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Record W4296710132 · doi:10.1177/0734242x221123145

Self-sustaining smouldering combustion for the treatment of industrial oily waste

2022· article· en· W4296710132 on OpenAlexaff
Andrew W Sims, Gabriel Sabadell, Carl W. Lam, Daniel C. Segal, Paul Bireta, Dave Thomas

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2022
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsChevron (Canada)
FundersChevron
KeywordsWaste managementEnvironmental scienceContext (archaeology)CombustionReuseEngineering

Abstract

fetched live from OpenAlex

Significant onsite handling and offsite management costs are incurred by oilfield operators annually to properly manage hydrocarbon waste streams such as tank bottoms or other oily sludge or oil impacted soil generated during oil and gas production processes. The current study reports for the first-time technical results of a field trial on use of a smouldering combustion technology performed in an active oilfield. Two treatment batches with oily sludges, stabilized through blending with soil, resulted in permanent hydrocarbon removal (98–99.9% reduction) to create treated soil that met standards for reuse as clean backfill onsite. Emissions profile data collected pre- and post-thermal oxidizer indicated effective removal of volatile organic compounds, CO and SO 2 , but had increased NO and CO 2 due to combustion of propane to affect the thermal oxidation. Regulatory, financial, environmental and safety considerations are discussed in context of future full-scale smouldering technology deployment. The technology has the potential to lower overall unit costs for management of hydrocarbon impacted waste and reduce waste sent to landfills, which can benefit more remote sites.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.314
Teacher spread0.261 · 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.

Study designSimulation or modeling
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

Citations12
Published2022
Admission routes1
Has abstractyes

Explore more

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