Process intensification of metal solvent extraction studies using a miniaturized solvent extraction plant
Bibliographic record
Abstract
This study examines the potential of a miniaturized solvent extraction plant (MSXP) to improve the efficiency and reproducibility of laboratory and pilot-scale metallurgical studies that typically require extended testing periods. The MSXP has been designed to replace conventional solvent extraction (SX) apparatus and aims to reduce start-up time, time to reach equilibrium, and overall test time. Two copper solvent extraction (Cu-SX) research campaigns were conducted to test the functionality, ease of operation, modularity, and reproducibility of the MSXP. The first campaign quantified the extraction efficiency and net copper transfer, while the second campaign operated the MSXP in a semicontinuous countercurrent flow configuration with three extraction and two stripping stages. The main variables studied in the first campaign are the number of stages (1-3), volumetric flow rates (O/A, 1-3), and organic phase residence time in the micro-contactor (0.7-2.4 min). The results demonstrate that the MSXP can work with very low stage efficiencies (48%), while achieving high copper recoveries ( > 90%). At the stripping circuit level, we show that the MSXP can operate at superior stage efficiencies ( > 100%), with low copper recoveries (30%–55%). The reproducibility evaluation also demonstrates high statistical confidence ( < 6% CV) in the obtained process values. The MSXP offers great flexibility in net copper transfer and can be used to benchmark and emulate a wide range of Cu-SX systems. Experimental Cu-SX campaigns can be run in about 60 min while investing roughly US$1.4/h on reagents, which is significantly lower compared to typical campaigns that can last from days to weeks to reach reliable research outcomes and require about US$62/h for reagents. The potential of MSXP extends to more elaborate solvent extraction systems, specifically those designed to purify energy-critical elements like lithium, nickel, cobalt, or rare-earth elements. This study demonstrates the promise of process intensification approaches for improving the efficiency of laboratory and pilot-scale SX studies, which can help in reducing time, reagents, and energy consumption while improving the reproducibility of results. • Techno-economic evaluation of a miniaturized solvent extraction plant was performed. • Extraction circuit operated at high extraction yield and low stage-wise efficiency. • Stripping circuit operated at low stripping yield and high stage-wise efficiency. • Highly reproducible results of copper SX tests in MSXP demonstrated. • 50-fold reduction in experimental costs using MSXP vs. comparable commercial setups.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".