Potential for CO<sub>2</sub>Fixation by Chlorella pyrenoidosa Grown in Oil Sands Tailings Water
Bibliographic record
Abstract
Discharge of process water into tailings ponds is associated with many mining operations, including that of bitumen. These tailings ponds can be used to grow organisms, such as algae, which, in turn, fix CO 2 and degrade unwanted dissolved components. After processing, algae can be used for the production of fuels (for example, biodiesel or methane). In this work, we explored the potential for growth of a unicellular algae, Chlorella pyrenoidosa, in tailings water from an oil sands mining and upgrading operation. Once we determined that it was possible to grow algae in the tailings water, we designed and optimized minimal growth media for biomass (algae) production and did a preliminary engineering estimate of the potential for CO 2 fixation. The medium components required for growth of C. pyrenoidosa in 95% oil sands tailings water (OSTW) were screened using a two-level full factorial experiment. Sodium nitrate, phosphate, and Fe-ethylenediaminetetraacetic acid (EDTA) were the most important medium components. After this work, response surface methodology (RSM) was used to find the optimum concentrations of these nutrients. The optimum concentrations of sodium nitrate, phosphate, Fe-EDTA, and trace metal solution were 11.9 mM, 9.4 mM, 49.5 μM, and 2 mL/L, respectively. On the basis of an optimized specific growth rate of 0.085 g L −1 day −1, it was estimated that 12 million tons/year of CO 2 could be fixed by C. pyrenoidosa growing in the tailings ponds in the Athabasca region of Canada. This value has to be considered optimistic because of fluctuations in temperature, light, and other growing conditions, which would be experienced in the full-scale system.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".