Optimizing milling energy for enhancement of solid-state magnesium sulfate (MgSO<sub>4</sub>) thermal extraction for permanent CO<sub>2</sub>storage
Bibliographic record
Abstract
Mineral carbonation of Mg-silicates via the indirect-dry route is among the most appealing technical approaches for permanent CO2 storage. It brings about the possibility of recycling heat released during exothermic carbonation and it offers a higher rate of conversion through two separate stages: producing of reactive compounds, mainly Mg(OH)2, through solid reactions between Mg-silicates and ammonium sulfates and subsequent carbonation of reactive compounds. Milling is essential to enhance the solid-state reaction rate and increase the conversion percentage. The milling energy, being the major energy consumption of the entire carbonation process, needs to be minimized without sacrifice of its activation purpose. This study focuses on enhancing the kinetics of solid-state magnesium sulfate (MgSO4) thermal extraction from the solid–solid reaction of olivine ((Fe,Mg)2SiO4) and ammonium sulfate ((NH4)2SO4), with optimized milling energy input. This process constitutes the first stage of Mg(OH)2 production for indirect CO2 storage purposes. The mechanical activation of reactants via a high-energy magneto ball milling with a controlled energy input is achieved. The variation of structural parameters such as the particle size, specific surface area (SSA), pore volume, and crystallite size and strain are characterized as a function of milling energy input and the correlation between structural factors and activation energy of extraction is investigated. In addition, the variation in the apparent activation energy of solid-state extraction is examined as a function of milling energy. The optimal amount of milling energy input for increasing the reaction kinetics of MgSO4 extraction is estimated to be about 27.6 kJ g−1 which causes around 34% reduction in the activation energy of MgSO4 solid-state extraction.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".