Optimizing Chloride and Calcium Ion Extraction from Municipal Solid Waste Incineration Fly Ash from Zhoushan, China: Effects of Leaching Conditions and Industrial Applications
Bibliographic record
Abstract
Municipal solid waste incineration (MSWI) fly ash, containing substantial amounts of calcium (Ca), chloride (Cl), and other valuable elements, offers promising potential as a raw material for carbon capture, utilization (CCU), and alkali production. Despite numerous approaches being explored to enhance calcium ion leaching from fly ash, the combined effects of salt and leaching conditions on ion extraction have not been thoroughly investigated. This study provides a comprehensive examination of various leaching conditions, including primary leaching—optimal for efficiency—secondary leaching, which achieved the highest leaching rate, and reverse secondary leaching, focusing on their impact on calcium extraction efficiency. Considering optimal leaching efficiency and resource utilization, this study identifies the most favorable industrial conditions as a 15 min leaching time, a stirring speed of 200 rpm, a temperature of 25 °C, and a 1:10 liquid-to-solid ratio (L/S ratio). The application of a 6% NaCl solution in salt-assisted leaching elevated the calcium ion concentration from 4101.5 mg/L to 4662.6 mg/L, indicating a substantial improvement in leaching performance. Additionally, in carbonate-assisted and ultrasound-assisted leaching, the introduction of CO2 further increased calcium extraction amounts, but it did not enhance efficiency, while ultrasonic intervention had minimal impact. This research investigates enhanced efficiencies through multiple optimized and assisted leaching conditions, advancing MSWI fly ash utilization in carbon capture applications while paving new pathways for sustainable industrial practices that could revolutionize waste management and support global environmental objectives.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".