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Coal

2020· book-chapter· en· W3001466685 on OpenAlex
Deepak Pudasainee, Vinoj Kurian, Rajender Gupta

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFuture Energy · 2020
Typebook-chapter
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoalClean coal technologyClean coalEnvironmental scienceWaste managementParticulatesCoal combustion productsGreenhouse gasPollutantBeneficiationFossil fuelEnvironmental engineeringEngineeringChemistryGeology

Abstract

fetched live from OpenAlex

Coal, a nonrenewable fossil fuel, which has been used since ancient times, is one of the major sources of energy at present as well. Coal use is associated with several operational and environmental problems. Most of the high-grade coals have already been extracted, so coal left for future use is more of low grade with high moisture and ash content. Environmental issues related to coal combustion are multifaceted and are threatening the sustainability of coal use, mostly in power generation and gasification. There are quite efficient technologies that do exist for controlling pollutants such as oxides of nitrogen and sulfur, trace elements, and fine particulate matter. However, one of the serious problems facing the future of coal is emission of carbon dioxide—a greenhouse gas leading to climate change. Coal is still going to be a major player in the global energy spectrum over next 30–40 years and, in particular, for the countries such as India and China. However, the sustainable future of coal depends on mitigating these pollutants and GHG emissions. This chapter reviews the advanced characterization of coal so as to link the coal properties to operational problems such as unburned carbon and ash-related issues. The beneficiation of coal, both physical and chemical, has been included to improve the coal quality in order to reduce these emissions. The environmental issues related to emissions of NOx, SOx, trace elements and fine particulate matter, and postcombustion technologies to reduce these emissions have been presented as well. The utilization of such technologies and installation of pollution control devices can meet stringent regulatory emission limits except GHGs emission. Advanced combustion technologies, such as supercritical, ultrasupercritical boilers, integrated gasification combined cycle, and integrated gasification fuel cell, increase electricity generation efficiency and thereby reduce the GHG emissions per unit of electricity. There are a number of carbon capture technologies, including precombustion, postcombustion, oxy-firing, and chemical looping combustion, those aid to reduce GHG emissions. Current status for carbon capture and storage is also presented in this chapter. The utilization of high-efficiency low-emission technologies, including coal beneficiation, advanced combustion technologies, installation of pollution control measures, and the deployment of carbon capture and storage technologies, can leverage the coal use sustainability.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.685
Threshold uncertainty score1.000

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 integrity0.0010.001
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 head0.006
GPT teacher head0.156
Teacher spread0.150 · 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