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Record W2014593304 · doi:10.1002/cjce.20384

Optimal implementation of CO<sub>2</sub> capture technology in power and hydrogen production for oil sands operations

2010· article· en· W2014593304 on OpenAlexaffvenueabout
Guillermo Ordorica‐Garcia, Ali Elkamel, Peter Douglas, Eric Croiset

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOil sandsCoalEnvironmental scienceGreenhouse gasCarbon capture and storage (timeline)Hydrogen productionNatural gasWaste managementFossil fuelSteam reformingCarbon sequestrationProduction (economics)Enhanced oil recoveryAsphaltElectricity generationHydrogenEngineeringCarbon dioxidePower (physics)Climate changeChemistryEconomics

Abstract

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Abstract The forecasted demands for electricity and hydrogen for oil sands operations in Alberta, Canada in the years 2012 and 2030 were optimised under CO2 emissions constraints, using a mixed integer linear optimisation model. The model features a variety of technologies (with and without CO2 capture), including coal and natural gas power plants, gasification, and oxyfuel plants. Hydrogen production technologies are steam methane reforming and coal gasification. The optimisation is executed at increasingly stringent CO2 emissions levels, yielding unique infrastructures that satisfy the energy demands of the oil sands industry at minimal cost. The economic and environmental impacts of the optimally chosen technologies on the forecasted operations of the oil sands industry in 2012 and 2030 are thus determined. The maximum CO2 emissions reductions attainable by implementing carbon capture in the hydrogen and power plants supplying the oil sands industry in 2012 and 2030 are 25% and 39% with respect to a business‐as‐usual baseline, respectively. This carbon emissions reduction causes energy cost increases ranging from 13% to 20% for synthetic crude and 2% for bitumen. The maximum achievable CO2 emissions intensity reduction is 31% (2012) and 46% (2030) for synthetic crude and <3% for bitumen. The optimal energy production technologies are strongly dependent on the CO2 reduction targets. Based on the optimisation results, natural gas‐based power production, particularly oxyfuel and combined cycle with CO2 capture, have great potential for achieving significant carbon emissions reductions. For H2 production, gasification (with and without capture) is an optimal technology for oil sands operations. Les demandes prévues pour l'électricité et l'hydrogène pour les activités de sables bitumineux en Alberta, au Canada pour 2012 et 2030 ont été optimisées conformément aux contraintes relatives aux émissions de CO2, à l'aide d'un modèle d'optimisation linéaire partiellement en nombres entiers. Le modèle présente un éventail de technologies (avec et sans capture de CO2), y compris les centrales de charbon et de gaz naturel, la gazéification et les centrales d'oxygaz. Les technologies de production d'hydrogène sont le reformage du méthane à la vapeur et la gazéification du charbon. L'optimisation est exécutée à des niveaux d'émissions de CO2 de plus en plus rigoureux, produisant des infrastructures uniques qui comblent les exigences énergétiques de l'industrie des sables bitumineux à un coût minimal. Les effets sur l'économie et l'environnement des technologies choisies de façon optimale sur les activités prévues de l'industrie des sables bitumineux en 2012 et 2030 sont, par conséquent, déterminés. Les réductions des émissions de CO2 maximales réalisables en mettant en œuvre la capture du CO2 dans l'hydrogène et les centrales qui fournissent l'industrie des sables bitumineux en 2012 et 2030 sont de 25% et 39% relativement à une situation de statu quo, respectivement. Cette réduction des émissions de CO2 entraîne des augmentations du coût de l'énergie allant de 13% à 20% pour le brut synthétique et de 2% pour le bitume. La réduction de l'intensité des émissions de CO2 réalisable maximale est de 31% (2012) et de 46% (2030) pour le brut synthétique et de moins de 3% pour le bitume. Les technologies optimales de production d'énergie dépendent fortement des cibles de réduction du CO2. En se fondant sur les résultats d'optimisation, la production d'énergie basée sur le gaz naturel, en particulier l'oxygaz et le cycle combiné avec capture du CO2, présente un grand potentiel pour parvenir à d'importantes réductions d'émissions de CO2. Pour la production de H2, la gazéification (avec ou sans capture) constitue une technologie optimale pour les activités de sables bitumineux. Can. J. Chem. Eng. © 2010 Canadian Society for Chemical Engineering

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.234
Teacher spread0.229 · 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 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

Citations9
Published2010
Admission routes3
Has abstractyes

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