generating site to the local municipal sewage treatment plant, and thereby reducing the disposal fees. Peat moss, compost, clinoptilolite, basalt and two types of activated carbon (DSR-A and F400) were investigated to determine the adsorption capacity for contaminants from leachate. Kinetic studies were also performed. The results based on batch adsorption isotherms show that peat moss has the highest adsorption capacity for boron (B) and barium (Ba), compared with the other adsorbents. Also peat moss has good removals of Total Kjeldahl Nitrogen (TKN), Total Organic Carbon (TOC), and benzene, toluene, ethylbenzene and xylene (BTEX), but these are lower than the removals obtained with activated carbon. Because of its relatively low cost and higher adsorption of B and Ba, peat moss was selected as the filter media for the column studies. The treated leachate was tested for B, Ba, TKN, carbonaceous biological oxygen demand (CBOD5) and hydrogen sulfide (H2S). The breakthrough curves for B and Ba showed the effectiveness of peat moss in removing these contaminants.
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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 | 0.002 |
| 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 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".