A NEW APPROACH TO THE UNIT OPERATIONS LABORATORY
Bibliographic record
Abstract
Unit operations laboratories are a standard feature of most chemical engineering programs. Students spend long hours running distillation columns, gas absorbers, and work with pumps, valves and heat exchangers. This provides much of the hands-on learning that they take into industry after graduation. Process control laboratories are often integrated into the unit operations laboratory. The most common control laboratory involves heating a tank with a steady inflow of cold water. Our laboratory has all of these features. Our approach can be described as using 20th century technology to control 19th century type processes in an 18th century learning environment while educating engineers for the 21st century. A different way to say it is that our approach is nothing like what a new graduate engineer sees when they arrive at a chemical facility. Several years ago, our department created a team tasked with upgrading the approach to the unit operations laboratory, and several guiding principles were created. It is important to retain a "hands-on" operational component – students need to open and close valves, read gauges, as well as start and stop pumps. It is equally important to introduce students to a proper distributed control system. It is also important that the DCS is not seen as a "black box" that does everything – the link between the equipment, the P&ID and the DCS needs to be reinforced.The equipment is now in regular operation, and we continue to expand its capabilities. This submission describes the genesis of the system and the staged approach that has been taken to manage the time and budget pressures.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.037 | 0.020 |
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".