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Record W4297229474 · doi:10.1002/cjce.24676

Migration and emissions of selenium during chemical looping combustion of coal

2022· article· en· W4297229474 on OpenAlexvenueno aff
Huifen Kang, Jingjing Ma, Xintong Guo, Ziheng Han, Jian Hao, Qingjie Guo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSeleniumCoalChemistryChemical looping combustionFlue gasEnvironmental chemistryCoal combustion productsCombustionAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Selenium is one of the most volatile toxic elements in coal, and its emissions must be strictly controlled. Chemical looping combustion (CLC) is a clean and efficient technology for coal. Herein, the iron‐based oxygen carrier (OC) was used as an adsorbent to study the migration and emissions of selenium during the CLC of coal. Due to the oxidation and adsorption of selenium by iron‐based OC, most of the selenium was retained in OC or distributed in the CO 2 stream. The proportion of gaseous selenium released into the atmosphere was less than 10%—significantly lower than that from the traditional combustion process of coal, which had a value of 91.79%. The presence of OC increased the distribution phase of selenium, promoted the conversion of gaseous selenium to solid selenium, and reduced selenium emissions in flue gas. During CLC of coal, the fuel reactor (FR) temperature and the number of OC re‐oxidation cycles played an important role in the emissions and retention of selenium. The increasing FR temperature increased the gaseous selenium in the CO 2 stream, reduced the particulate selenium absorbed by OC, and reduced the selenium emissions in the atmosphere. After 10 continuous CLC cycles, the selenium concentration in OC increased from 0.889 to 8.20 mg kg −1 . The continuous cycling of CLC could realize the enrichment of selenium from coal to OC. Furthermore, the migration and transformation mechanism of selenium during CLC was deduced by experiments and thermodynamic simulation. This research provides a suitable reference for reducing selenium emissions and developing CLC technology.

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 score0.037
Threshold uncertainty score0.437

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 head0.005
GPT teacher head0.171
Teacher spread0.165 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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