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Record W6921769858 · doi:10.7939/r3-pd9r-ft23

Investigation of hydrocarbons in the naphthenic and paraffinic froth treatment tailings

2020· dissertation· en· W6921769858 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2020
Typedissertation
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsOil sandsDiluentAsphaltNaphthenic acidFroth flotationRefining (metallurgy)

Abstract

fetched live from OpenAlex

The oil sands reserves in Northern Alberta is the primary energy source for many consumers. The production of saleable oil sands generates large amounts of tailings waters, which are stored in dykes or ponds due to the zero-discharge policy. As global demand increases, oil production increases, leading to the storage of large volumes of tailings. This results in an increase in the overall emissions and impacting a facility's environmental footprint. The surface mining production process relies on the froth treatment process, which is responsible for producing two main streams: diluted bitumen froth and the waste tailings stream, known as froth treatment tailings (FTT). The addition of a paraffinic diluent or naphthenic diluent reduces the viscosity of the crude oil, the tailings streams from these processes are known as the paraffinic froth treatment tailings (PFT) and naphthenic froth treatment tailings (NFT). Paraffinic froth treatment process uses a mixture of alkanes while naphthenic forth treatment use a mixture of aliphatic, aromatic and naphthenic compounds. The focus of this thesis was on the froth treatment tailings (FTT) as this stream is known to have higher concentrations of hydrocarbons with trace amounts of solvents, despite the froth treatment tailings comprising 2% - 4% of the total tailings discharged by volume of the overall tailings. As well, this diluent is often found in the tailings stream. While current industrial practices rely on naphthenic froth treatment tailings, individual operators are choosing to rely on the alternative, which utilizes paraffinic diluents to reduce the viscosity. Based on the previous studies performed, there was a need for characterization studies of the hydrocarbons within this stream. Specifically, an understanding that the hydrocarbons exist in both the aqueous phase and the solids phases. Both tailings samples were centrifuged for separation into the aqueous and organic phases. Therefore, the separate studies on the filtered aqueous phase and the centrifuged solids were conducted. Chloroform was used to extract the hydrocarbons from the aqueous phase. The pH (3.0, 2.0, 1.0) and temperature (20 °C, 40 °C and 60 °C) was varied for the liquid-liquid extractions. Attenuated Total Reflectance - Fourier Transform Infrared Spectroscopy (ATR-FTIR) was used to identify the functional groups of the hydrocarbons extracted. An increase in temperature led to slight increases in the O—H peaks for NFT and the PFT between 20 °C to 40 °C however, at 60 °C, the increase in temperature drove evaporation rather than the transfer of the acids from one phase to another. Toluene and heptane extracted the hydrocarbons for the solids phase of the tailings. The extractions were performed at 20°C, 40°C and 60°C and for 0.5 hours, 1 hour, 2 hours, 4 hours and 6 hours. Ultraviolet-Visible Spectroscopy confirmed presence of metal porphyrins as all samples corresponding to the Soret bands and certain extraction conditions showed indications of the etioporphyrin or the octethylprophyrin groups. NMR studies showed the presence of aromatic and aliphatic groups in the extracts, as well as clusters of peaks in the aliphatic and the aromatic regimes. This analysis is better understood by obtaining NMR spectra using a high-resolution NMR. Overall, the estimates of the hydrocarbons from the centrifugation and the TGA studies for the NFT and PFT are 83.3% and 73.1%.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.013
GPT teacher head0.179
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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