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Record W4403352883 · doi:10.1016/j.scs.2024.105899

A new integrated system for carbon capture and clean hydrogen production for sustainable societal utilization

2024· article· en· W4403352883 on OpenAlexaff
Mert Temiz, İbrahim Dinçer

Bibliographic record

VenueSustainable Cities and Society · 2024
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsProduction (economics)Hydrogen productionEnvironmental scienceSustainable productionCarbon fibersWaste managementNatural resource economicsBusinessEngineeringHydrogenComputer scienceEconomicsChemistry

Abstract

fetched live from OpenAlex

Both hydrogen production and carbon dioxide removal are considered in this study as two of the critical pieces to achieve the ultimate sustainability target. This study proposes and investigates a new variation of potassium hydroxide thermochemical cycle in order to combine hydrogen production and carbon dioxide removal synergistically. An alkali metal redox thermochemical cycle is developed to utilize the potassium hydroxide uniquely through a nonequilibrium reaction. Also, the multigeneration options are explored by employing two-stage steam Rankine cycle, multi-effect distillation desalination, and Li-Br absorption chiller, which is integrated with potassium hydroxide thermochemical cycle for hydrogen production, carbon capture, power generation, water desalination, and cooling purposes. A comparative assessment under different scenarios is carried out. The energy and exergy efficiencies of the hydrogen production thermochemical cycle are found to be 44.2 % and 67.66 % when the hydrogen generation reaction is carried out at 180 °C and the separation reactor temperature is set at 400 °C. Among the multigeneration scenarios considered, a trigeneration option for producing hydrogen, power and freshwater provides the highest energy efficiency as 66.02 %.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.604

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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 designQualitative
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

Citations8
Published2024
Admission routes1
Has abstractyes

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