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Record W3112031370

Scaling back : new process does away with scaling in SAGD water

2010· article· en· W3112031370 on OpenAlexaboutno aff
L.B. Harrison

Bibliographic record

VenueOSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsManganeseCadmiumArsenicProduced waterChemistryBariumZincGypsumReagentLimeMercury (programming language)Environmental chemistryMetallurgyEnvironmental scienceInorganic chemistryEnvironmental engineeringMaterials science
DOInot available

Abstract

fetched live from OpenAlex

This article presented a method to reduce scaling problems associated with the use of brackish water in steam assisted gravity drainage (SAGD) operations. Vancouver-based BioteQ Environmental Technology Inc. has developed an ion exchange process known as Sulf-IX, which can remove silica and sulphate from both SAGD-produced water and shale gas frac water. It has also developed a method that oilsand miners can use to selectively recover metals from contaminated water using established BioSulphide or ChemSulphide technologies. The recovered metals can then be sold to generate revenue. Although each of the two technologies' plants have very large footprints, they treat very large volumes of water. The two-stage Sulf-IX ion exchange process removes sulphates to very low levels using chemical resins that remove calcium, magnesium and sulphate ions from water. The process first loads calcium and magnesium cations onto a cation resin, and then uses anion resin to remove sulphate. The resins are regenerated using sulphuric acid and lime. The outputs from the process are a solid gypsum byproduct and clean water. BioteQ's process to precipitate metals dissolved in water exceeds the stringent environmental requirements at Xstrata plc's Raglan nickel mine in northern Quebec. Depending on the site, the dissolved metals that can be removed with BioteQ's processes include aluminum, antimony, arsenic, barium, cadmium, copper, cobalt, iron, lead, manganese, mercury, molybdenum, nickel, rhenium, selenium, strontium and zinc. Some of these metals are recovered because they have value, while others are removed because they are toxic. The difference between BioSulphide and ChemSulphide is the source of the sulphide. In the BioSulphide process, the sulphide is generated biologically in an anaerobic bioreactor in which bacteria generate hydrogen sulphide gas. A chemical source of sulphide, generally sodium hydrosulphide (NaHS), is used for the ChemSulphide. The rest of the chemical reaction to recover the metals from the water is the same. BioteQ's water treatment solutions have received numerous awards and the company has built 10 customized commercial metal-removal plants in Canada, the United States, Mexico, Australia and China. 1 ref., 2 figs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.008
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0030.004
Scholarly communication0.0060.013
Open science0.0030.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0150.006

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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