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Record W3036338662 · doi:10.2118/200656-ms

Comparison of Two SEM-EDX Methods for the Analysis of Produced Water Scale Filter Papers

2020· article· en· W3036338662 on OpenAlexaff
M. M. Jordan, Jim Buckman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsNalco (Canada)
Fundersnot available
KeywordsScale (ratio)ThresholdingParticulatesSample (material)Computer scienceAutomationFilter (signal processing)Process engineeringBiological systemMaterials sciencePattern recognition (psychology)Artificial intelligenceImage (mathematics)Computer visionChemistryChromatographyEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Low-vacuum scanning electron microscopy (SEM) / energy dispersive x-ray (EDX) analysis can be used to characterize the nature of inorganic scale from produced water (Method 1); routinely used to visually determine the degree, form and composition of scale particulates. Quantitative data on scale coverage can be extracted through image analysis, and morphology can indicate origins of particulates (transported scale, active scale…). Recent trends demand more detailed quantitative analysis, believed to produce more accurate / reproducible results. Such a method is automated SEM-EDX particle analysis (Method 2). This has the advantage of full automation and delivers quantitative data on scale coverage, composition, shape and size. Neither method is perfect, the first relies on experienced SEM users, is a manual method, susceptible to bias, and is often perceived as producing qualitative data, while the second method although producing large quantitative data sets, depends upon the criteria used to classify particles, and can be time consuming. Both methods were used to examine a number of filtered produced water samples. The traditional manual method provides good representative results on scale coverage, details on particulate morphology and composition, and can be undertaken in about thirty minutes per sample; it is also a simple matter to differentiate between particulate and blanket scale deposits. The second method generates superior levels of quantitative data, but results are dependent on image thresholding (for particle selection), erroneous misleading results are all too easily obtained (unless rigorously tested particle classification schemes are used), and the method can take in excess of an hour per sample. In general Method 1 should be adequate to track scale issues from produced water, which can be supplemented where desired by automated particle analysis (APA). Where APA is to be used it is recommended that an industry standard classification criterion be developed, which will increase the degree of confidence that can be applied to results, and allow direct comparison of results between laboratories.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.218
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.062
GPT teacher head0.382
Teacher spread0.320 · 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 teacher head, 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

Citations2
Published2020
Admission routes1
Has abstractyes

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