Bibliographic record
Abstract
Chemical engineering unit operation labsdo an excellent job of integrating the whole curriculumand exposing students to pilot-scale systems. Where theyare often lacking, though, is the exposure to and use ofreal-life industrial automation by the future graduates. Aunit operation lab that has been automated usingindustrial level paradigms and equipment is the focus ofthis paper. A partnership with a global automationmanufacturer (Emerson) was established and the lab wasretrofitted using industrial sensors and actuators, aDistributed Control System (DeltaV DCS), industrialnetworks (FOUNDATION Fieldbus and AS-i), HumanMachine Interface (HMI) screens, and systemredundancy. The details of the automation along with itsuse through the lab curriculum will be discussed. Thiscross-curricular approach benefits students as, throughthe regular unit operation labs, they become familiar withkey elements of an automated set-up, understand the needfor it and its limitations, see control loops in action,communicate to the units through the HMI, and use theHMI to recover historical data on the processes. The labis a meso-scale of a processing facility and preparesstudents for field work after graduation. At the sametime, the traditional exposure to “manually operated”sensors and final elements is maintained as some of theunits have not been converted to fully automated systems
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.013 |
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".