MétaCan
Menu
Back to cohort
Record W4281797957 · doi:10.3390/min12060678

Evaluation of Rheology Measurements Techniques for Pressure Loss in Mine Paste Backfill Transportation

2022· article· en· W4281797957 on OpenAlexaff
Haitham M. Ahmed, Mehrdad Kermani, Ferri Hassani, Mohammed A. Hefni, Hussin A. M. Ahmed, Gamal S. A. Hassan, Essam B. Moustafa, Hussein A. Saleem, Agus P. Sasmito

Bibliographic record

VenueMinerals · 2022
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsRheologyRheometerSlumpBingham plasticMaterials scienceGeotechnical engineeringViscosityViscometerGeologyComposite materialCement

Abstract

fetched live from OpenAlex

Understanding the rheology behavior of non-Newtonian mine paste backfill is critical to ensure its flowability in the pipeline distribution system. Several rheology measurements methods for paste backfill have been proposed in the literature to quantify the rheology properties. However, there is no definite conclusion on the best measurement method to correctly predict pressure loss in the paste backfill pipeline system with a high solid concentration. This study addresses the issue by comparing several rheology measurements techniques, i.e., coaxial rheometer with various configurations, vane viscometer, and slump and pipe loop tests, with regard to Bingham yield stress, Bingham plastic viscosity, and pressure loss using statistical analysis. The paste backfill samples are prepared from two different mine tailings: finer and coarser particles. The pressure loss from the pipe loop test along with Buckingham-Reiner transformation using the Darcy-Weisbach equation are used as reference properties. The results suggest that a simple slump test can accurately predict the Bingham yield stress for coarser tailing. At the same time, a coaxial rheometer with MVDIN cup is found to predict the Bingham paste properties accurately for finer tailing.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.057
GPT teacher head0.267
Teacher spread0.210 · 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 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

Citations15
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMineralsSame topicTailings Management and PropertiesFrench-language works237,207