Mineral carbonation as a design project for green chemical engineering education
Bibliographic record
Abstract
Abstract Accelerated mineral carbonation is a promising CO2 sequestration technology that is strongly linked to concepts of sustainability and Green Chemistry, and its process requirements apply principles of reaction kinetics, transport phenomena, and materials characterization. The present work aimed to develop educational tools for including accelerated mineral carbonation in chemical engineering curricula. To this end, an experimental investigation laboratory procedure and a design project outline have been conceived. As a way to further engage students in this learning experience, the process conditions for the laboratory work are varied between groups of students, and the experimental data obtained are pooled to be used by every group for the subsequent design exercise. This is meant to give students motivation to generate accurate data that they knew would be useful for the entire class and, at the same time, provide students with the opportunity to use data generated by colleagues, much in the same way the design work is done in the industry. In the design project, students use the experimental data obtained by themselves and classmates on the accelerated mineral carbonation of wollastonite, to determine if this is a feasible process for industry to sequester carbon dioxide, in view of mitigating climate change. Also, they use the experimental data, acquired using a range of process conditions, to study the effect of the process variables (CO2 pressure and mixing rate) on the carbonation kinetics and mass transfer rate. The focus of our previously published article was on the experimental investigation, while the focus of this conference paper is on the design project.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".