A Stream In Process Systems Engineering (Pse) In The Undergraduate Chemical Engineering Curriculum
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract 1 A STREAM IN PROCESS SYSTEMS ENGINEERING (PSE) IN THE UNDERGRADUATE CHEMICAL ENGINEERING CURRICULUM T. Marlin*, A. Hrymak, J. MacGregor, V. Mahalec, P. Mhaskar, and C. Swartz Department of Chemical Engineering McMaster University 1280 Main Street West Hamilton, Ontario, Canada L8S 4L7 (marlint@mcmaster.ca) 1. Introduction Process Systems Engineering (PSE) plays a central role in the chemical engineering education and practice. In this paper, we present our experiences with offering an undergraduate stream in Process Systems Engineering to enable students to build expertise in this field. (We will discuss the meaning of a stream later; for now, let’s consider it a “minor” within the chemical engineering four-year curriculum.) We believe that a stream offers tremendous advantages to students, namely (1) enabling students to follow their interests, (2) providing experiences in learning in depth, and (3) empowering students to focus their course options and electives. The stream has advantages for faculty as well; for example, faculty can make research strengths accessible to undergraduates and can convey to their students the excitement of studying and applying new technologies. In this paper, we provide • An approach to provide focussed course options and electives in a stream, which could be modified for other stream topics • A recommendation for the division of PSE topics between required and elective courses • A description of advanced PSE topics and how they can be delivered within the chemical engineering curriculum We begin by explaining our view of the topics included in PSE stream, with a brief comparison with a few prominent alternative definitions of PSE, and we address the need for a clearly defined stream, rather than a selection of courses. Then, we define PSE learning goals, and present the sequence of courses that address these goals. We demonstrate that the courses include considerable integration and numerous industrial experiences. We conclude by relating experiences from the stream and plans for future enhancements. * Author to whom correspondence should be addressed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".