Toxic metal recovery from spent hydroprocessing catalyst
Notice bibliographique
Résumé
Spent hydroprocessing catalysts (HPCs) are solid wastes generated in refinery industries \nand typically contain various hazardous metals, such as Co, Ni, and Mo. These wastes \ncannot be discharged into the environment due to strict regulations and require proper \ntreatment to remove the hazardous substances. Various options have been proposed and \ndeveloped for spent catalysts treatment; however, hydrometallurgical processes are \nconsidered efficient, cost-effective and environmentally-friendly methods of metal \nextraction, and have been widely employed for different metal uptake from aqueous \nleachates of secondary materials. Although there are a large number of studies on hazardous \nmetal extraction from aqueous solutions of various spent catalysts, little information is \navailable on Co, Ni, and Mo removal from spent NiMo hydroprocessing catalysts. \nIn the current study, a solvent extraction process was applied to the spent HPC to \nspecifically remove Co, Ni, and Mo. The spent HPC is dissolved in an acid solution and \nthen the metals are extracted using three different extractants, two of which were aminebased \nand one which was a quaternary ammonium salt. The main aim of this study was to \ndevelop a hydrometallurgical method to remove, and ultimately be able to recover, Co, Ni, \nand Mo from the spent HPCs produced at the petrochemical plant in Come By Chance, \nNewfoundland and Labrador. The specific objectives of the study were: (1) characterization \nof the spent catalyst and the acidic leachate, (2) identifying the most efficient leaching agent \nto dissolve the metals from the spent catalyst; (3) development of a solvent extraction \nprocedure using the amine-based extractants Alamine308, Alamine336 and the quaternary \nammonium salt, Aliquat336 in toluene to remove Co, Ni, and Mo from the spent catalyst; (4) selection of the best reagent for Co, Ni, and Mo extraction based on the required contact \ntime, required extractant concentration, as well as organic:aqueous ratio; and (5) evaluation \nof the extraction conditions and optimization of the metal extraction process using the \nDesign Expert® software. \nFor the present study, a Central Composite Design (CCD) method was applied as the main \nmethod to design the experiments, evaluate the effect of each parameter, provide a \nstatistical model, and optimize the extraction process. Three parameters were considered \nas the most significant factors affecting the process efficiency: (i) extractant concentration, \n(ii) the organic:aqueous ratio, and (iii) contact time. Metal extraction efficiencies were \ncalculated based on ICP analysis of the pre- and post–leachates, and the process \noptimization was conducted with the aid of the Design Expert® software. \nThe obtained results showed that Alamine308 can be considered to be the most effective \nand suitable extractant for spent HPC examined in the study. Alamine308 is capable of \nremoving all three metals to the maximum amounts. Aliquat336 was found to be not as \neffective, especially for Ni extraction; however, it is able to separate all of these metals \nwithin the first 10 min, unlike Alamine336, which required more than 35 min to do so. \nBased on the results of this study, a cost-effective and environmentally-friendly solventextraction \nprocess was achieved to remove Co, Ni, and Mo from the spent HPCs in a short \namount of time and with the low extractant concentration required. This method can be \ntested and implemented for other hazardous metals from other secondary materials as well. \nFurther investigation may be required; however, the results of this study can be a guide for \nfuture research on similar metal extraction processes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».