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Record W4237680251 · doi:10.2118/136887-ms

Carbon Sequestration From Waste via Conversion to Charcoal: Equipment for a Small Scale Operation

2010· article· en· W4237680251 on OpenAlexafffund
Suraj Gupta, A.. Struyk, Dylan Gilbert

Bibliographic record

VenueCanadian Unconventional Resources and International Petroleum Conference · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsCenovus Energy (Canada)
FundersCenovus Energy
KeywordsCarbon sequestrationCharcoalFlexibility (engineering)Raw materialOffset (computer science)Waste managementContext (archaeology)Biomass (ecology)Environmental economicsFossil fuelScale (ratio)Computer scienceProcess engineeringEnvironmental scienceEngineeringEconomicsChemistryCarbon dioxide

Abstract

fetched live from OpenAlex

Abstract Carbon emitted on account of our continued use of fossil fuel can be offset using carbon capture and storage (CCS). The technology for this exists, however the economics of it is context dependent and CCS is shown not to be very cost effective in oilsands. Committing to the needed large scale sequestration projects without properly considering alternatives can prove costly at both economic and social levels. Charcoal sequestration, discussed earlier by Gupta carries with it a few advantages such as being less costly and lacking any post operation liabilities. Above all, it is reversible allowing flexibility of policy and operation and avoiding long term or large scale commitments. The economics of the charcoal approach depends mainly on two factors: the cost of the feed biomass and the cost of processing. The first of these is addressed by using municipal waste as feedstock which can be available free of charge. Expectedly the cost of processing, the second factor, depends on the apparatus and the scale of operation. In this paper, the authors discuss prominent traditional and modern apparatus used for conversion of biomass to charcoal with their benefits and drawbacks and describe a simple and pragmatic apparatus which could be assembled relatively easily, for a small scale operation such as processing industrial camp generated solid organic waste. Offsetting carbon in this manner can obviously be a good way to initiate demo projects for the charcoal sequestration approach as it also helps with waste management. These demo projects in turn will help evaluate various aspects of this novel method of sequestration, and enhance public awareness on the subject which in turn will help the larger society make an informed choice to embark on a right course of action for atmospheric carbon abatement. Additionally, in light of the growing per capita waste worldwide, use of municipal waste as feedstock for charcoal sequestration can be a significant measure of carbon offset at global scale in its own right.

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)0.000
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.217
Teacher spread0.201 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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