Education 4.0 : défi de la révolution digitale dans l’actualisation des connaissances et compétences des cursus de génie des procédés
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| Publicat a: | MATEC Web of Conferences vol. 407 (2025) |
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| Altres autors: | , , |
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1051/matecconf/202540703001 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Schaer, Eric | |
| 245 | 1 | |a Education 4.0 : défi de la révolution digitale dans l’actualisation des connaissances et compétences des cursus de génie des procédés | |
| 260 | |b EDP Sciences |c 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a Data management technologies, communication, and connection techniques, and the disruptive innovations of Industry 4.0 processes imply a skilled population of operators, technicians, and engineers proficient in the implementation and consequences of these digital technologies.This article examines the curriculum used in initial training to teach the up-to-date skills required by the actors in the industrial field of chemical and process engineering to adapt to industrial needs and societal changes generated by the disruption of digital technologies.A first immediate and unanimous recommendation is to mutualize the currently disjointed languages between the chemical and process engineering community and that of artificial intelligence and digitization experts, in terms of mutual reciprocal understanding.A review of the new skills and knowledge needed to adapt to Industry 4.0 is then presented. A pedagogical framework of the main components of Education 4.0 is retained. It strategically incorporates diverse skills such as mathematics, modelling, artificial intelligence (AI), simulation, internet of things (IoT), information technology, simulation, neural networks, mega data, robotics, cloud computing, machine learning, deep learning, and additive manufacturing for the learning experience, to respond to today’s Industry 4.0 requirements. A practical, applicable, and acceptable version of this framework is formulated according to the relative relevance of each family of components, assessed on a Blum scale based on expert opinion. By way of example, a detailed schematic representation of data literacy skills and competencies can be obtained for the “data management” component.A review of experiences of introducing data science teaching methods into chemical and process engineering curricula is reported. Two proposals for application to examples extended to the AI component in the chemical engineering departments of the Universities of Columbia (USA) and Leuwen (B) are detailed.Chemical engineering and process safety are connected interdisciplinary subjects. As such, a comprehensive syllabus in process safety included in a chemical engineering curriculum should cover a wide range of topics, from basic physical and chemical phenomena and unit operations to complex and increasingly automated systems, designed and operated by humans. Classical risk analysis and assessment methods and techniques are traditionally used in the application of good qualitative, semi-quantitative, and quantitative assessment practices. However, these conventional methods have their limitations. The inclusion of risk dynamics, in conjunction with recent and accurate information, in these assessment methods is therefore now a necessity to make 4.0 operators and various stakeholders aware of the requirements of process safety 4.0. It is proposed that up-to-date pedagogical content should be limited to the contribution of simulation, Bayesian networks, and fuzzy logic to the dynamic completeness of classical risk analysis methods.Finally, the 4.0 digital revolution has also generated a variety of digital teaching aids. Some pedagogical application examples limited to the two teaching aids Digital Twin and Machine learning are discussed. | |
| 653 | |a Pedagogy | ||
| 653 | |a Internet of Things | ||
| 653 | |a Information literacy | ||
| 653 | |a Curricula | ||
| 653 | |a Skills | ||
| 653 | |a Industry 4.0 | ||
| 653 | |a Machine learning | ||
| 653 | |a Risk analysis | ||
| 653 | |a Industrial safety | ||
| 653 | |a Instructional aids | ||
| 653 | |a Colleges & universities | ||
| 653 | |a Risk assessment | ||
| 653 | |a Data management | ||
| 653 | |a Robotics | ||
| 653 | |a Teaching aids & devices | ||
| 653 | |a Simulation | ||
| 653 | |a Chemical engineering | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Neural networks | ||
| 653 | |a Process engineering | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Automation | ||
| 653 | |a Operators (mathematics) | ||
| 653 | |a Data science | ||
| 653 | |a Science education | ||
| 653 | |a Education | ||
| 653 | |a Cloud computing | ||
| 653 | |a Digital twins | ||
| 653 | |a Teaching methods | ||
| 653 | |a Industrial applications | ||
| 653 | |a Deep learning | ||
| 653 | |a Interdisciplinary subjects | ||
| 653 | |a Digitization | ||
| 653 | |a Digital technology | ||
| 700 | 1 | |a Jean-Marc Commenge | |
| 700 | 1 | |a Perrin, Laurent | |
| 700 | 1 | |a Laurent, André | |
| 773 | 0 | |t MATEC Web of Conferences |g vol. 407 (2025) | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3180657650/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3180657650/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |