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Record W1528269300 · doi:10.5539/mas.v9n8p221

Analysis of Effectiveness of Interphase Transfer in a Case of Purification of Biogas in Microbubbling Equipment with a Consideration of Chemisorption of Carbon Dioxide

2015· article· en· W1528269300 on OpenAlexvenueno aff
Botagoz Kaldybaeva, Alisher Evadilloevich Khusanov, Darkhan Sabyrkhanovich Sabyrkhanov, M.I. Sataev, Zakhangir Evadilloevich Khusanov

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
FundersD. Mendeleev University of Chemical Technology of RussiaMinistry of Education and Science of the Republic of Kazakhstan
KeywordsBiogasMass transferCarbon dioxideChemisorptionProcess engineeringProcess (computing)InterphaseCarbon dioxide removalEnvironmental scienceMaterials scienceChemical engineeringComputer scienceChemistryWaste managementChromatographyEngineeringCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The presented paper describes energy saving technology of biogas purification by microbubling method Description of a new design of microbubling equipment for gas-liquid solution and mathematical simulation of a process during chemisorption of carbon dioxide are presented. Experimental studies of mass transfer characteristics of microbubling equipment, based on model systems, were conducted with an aim to define a possibility of application of microbubling process for removal of carbon dioxide from biogas in order to obtain highly concetrated metane. As a result of the study optimal process parameters are defined, key factors affecting mass transfer characteristics of membrane microbubling method are also established. Efficiency of membrane microbubling equipment from the point of view of interphase mass transfer is assessed.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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
Published2015
Admission routes1
Has abstractyes

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