STRATEGIES FOR CHEMICAL PROCESS DESIGN: A SUSTAINABILITY-BASED APPROACH
Bibliographic record
Abstract
Team Strategies for Engineering Design" is a third-year undergraduate course in our chemical engineering curriculum where student teams develop leadership and management skills while applying decision-making methodologies to process engineering design.Typical deliverables for this course include process flow and piping & instrumentation diagrams centred on developing processes under safety and environmental considerations.This work describes the design and implementation of our revamped version of this course, which consists of four (4) engineering pillars: (i) process description and heat & material balance, (ii) process drawings, (iii) sizing and safety, and (iv) circular economy.Sustainability is discussed in all deliverables and tasks, with a special emphasis on minimizing waste and energy consumption, complying with environmental regulations, performing plant risk assessments, and discussing life cycle assessments.Data-based modelling for prediction and optimization is also taught as a complementary tool for traditional process simulation approaches.Moreover, the corresponding chemical processes are linked to a vertically integrated framework of our curriculum, which combines core engineering concepts and process design around biodiesel plants in different courses of our program.Finally, the teams submit a "strategies report" (engineering logbook), where all engineering strategies to achieve the process engineering goals are summarized and discussed.With this revamped version, we expect to guide students to assume responsibility for designing sustainable chemical processes while enhancing students' career readiness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".