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Record W1968095097 · doi:10.1002/cjce.21986

Modelling of a solid dissolution in liquid with chemical reaction: Application to the attack reaction of phosphate by sulphuric acid

2014· article· en· W1968095097 on OpenAlexvenueno aff
Nader Frikha, Amira Hmercha, Slimane Gabsi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
Fundersnot available
KeywordsPhosphoric acidDissolutionChemistryPhosphateChemical reactionMass transferDehydration reactionReaction rateChemical kineticsInorganic chemistryKinetic energyKineticsChemical engineeringOrganic chemistryChromatographyCatalysis

Abstract

fetched live from OpenAlex

This paper aims at the determination of the kinetics and thermodynamics of a liquid–solid reaction: the attack reaction of the phosphate ore by sulphuric acid. This reaction, which is used industrially for the production of phosphoric acid, has two stages, the first of which consists in the dissolution of phosphate ore in the diluted phosphoric acid diluted and the second relates to the attack of the dissolved ore by sulphuric acid. The thermodynamic properties and the kinetic parameters of each reaction are determined using a calorimetric approach. The complete kinetic study of the step in dissolving the phosphate ore was conducted to develop a kinetic model involving the process of transfer kinetic liquid–solid and the chemical kinetic processes. This model takes into account the coupling between the mass and heat balance and the multitude particles' sizes of phosphate and the stirring speed. The study of the attack of monocalcium phosphate by sulphuric acid allows the evaluation of the reaction order and the values of various parameters that describe the kinetic model of the precipitation reaction. The comparison of different parameters determined with those reported in the literature shows the reliability and the relevance of the proposed methodology.

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 score0.099
Threshold uncertainty score0.316

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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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