Toxic metal recovery from spent hydroprocessing catalyst
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".