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Record W1514562855 · doi:10.24908/pceea.v0i0.4054

AN INTEGRATED, REAL-TIME APPROACH TO PROCESS CONTROL SYSTEM DESIGN EDUCATION

2011· article· en· W1514562855 on OpenAlexafffundvenueabout
Brent R. Young, William Y. Svrcek

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsInstrumentation and control engineeringProcess (computing)Process controlControl (management)Advanced process controlEngineeringDomain (mathematical analysis)Computer scienceEngineering managementSystems engineering

Abstract

fetched live from OpenAlex

Throughout the chemical and process industries, ever more emphasis is being placed upon extracting increasingly greater value from plant equipment, with substantial interest in energy efficiency and responsible environmental stewardship. Improved process control is almost unique in its ability to deliver substantial operational efficiency and environmental improvements with relatively little additional capital investment. As such, process control has become one of the most sought after skills within the chemical and process industries. Industry needs graduates that are educated in the latest and most relevant skills. Industry practitioners rely heavily on commercially available process simulation tools and hands on, time domain based control strategy development techniques, e.g. [1-2]. This paper describes an integrated, real-time approach to the education of undergraduate chemical engineering students in process control system design [3-4]. The real-time approach to process control system design education integrates introductory process control education and industrial practice. The approach focuses on the more applied and practical time domain based techniques derived from modern process simulation. The use of computers is a central theme to the approach, and their use in simulations and the software is carefully introduced. The students gain a thorough understanding of instrumentation, process design versus controllability trade offs, control loop configurations and tuning, practical techniques for the control of unit operations and basic plant-wide control. This integrated, real-time approach to the education of undergraduate chemical engineering students in process dynamics and control has been taught as a capstone subject at the University of Calgary since 1997 using active, “hands on” or resource based learning [5-6]. A small number of lectures at the beginning of the course are advocated from a learning perspective to motivate students rather than to simply transmit information. A majority of “hands on” tutorial and / or simulation sessions are recommended on case studies, workshops or projects facilitated by the instructors [7]. The approach is illustrated by examples from this capstone course.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.007
GPT teacher head0.193
Teacher spread0.187 · 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.

Study designSimulation or modeling
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 routes4
Has abstractyes

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