CONTINUOUS IMPROVEMENTS IN THIRD YEAR CHEMICAL ENGINEERING DESIGN
Bibliographic record
Abstract
An impending retirement has led us to reevaluate the course: should we keep it - and if so who teaches it, or should we look to change it? The course was co-taught with a new faculty member, coupled with internal reviews and discussions. Ultimately, we decided to keep the course, and also to work to broaden its base. Historically, the design problems have been petrochemical in nature. Each team gets a reaction, with a first order rate law, as well as a production rate and fractional conversion. From this, they do the design calculations for a reactor and separator, and also generate the base engineering drawings. This past year, the project types were expanded to include environmental remediation and hydrometallurgical extraction. The environmental remediation problems involved wastewater processing (eg., BOD oxidation) which can be treated as a first order reaction. The hydrometallurgical problem (Li leaching from spodumene) also was set up as first order. Additionally, we provided the option of a case study review. The students chose a classic chemical safety incident, reviewed the details, and created a summary report which included recommendations on how to incorporate learnings from the incident into the curriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".