Technoeconomic Analysis of the Supercritical Fluid Extraction Process for the Extraction of Rare Earth Elements from Ores
Bibliographic record
Abstract
This study investigates the technoeconomic feasibility of utilizing supercritical fluid extraction (SCFE) with supercritical CO 2 and a tributyl phosphate–nitric acid (TBP-HNO 3 ) adduct for recovering rare earth elements (REEs) from the complex zircon-rich mineral concentrate. A detailed technoeconomic analysis (TEA) framework is employed, integrating mass and energy balance calculations, economic modeling, scenario evaluation, and sensitivity analysis. The research aims to establish the economic viability and scalability of SCFE technology as a sustainable alternative to conventional extraction methods. The study focused on an industrial-scale facility in Ontario, Canada, equipped with a 4000 L SCFE reactors. Key findings included first-year operational expenditures approaching 3 million USD and total capital expenditures of 13.7 million to 14.6 million USD. Revenue from the extracted REEs varied, with the highest returns associated with high-value elements such as terbium and dysprosium. Sensitivity analysis highlights that the profitability of the process is most sensitive to REE prices, particularly for Nd 2 O 3, Dy 2 O 3, and Tb 4 O 7, followed by reagent costs and utility expenses. Payback periods ranged from 6.9 years in the optimal scenario to 12.8 years in less favorable configurations. This study demonstrates the potential of SCFE as a viable technology for REE recovery, emphasizing the importance of feedstock optimization, cost-effective reagent usage, and scalability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".