Application of Artificial Neural Networks and Factorial Design Analysis for Predicting the Interaction of Influencing Process Parameters in CO<sub>2</sub> Mineralization of Magnesium-Rich Mining Materials
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Ex situ aqueous-based CO 2 mineralization of magnesium-bearing mine wastes presents a promising pathway for carbon sequestration and resource recovery, relying on both direct and indirect carbonation approaches. However, their efficiency depends on optimizing key process parameters, such as solid/liquid particle size, pretreatment, temperature, pressure, pH, and their interactions. This study provides a comprehensive analysis of CO 2 mineralization in magnesium-based mine wastes, utilizing an extensive data library from existing literature. Both direct and indirect mineralization approaches, including extraction and carbonation, were assessed to understand key process parameter interactions. Utilizing artificial neural networks (ANN) and a 3 k full factorial design coupled with Analysis of Variance (ANOVA), the study investigated nonlinear relationships and the statistical significance of influencing factors for these processes. Key findings indicated that for the extraction process, optimization is driven by feedstock material pretreatment, extraction agent, temperature, and particle size. For direct CO 2 carbonation, prioritization of pretreatment, CO 2 concentration, and particle size reduction are most important, with a secondary focus on solution chemistry. However, for the indirect carbonation approach, the dominance of carbonation assisting agents and solution pH highlights the importance of solution chemistry in aqueous carbonation rather than the physical properties of the feedstocks. These insights provide a robust framework for understanding the complex relationships between different process variables that play a pronounced role in the CO 2 mineralization of magnesium-rich mining materials. Understanding influential factors enables better design and optimization of processes for enhanced efficiency and sustainability.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".