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Interaction of Potassium and Calcium in the Catalytic Gasification of Biosolids and Switchgrass

2017· article· en· W2606680293 on OpenAlexafffund
Ross A. Arnold, Rozita Habibi, Jan Kopyscinski, Josephine M. Hill

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsMcGill UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiosolidsPotassiumCalciumChemistryCatalysisBimetallic stripCharCarbon fibersChemical engineeringPulp and paper industryInorganic chemistryWaste managementMaterials scienceEnvironmental scienceEnvironmental engineeringOrganic chemistryPyrolysis

Abstract

fetched live from OpenAlex

Catalytic gasification is a method of converting biosolids, the solids created during wastewater treatment, into a valuable gaseous stream. One of the challenges with this process is that the components in the ash of the biosolids can interact with the gasification catalyst(s)—in particular, calcium and potassium. In this study, the behaviors of different combinations of switchgrass (the source of potassium), biosolids, ash-free carbon black, and mixtures of each feed with added calcium and/or potassium were observed with a thermogravimetric analysis unit. The results were consistent with calcium preferentially reacting with components in the ash, preventing the deactivation of potassium. Any additional calcium available may form bimetallic compounds with the potassium, but this interaction did not increase the rate of reaction. Modeling was performed using the random pore model and extended random pore model, with the appropriate model chosen for each mixture. The extended random pore model was better suited for the data with the highest reaction rates.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.175

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 head0.016
GPT teacher head0.242
Teacher spread0.227 · 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 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

Citations25
Published2017
Admission routes2
Has abstractyes

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