Reduction–Sulfurization Smelting Process of Waste Hydrogenation Catalysts, Automotive Exhaust Purifier Waste Catalysts, and Laterite Nickel Ore
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Reduction–sulfurization smelting is an effective method for treating solid hazardous waste and recovering valuable components from them. In this work, a waste hydrogenation catalyst (WHC), an automotive exhaust purifier waste catalyst (AEPWC), a vulcanizer, and laterite nickel ore were mixed, and the reduction smelting behavior of this solid waste was investigated. XRD (X-ray diffractometry), TG-DSC (thermogravimetric/differential scanning calorimetry), SEM-EDS (scanning electron microscopy-energy dispersive spectroscopy), OM (optical microscopy), and ICP-OES (inductively coupled plasma-optical emission spectrometry) methods were used to examine the chemical composition, thermal stability, structure, and morphology, as well as the metal content of the samples. Under the Al 2 O 3 -FeO-SiO 2 ternary slag system, at a smelting temperature of 1450 °C, smelting time of 2 h, mass ratio of coke, pyrite, and CaO to waste catalysts of 16, 25, and 0%, respectively, nickel (Ni) and molybdenum (Mo) recovery reached 91.1 and 92.9%, respectively, where average PGMs (platinum group metals, platinum (Pt), palladium (Pd), rhodium (Rh)) recovery reached 96%, although vanadium (V) recovery was only 25.1%. The characterization of the slag shows that Al, Si, and Fe are mainly bound in the form of chemical compounds, while V is intercalated with ferro- or aluminosilicate, which hinders the reduction and sulfurization of V. A series of tests using reduction smelting without sulfurization were also conducted, after which the Ni, Mo, and V recovery reached 96.8, 96.6, and 89.7%, respectively, while PGMs (Pt, Pd, Rh) recovery ranges from 90.2 to 98.0%. The collaborative disposal of primary ore and multisource solid waste has been achieved through two process paths: reducing smelting and reducing sulfurization smelting, which provide reference for the collaborative smelting of multisource secondary resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".