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Record W2739281296 · doi:10.2118/0717-0085-jpt

Approaches for CO2 Capture and Sequestration Inspired by Biological Systems

2017· article· en· W2739281296 on OpenAlexaboutno aff
Chris Carpenter

Bibliographic record

VenueJournal of Petroleum Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCarbonationCarbon sequestrationCarbonateAtmosphere (unit)Carbon dioxideEnvironmental scienceBiomineralizationProcess (computing)ChemistryEarth scienceBiochemical engineeringGeologyComputer sciencePaleontologyEngineeringMeteorologyGeographyOrganic chemistry

Abstract

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 180718, “Approaches for CO2 Capture and Sequestration Inspired by Biological Systems,” by Z. Ouled Ameur and S. Gupta, Cenovus Energy, and Hector De La Hoz Siegler, University of Calgary, prepared for the 2016 Canada Heavy Oil Technical Conference, Calgary, 7–9 June. The paper has not been peer reviewed. 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 favorable, it does not occur significantly because of kinetic limitations and the formation of products that hinder the evolution of the process. In the complete paper, the authors propose biomimicking approaches to precipitate solid carbonates while limiting the amount of energy required or using the byproducts to generate valuable materials. Introduction Permanent sequestration of CO2 as solid carbonates is a feasible solution to the increased levels of CO2 in the atmosphere. Mineral carbonation—the process of capturing CO2 in the atmosphere in the form of solid carbonates through the reaction of CO2 with silicates—is a spontaneous, thermodynamically favorable process. Unfortunately, the kinetics of natural mineral carbonation is very slow and the process is only significant over geological time periods (millions of years). Accelerated formation of solid carbonates is, nonetheless, widely observed in biological systems, particularly in corals, bivalve molluscs, echinoderms, and foraminifera. These organisms have developed mechanisms to induce and accelerate the precipitation of carbonates, required for their skeletons, in natural saline waters. Biomimicking is the imitation of biological processes in other contexts for achieving a result not originally present in the mimicked biological systems. While corals need to precipitate carbonate to build their exoskeletons, they do not significantly modify the concentration of CO2 in the atmosphere. The authors propose use of the mechanisms for carbonate precipitation relied upon by corals and other organisms to develop a large-scale process for accelerating the sequestration of CO2 in the form of stable mineral rock. Process Alternatives The conversion of CO2 into a mineral form involves the transformation of gaseous CO2 into ionic form and the further reaction of the carbonate ions (CO32−) with divalent cations [e.g., calcium (Ca2+) and magnesium (Mg2+)] to form an insoluble precipitate (e.g., CaCO3). Because the reaction system must maintain electroneutrality, an exchange of ions is usually required. In the case of corals, the carbonate-precipitation reaction occurs in the presence of sea water, which naturally contains Ca2+ and chlorine (Cl−) ions. The Ca2+ ions react with CO32− ions, which are present because of the reaction of CO2 with water. The process results in the reduction of one mole of Ca2+ ions per mole of CO2 removed, and the production of 2 moles of hydrogen ions (H+), thus maintaining electroneutrality. To preserve a favorable pH, however, corals couple the production of H+ with photosynthesis, which provides a net sink of H+ ions.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.265
Teacher spread0.226 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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