Mineral Carbonation as an Educational Investigation of Green Chemical Engineering Design
Bibliographic record
Abstract
Engaging students in the experimental design of “green” technology is a challenge in Chemical Engineering undergraduate programs. This concept paper demonstrates an educational methodology to investigate accelerated mineral carbonation, which is a promising technology related to mitigation of climate change by sequestering carbon dioxide (CO2) from industrial sources as stable solid carbonates. An experimental investigation is conceived, whereby students test the effect of two process parameters (CO2 pressure and mixing rate) on the extent of carbonation reaction. The carbonation reaction has been performed using a mineral called wollastonite (CaSiO3). The experimental study and laboratory report cover principles of reaction kinetics and mass transfer, while illustrating the steps to develop and investigate a green process technology. The results from the experimental investigation, which is carried out by multiple teams of students, are then pooled and used to guide a subsequent design project. Students would conceive a flowsheet, size equipment, and estimate the energy demand and net CO2 sequestration efficiency of a full-scale implementation of the mineral carbonation technology. This educational investigation aims to help undergraduate students to acquire deeper experiential learning and greater awareness of future green technologies by applying fundamental engineering principles into an engaging experimental and design exercise.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".