Design and Assessment of a Team-Based Mini-Lab Integrated into Chemical Process Engineering Curriculum
Bibliographic record
Abstract
Abstract With a growing emphasis on sustainable development, chemical process engineering education must equip students with skills in industrial system optimization. Drying, a common but energy-intensive operation, highlights the importance of efficiency in process design. Foundational courses such as mass and heat transfer introduce these concepts, while the “mini-lab” provides senior undergraduates with a practical, flexible alternative to traditional experiments. By addressing the time and stress constraints of final-year students, the mini-lab enhances engagement and learning effectiveness in process optimization. The goal is to enhance their interest and learning enthusiasm through hands-on activities and competition-based learning, enabling them to more efficiently learn about the drying process and fit within their demanding schedules while still achieving key learning outcomes. After students had mastered the fundamental principles of drying operations and understood the influencing factors of drying efficiency, such as temperature, air velocity, and contact area, they then used Aspen Plus as a design tool. A completed industrial-scale drying process model will be provided to the students, and based on this scaled drying equipment model, they will design a high-efficiency and high-productivity process by modifying different operating conditions. More importantly, through evaluations and student feedback, the teaching model introduced in this paper can be widely applied to various engineering courses, especially those with significant theoretical content. The combination of theory and practice not only increases student engagement but also brings students closer to real-world industrial applications, positively impacting their future development.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".