Fine particle flotation using columns (conventional and Jameson downcomer) for a niobium deposit in Canada
Bibliographic record
Abstract
Niobium, originally called columbium, is an important element used in alloying and currently mineral from three deposits, two in Brazil and one in Canada "Niobec".The current production rates of niobium are considered sufficient to meet the global demand.However, this may not always be the case.The minerals containing the highest amount of niobium are pyrochlore and columbite.The main method for their recovery is flotation, utilized by all three processing plants.Currently, a significant amount of niobium is lost as slimes, which are removed prior to flotation due to their negative impact on the separation.This thesis is investigated the potential of recovery the "lost" niobium through flotation.The properties of valuable and gangue minerals were analyzed through microflotation, zeta potential and bench flotation.Column and Jameson cell flotation was carried out to investigate the amount of mineral that could be recovered.The collectors that were used for the analysis were: sodium oleate, florrea 7510, dodecylamine and benzohydroxamate, in the presence and absence of activating ions (cobalt and lead).On the other hand, the gangue mineral is dolomite which was analysed to understand their behaviour.The minerals showed their possible separation under each collector (different schemes).Pyrochlore presented high percentages of recovery in microflotation under the analyzed collectors and activators.These high percentages are convincing according to the results obtained from zeta potential.Columbite showed its best results at pH 7 under florrea 7510 + lead and benzohydroxamate + lead, while with dodecylamine the best recovery is obtained at pH 9 and with sodium oleate + lead at pH 5 the highest recovery is shown.Finally, dolomite indicated that low recovery percentages can be obtained with florrea 7510 + lead and benzohydroxamate + lead.Flotation of the slimes sample did not lend to a concentration of niobium in the recovered froth, which is contrary to the microflotation tests.This indicates that further research is required to iii optimize the process, this includes investigation, whether other minerals are preferentially absorbing the reagents.The inclusion of pre-concentration using other techniques targeting the physical properties could be employed.These would include the multi-gravity separator (density based) and wet high intensity magnetic separation which are effective for fine particle processing.v La flottation de l'échantillon de slimes n'a pas donné une concentration de niobium dans la mousse récupérée, ce qui est contraire aux tests de microflotation.Cela indique que des recherches supplémentaires sont nécessaires pour optimiser le processus, y compris la recherche pour savoir si d'autres minéraux absorbent préférentiellement les réactifs.L'inclusion de préconcentration en utilisant d'autres techniques ciblant les propriétés physiques pourrait être employée.Celles-ci incluraient le séparateur multi-gravité (basé sur la densité) et la séparation magnétique humide de haute intensité qui sont efficaces pour le traitement des particules fines.viACKNOWLEDGEMENTS I would like to thank Prof. Waters for granting me the opportunity to do my master's and this research, but most importantly for continuously challenging me while providing insight and wisdom.Thanks to each person how to help me in this research, as well the min-pro group members (Chris, Eileen, Ozzy, Ronghao, Mark, Meng, Nonku, Luis) for their support through the entire process in my project.I also want to acknowledge
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".