Treatment of Synthetic Oil Sands Tailing Water with Activated Carbon
Bibliographic record
Abstract
The extraction of bitumen from the Canadian tar sands has thus far generated approximately 840 billion liters of oil sands process affected water (OSPW) that is currently being stored in containment ponds. OSPW is known to be acutely toxic to various organisms, due to the presence of naphthenic acids (NAs). The scale and sheer volume of OSPW being stored in tailing ponds have generated serious public concerns. Many emerging technologies have been investigated as treatment options for OSPW reclamation. This work studied the feasibility of using granular activated carbon (GAC) to adsorb several naphthenic acids in a model solution of OSPW in a batch adsorption process. The effect of process pH on both competitive and single solute adsorption was studied at equilibrium (i.e. isotherms) and transient conditions (i.e. kinetics). The adsorption results for the multi-component solution were compared to those for single compound adsorption. The model compounds selected for this work were 1,4- cylochexanedicarboxylic acid, 2-naphthoic acid, and diphenylacetic acid. GAC showed good adsorption capacity for all three model compounds and the adsorption was significantly affected by the solution pH. Maximum adsorption occurred at pH 4, and adsorption decreased with increasing pH. For multi-component adsorption it was found that at pH 8 the overall capacity increased and the time constant decreased as compared to the single compound results. Additionally it was found that 1,4-cyclohexanedicarboxylic acid exhibited cooperative adsorption, adhering to an S-class isotherm profile. The other two model compounds demonstrated conventional isotherm profiles, either L or H-class. It was found that cooperative adsorption could be hindered in multi-component adsorption due to solute competition. This was observed at all pH investigated other than pH 8 and 9. This finding is promising, as the pH range in which cooperative adsorption took place corresponds to the tailing water pH. The point of zero charge was found to be a significant factor; as the pH of the system approached the pHpzc the adsorbent performance decreased. The findings of this work show promising results regarding the use of GAC for naphthenic acids removal from water.
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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) | 0.001 | 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".