Use of Calcium Sulfate to Accelerate Densification while Reducing Greenhouse Gas Emissions from Oil Sands Tailings Ponds
Bibliographic record
Abstract
Abstract Tailings ponds contain large volumes of fine tailings that often settle slowly on a timescale of years. Accelerated densification can be achieved by addition of agents such as calcium sulfate (gypsum), although some microbial activity such as methanogenesis may also accelerate densification. The latter process, however, contributes to undesired methane emissions from tailings ponds. It is known that the presence of sulfate can curtail the activity of methane-forming bacteria by diverting microbial activity from the production of methane to the reduction of sulfate to sulfide. We sought to characterize the microbial processes in a tailings pond managed by Suncor in order to estimate their contribution to densification and reduction of greenhouse gas emissions. The sulfate and sulfide concentrations, sulfide oxidation potential, and rates of microbial sulfate reduction and methanogenesis were measured as a function of depth in a Suncor tailings pond. This pond is routinely treated with gypsum (CaSO4-2H2O) to consolidate fine tailings. The highest concentration of sulfate was found at the pond surface (over 6 mM or ∼ 575 ppm) where no sulfide was detected. At various depths, the sulfate and dissolved sulfide concentrations measured correlated well with each other and with the rates of sulfate reduction. In general, the highest rates of methanogenesis (up to 80 mmol CH4/m3 tailings/d) were measured where the sulfate reduction rates were lowest and vice versa, which supports the notion that methanogenesis will occur when sulfate-reducing activity is low. The fluctuating rates of sulfate reduction and methanogenesis we measured as a function of depth also shows that different microbial activities occur at discreet levels within the pond, probably due to the availability of sulfate. Based on the average rate of sulfate reduction measured in the pond samples, 10 mmol sulfate reduced/m3 tailings/d, the consumption of pond hydrocarbons by sulfate-reducing bacteria could potentially inhibit the formation of over 2 million L of methane per day from a typical pond harboring ∼107 m3 of tailings. Although the stimulation of sulfate reduction would produce undesirable H2S, our data suggest that sulfide is converted to sulfate at the pond surface due to either chemical or microbial oxidation. The data collected so far support a model in which sulfide, formed by reduction of sulfate at depth, is carried upwards in gas bubbles (possibly methane) and is then oxidized back to sulfate in the upper oxygenated layers of the pond. Our observations show that the use of calcium sulfate as a consolidation agent has the additional advantage of reducing greenhouse gas emissions from tailings ponds. Introduction Tailings ponds minimally contain water, sand, clays, and residual bitumen and hydrocarbon diluent. A typical pond has a large volume (∼107 m3) of a water-clay-bitumen suspension as its core surrounded on all sides by sand, which has segregated during oil extraction. The suspension, referred to as fine tails, settles slowly on a time scale of years, compromising water reuse and eventual pond reclamation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".