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Record W2952918596 · doi:10.1021/acs.est.8b06931

Fate of Mercury in Volatiles and Char during in Situ Gasification Chemical-Looping Combustion of Coal

2019· article· en· W2952918596 on OpenAlexaboutno aff
Jinchen Ma, Daofeng Mei, Xin Tian, Shibo Zhang, Jianping Yang, Chaoquan Wang, Guoping Chen, Yongchun Zhao, Chuguang Zheng, Haibo Zhao

Bibliographic record

VenueEnvironmental Science & Technology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsnot available
FundersNatural Science Foundation of Hubei ProvinceMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMercury (programming language)CharCoalChemistryCombustionPyrolysisCoal combustion productsEnvironmental chemistryWaste managementOrganic chemistry

Abstract

fetched live from OpenAlex

Mercury emission is an important issue during in-situ gasification chemical-looping combustion ( i G-CLC) of coal. This work focused on experimentally “isolating” two elementary subprocesses (coal pyrolysis and char gasification) during i G-CLC of coal, identifying mercury distribution within the two subprocesses, and examining the effects of a hematite oxygen carrier (OC) on the mercury fate. The mercury measurement accuracy was carefully ensured by comparing online measurements (by a VM 3000 instrument) and benchmark measurements (by the standard Ontario Hydro Method, ASTM D6784) as well as repeated tests (10 times for each case). The mercury mass balance was 115% for the entire i G-CLC. A total of 44.7% of the mercury was released as the gas phase form within the coal pyrolysis process at a typical CLC operation temperature (950 °C), whereas 13.4% was released during the char gasification process. The release rate and amount of mercury were minimally affected by the presence of OC; however, the OC promoted the conversion of Hg 0 (g) to Hg 2+ (g). Only a small amount of mercury was absorbed by the OC and transported into the air reactor along with carbon residue, released as Hg 0 (g) and Hg 2+ (g) or remained in the OC and coal ash as particulate mercury.

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 head0.176
Teacher spread0.171 · 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

Citations51
Published2019
Admission routes1
Has abstractyes

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