Education 4.0: Integrating Codes, Standards, and Regulations in the Chemical Engineering Curriculum
Bibliographic record
Abstract
Education 4.0 is the framework to facilitate the development of skills and competencies of engineering students required for Industry 4.0 through the integration of Industry 4.0 applied concepts, networked approach, digitalization of higher education institutions (HEI), and online advancement of teaching and learning practices. In the chemical engineering curriculum of several HEIs, considerable progress in implementing this framework has been made by including computer-aided design tools, updating manufacturing technologies, using simulation and analysis of virtual models, and implementing data analytics in engineering courses and programs. Process and plant design courses such as Plant Design demand that undergraduate students leverage knowledge from core courses completed during first three years of their degree program by developing a plant's conceptual design. This course clearly sets a pathway to integrate Education 4.0 to Industry 4.0. All stakeholders of this course (students, teaching team, and clients) can progressively identify challenges and opportunities to optimize this integration. Many suggested improvements might require a vertical integration of new concepts in the chemical engineering curriculum, involving courses of different levels throughout the undergraduate curriculum. Nevertheless, we consider that immediate actions shall be taken by teaching teams and industry partners in courses such as Plant Design for students achieving the required competencies and skills before graduating from universities. For instance, running a successful multi-disciplinary engineering team for plant design in the industry will require undergraduate students to become familiar with codes, standards, and regulations. According to our industry partners, this lack of familiarization significantly affects the learning curve of junior engineers at work and shows a disconnection between what is learned at university and what is required in the workplace. To facilitate the transition of our students into the process design industry in the framework of Education 4.0-Industry 4.0, in this work, we describe and present the results of applying a strategy to tackle this challenge by (i) identifying the currently applicable codes, standards, and regulations in the process engineering industry for each technical deliverable (process flow diagram, piping and instrumentation diagram, line list, plot plan, design of equipment, risk management, and safety documents) of the course; (ii) designing and delivering workshops to describe and illustrate their applicability; and (iii) creating a written set of guidelines applicable to the course and the workplace.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".