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Record W2105510416 · doi:10.24908/pceea.v0i0.3835

INTEGRATING CHEMICAL PROCESS DESIGN THROUGHOUT THE CURRICULUM

2011· article· en· W2105510416 on OpenAlexaffvenue
Barrie Jackson

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsCapstoneCurriculumEngineering design processProcess (computing)Engineering ethicsComputer scienceEngineeringMathematics educationMechanical engineeringPedagogyPsychology

Abstract

fetched live from OpenAlex

Over the years we have observed that many Chemical Engineering and Engineering Chemistry students when they come into the fourth year one term Capstone Process Design course claim that most of the material is completely new to them. Making allowance for the expectations that students would claim this in any case, we believe that there are many advantages to trying to integrate preceding courses as much as possible with the fourth year design course. What we are proposing is to develop a framework process model using a simulator such as PRO/II, UniSim, or ASPEN that would serve as a thread to link assignments in preceding courses such as heat and mass transfer. This model would then be used as the basis for the Capstone design exercise. Although there are several potential advantages to this such as reducing duplication of effort and having a more cohesive design concept throughout the three years, there are bound to be teething problems. One potential problem is a common understanding of design. There are many concepts as to what Chemical Process Design consists of, which reflects the background and experience of the instructors. What we hope to do is to keep in mind the definitions of Science, Engineering and Technology as stated at the Inaugural CDEN conference by Dr. Tom Brzustowski P.Eng., the president of NSERC. This talk clearly pointed out the difference between Research and Development as well as Design. These are a continuum, Research, particularly speculative research is the least expensive and to a large degree the easiest of the three since there is not often the pressure to achieve a specific outcome. Development on the other hand which is primarily an engineering activity is considerably more expensive and usually has a fixed deliverable. Development is a team effort. There is a direct link between development and design since design cannot take place without the engineering development phase. The design and implementation of an artifact is the most expensive item by far and requires inputs from Economics, Environmental Health and Safety as well as knowledge of the market. We are also aware of increasing demands on our engineering graduates from the two perspectives, the increasing competition on a global basis and the expanding core body of knowledge expectations

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.209
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

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