Évaluation du recyclage de la soude caustique dans un procédé de purification chimique d'un concentré de graphite naturel
Bibliographic record
Abstract
RÉSUMÉ: « RÉSUMÉ : Le graphite est un minéral faisant partie de la liste des minéraux critiques et stratégiques du Canada en raison de sa demande croissante liée à son utilisation comme matériel d’anode dans les batteries lithium-ion pour les véhicules électriques. Toutefois, pour cette application spécifique liée au stockage de l’énergie, le graphite doit être de très haute pureté et les procédés de purification actuellement utilisés à l’échelle industrielle pour le graphite naturel utilisent principalement une combinaison d’acides pour solubiliser les impuretés présentes. L’acide hydrofluorique (HF) est utilisé en raison de son efficacité pour l’enlèvement des silicates mais présente des risques importants pour la santé, la sécurité et l’environnement. Des procédés de purification alternatifs sont donc étudiés, dont la purification par fusion alcaline, qui utilise la soude caustique (NaOH) pour convertir les silicates en espèces solubles avant une lixiviation acide. La fusion caustique nécessite un excès important de NaOH pour atteindre un enlèvement suffisant des impuretés et le recyclage de ce réactif, peu abordé dans la littérature, est donc d’intérêt. Ce mémoire de maîtrise vise à explorer les bénéfices et les conséquences potentielles du recyclage du NaOH dans un procédé de purification du graphite par fusion alcaline suivie d’une lixiviation acide. Une revue de littérature a permis d’identifier une séquence et des paramètres d’opération pour des essais de purification effectués en laboratoire sur un concentré de graphite. Cette revue de littérature a également permis de recueillir de l’information sur les procédés qui utilisent et recyclent le NaOH afin d’établir un parallèle avec ce qui pourrait être applicable au procédé de purification du graphite. Ainsi, il a été possible de procéder à des essais de purification du graphite et de reconditionnement d’une solution caustique. La simulation par bilans de masses a permis d’évaluer l’impact économique potentiel que le recyclage du NaOH pourrait avoir sur le procédé de purification du graphite ainsi que d’estimer le taux d’accumulation potentiel d’impuretés dans le procédé en fonction des paramètres opératoires.» ABSTRACT: « ABSTRACT : Graphite is on Canada’s critical minerals list with the growing demand for this mineral driven by its use as an anode material in lithium-ion batteries for electric vehicles. However, this specific application for energy storage requires high purity graphite and the purification processes currently used for natural graphite at the industrial scale are mainly based on the combination of acids to dissolve impurities. Hydrofluoric acid (HF) is used due to its efficiency in removing silicates but presents risks for health, safety and the environment. Alternative purification processes are thus studied, one of them being alkaline fusion which uses sodium hydroxide (NaOH) to convert silicate minerals into soluble species prior to an acid leaching step. Caustic fusion requires NaOH to be added in large excess to achieve sufficient removal of targeted impurities. The recycling of this reagent for this process is seldom discussed in the literature and is thus of interest. This thesis aims to investigate the benefits and potential consequences of recycling NaOH in a graphite purification process based on alkaline fusion followed by an acid leaching. A literature review led to the identification of operating conditions for laboratory scale purification tests of a graphite concentrate. Information on processes that use and recycle NaOH were also collected during this literature review better defining what could be applicable to the graphite purification process. Tests were done conducted for the purification of graphite and for the regeneration of caustic solutions. Simulation by mass balance was used to evaluate the potential economic impact of the recycling of NaOH on the graphite purification process and to estimate the potential accumulation level of impurities in the process based on the operating conditions. Graphite purities greater than 99.95%, the typical purity required for Li-ion batteries applications, were achieved during several of the purification tests. Impurity removal efficiencies through alkaline fusion – acid leaching (HCl) were 95% to 99.5% for Si, > 98.8% for Al, > 99.8% for Fe and 97% to 99.5% for Ca. Silicon was the main impurity remaining after purification (> 68% SiO2 in ashes after calcination). Analysis of the alkaline solutions and of the intermediate and final graphite confirmed that Si and Al were removed primarily during the alkaline step, while Fe and Ca were removed during the following acid leaching.»
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".