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Record W2889823810 · doi:10.3390/min8090400

Impact of Clay Minerals on the Dewatering of Coal Slurry: An Experimental and Molecular-Simulation Study

2018· article· en· W2889823810 on OpenAlexfundno aff
Xiaomin Ma, Yuping Fan, Xianshu Dong, Ruxia Chen, Hongliang Li, Dong Sun, Suling Yao

Bibliographic record

VenueMinerals · 2018
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersCanadian Centre for Clean Coal/Carbon and Mineral Processing TechnologiesNational Youth Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Shanxi ProvinceUniversity of Alberta
KeywordsKaoliniteMontmorilloniteDewateringSlurryFiltration (mathematics)Coal slurryFilter cakeClay mineralsPorosityMaterials scienceAdsorptionCoalMoistureMineralogyChemical engineeringChemistryGeologyComposite materialGeotechnical engineeringChromatographyOrganic chemistryMathematics

Abstract

fetched live from OpenAlex

The cleaning process of coals is challenging due to the existence of clay minerals. The overall objective of this study is to investigate how the dewatering of coal slurry is impacted by the presence of clay minerals, i.e., kaolinite and montmorillonite. Filtration tests were first conducted to investigate the effect of kaolinite and montmorillonite on the dewatering efficiency of coal. Specifically, we measured the filtration velocity, moisture, average specific resistance, and porosity of filter cakes for six slurry samples, in which different amounts of kaolinite and montmorillonite were contained. Filtration tests show that a small amount of kaolinite and montmorillonite leads to a significant reduction in the filtration velocity and porosity, and a big increase in the average specific resistance and the moisture of the filter cake. We observe that most kaolinite existed in the top and middle layers of the filter cake, while most montmorillonite existed in the top layer; on the contrary, little montmorillonite is observed in the middle and bottom layers of the filter cake. Montmorillonite results in a much more deteriorative effect than kaolinite. Considering that the interactions between clay minerals and water may play a key role, we then further investigate the effect of such interactions using molecular simulations. Simulation results show that water molecules could hardly diffuse into kaolinite from the edge, while they could readily penetrate into the montmorillonite layers from the edge surface. This result can be explained by the hydrated cation in montmorillonite. The adsorption density of water on the octahedral surface of kaolinite is higher than that of water on the tetrahedral surface of kaolinite. Furthermore, the adsorption density of water on the double surfaces of kaolinite is higher than that of water on the montmorillonite surface. This research is expected to provide benefits or contributions to the dewatering of clay-rich coal tailings.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.289
Teacher spread0.270 · 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 designSimulation or modeling
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

Citations49
Published2018
Admission routes1
Has abstractyes

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