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Record W2406993699 · doi:10.2118/180718-ms

Approaches for CO2 Capture and Sequestration Inspired by Biological Systems

2016· article· en· W2406993699 on OpenAlexafffund
Zied Ouled Ameur, Suraj Gupta, Hector De la Hoz Siegler

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of CalgaryCenovus Energy (Canada)
FundersCenovus Energy
KeywordsPrecipitationCarbon dioxideEnvironmental scienceCarbon sequestrationEnvironmental chemistryMineralization (soil science)ChemistryChemical engineeringSeawaterNitrogenGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this study, several process alternatives for the permanent sequestration of carbon dioxide (CO2) as solid carbonates are evaluated. Although the formation of mineral carbonates is thermodynamically favourable, it does not occur significantly due to kinetic limitations and the formation of products that hinder the evolution of the process. Several biological organisms, including corals, have developed different mechanisms for accelerating the process and managing undesirable products. In this report, we propose biomimicking approaches to precipitate solid carbonates while limiting the amount of energy required or using the produced by-products to generate valuable materials. A few processes alternatives are described and evaluated in this study. In all these explored cases CO2 mineralization requires divalent cations such as Ca++ or Mg++. These could be sourced from sea water or land based silicates containing these cations. Sea-water based source results in two attractive options, namely: 1. electrochemically assisted precipitation of carbonates with production of sales of Cl2 or HCl; 2. electrochemically assisted precipitation of carbonates and production of Vinyl Chloride Monomer (VCM) and polymerization into Polyvinyl Chloride (PVC), with sales of PVC; the land based source results in further in options 3, and 4: 3. electrochemically assisted precipitation of carbonates with in-situ mining of silicates; and 4. ammonia assisted precipitation of carbonates with in-situ mining of silicates. This study explores the technical feasibility of these options further.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.876

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 head0.052
GPT teacher head0.239
Teacher spread0.187 · 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 designNot applicable
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
Published2016
Admission routes2
Has abstractyes

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