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Record W2017393167 · doi:10.3166/acsm.32.487-504

Adsorption des métaux lourds contenus dans l'acide phosphorique industriel par une bentonite algérienne activée. modélisation

2007· article· fr· W2017393167 on OpenAlexvenueno aff
A. Mellah, Djafar Benachour

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2007
Typearticle
Languagefr
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsnot available
Fundersnot available
KeywordsBentoniteChemistryNuclear chemistryGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Dans le present travail, nous nous interessons a la caracterisation physico-chimique de l'acide phosphorique pretraite produit a l'unite des engrais phosphates d'Annaba (Est-Algerie) et a l'activation de la bentonite brute, utilisee pour l'elimination des metaux lourds contenus dans l'acide phosphorique pretraite. Le pretraitement de l'acide phosphorique industriel est realise au moyen de charbon actif pour eliminer les matieres organiques dissoutes successibles de gener la recuperation des metaux lourds. L'activation de la bentonite est effectuee par voie chimique, en utilisant l'acide sulfurique H 2 SO 4 . Deux parametres importants ont ete examines, la concentration de l'acide et la temperature. Des tests d'adsorption de zinc (II), de cadmium (II) et de chrome (III), contenus dans H 3 PO 4 5,5 M pretraite (30% P 2 O s ), ont ete realises dans differentes conditions de temperature, de pH et de quantites de bentonite, dans le but de developper un procede de fixation a l'echelle du laboratoire. Une modelisation du procede a ete egalement realisee. L'application du modele, defini dans le domaine experimental 1,25≤pH ≤2,50; 0,50g≤ masse de bentonite ≤2,5 g; 20°C ≤ T ≤ 70°C, a permis de donner un rendement moyen de 88 % pour l'adsorption du zinc (II) et 89 % pour le cadmium (II) et le chrome (III).

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.008
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.271
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations6
Published2007
Admission routes1
Has abstractyes

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