Open Access
Issue
MATEC Web Conf.
Volume 407, 2025
19e Congrès de la Société Française de Génie des Procédés (SFGP2024)
Article Number 03001
Number of page(s) 18
Section Formation & enseignement / Training & Education
DOI https://doi.org/10.1051/matecconf/202540703001
Published online 04 March 2025
  1. Beck, D.A.C., James, W.A., Carithers, M., Subramanian, V.R. and Pfaendtner, J., Data Science: Accelerating innovation and discovery in chemical engineering, AIChE Journal, 2016, 62 (5), 1402–1416. [CrossRef] [Google Scholar]
  2. Boettcher, K., Terkowski, C., Schade, M., Brandner, D., Grünendahl, S., and Pasaliu, B., Developing a real- world scenario for foster learning and working 4.0 on using a digital twin of a jet pump experiment in process engineering laboratory education, European Journal of Engineering Education, 2023, 48 (5), 949–971. [CrossRef] [Google Scholar]
  3. Bolton, L.W., Glassey, J. and Ventura-Medina, E., Updating chemical engineering degree accreditation in changing time, Education for Chemical Engineers, 2023, 43, 31–36. [CrossRef] [Google Scholar]
  4. Buyel, J., (2024) EFCE spotlight talks “Digitalization in chemical engineering education” Working Party Education, https://www.youtube.com/wateh?v=2mhHVuSEpSo [Google Scholar]
  5. Carretero, S., Vuorikari, R. and Punie, Y. (2017). DigComp 2.1: The Digital Competence Framework for Citizens with eight proficiency levels and examples of use, EUR 2558 EN. https://doi.org/10.2760/38842 [Google Scholar]
  6. Chakraborty, S., Gonzales-Triana, Y., Mendoza, J. and Galatro, D., Insights on mapping Industry 4.0 and Education 4.0, Frontiers in Education, 2023, 8:1150190. [CrossRef] [Google Scholar]
  7. CHARMING, 2024, https://charming-etn.eu/outreach-communication/ [Google Scholar]
  8. Chiang, L., Lu, B. and Castillo, I., Big data analytics in chemical engineering, Annual Review of Chemical and Biochemical Engineering, 2017, 8, 63–85. [Google Scholar]
  9. Colegrove, L.F., Seasholtz, M.B., and Khare, C. Big Data: Getting Started on the Journey. CEP Magazine. 2016, 112, 41–45. [Google Scholar]
  10. Commenge, J.M., Big data et intelligence artificielle pour le génie des procédés, France, 2020, 135 pages, hal- 03107557, https://hal.univ-lorraine.fr/hal-03107557 [Google Scholar]
  11. De Souza, A.S.C. and Debs, L., Concepts, innovative technologies, learning approaches and trend topics in education 4.0: A scoping literature review, Social Science & Humanities Open, 2024, 9, 100902. [CrossRef] [Google Scholar]
  12. Duever, T.A., Data science in the chemical engineering curriculum, Processes, 2019, 7, 830, 1–7. [Google Scholar]
  13. EFCE, 2020, European Federation of Chemical Engineering, EFCE Bologna Recommendations https://efce.info/efcemedia/Downloads/wpe/EFCEBolognaRecommendationsApproved.pdf [Google Scholar]
  14. EFCEWPE (2024), Working Party Education, Teaching methodologies, https://efce.info/Scientific+Groups/Education/Current+activities/Teaching+methodologies.html [Google Scholar]
  15. ENAEE, 2024, European Network for Accreditation of Engineering Education, https://www.enaee.eu/ [Google Scholar]
  16. Feise, H.J. and Schaer, E., Mastering digitized chemical engineering, Education for Chemical Engineers, 2021, 34, 78–86. [CrossRef] [Google Scholar]
  17. Fracaro, S.G., Chan, P., Gallagher, T., Tehreem, Y., Toyoda, R., Bernaerts, K., Glassy, J., Pfeiffer, T., Slof, B., Wachsmuth, S. and Wilk, M., Towards design guidelines for virtual reality training for the chemical industry, Education for Chemical Engineers, 2021, 36, 12–23. [CrossRef] [Google Scholar]
  18. Fracaro, S.G., Tehreem, Y., Toyoda, R., Gallagher, T., Glassey, J., Bernaerts, K. and Wilk, M., Benefits and impact of emergency training in a VR environment, Education for Chemical Engineers, 2024, 48, 63–72. [CrossRef] [Google Scholar]
  19. Gajek, A., Fabiano, B., Laurent, A. and Jensen, N., Process safety education of future employees 4.0 in Industry 4.0, Journal of Loss Prevention in the Process Industries, 2022, 75, 104691:1–29. [CrossRef] [Google Scholar]
  20. Galeazzi, A., Marenghi, P., Duo, L., Galardo, M., Rota, R., Sancassani, S. and Manenti, F., Virtual reality and digital twins for enhanced learning in chemical engineering, Computer Aided Chemical Engineering, 2024, 53, 3535–3540. [CrossRef] [Google Scholar]
  21. Garcia-Munoz, S., MacGregor, J.F., Big data: Success stories in the process industries. CEP Magazine, 2016, 112, 36–40. [Google Scholar]
  22. Horbez, D., L’usine du futur pour les industries de procédés, Livre blanc, SFGP, 2019 https://www.sfgp.asso.fr/2018/05/30/livreLblancLduLgenieLdesLprocedes/&&. [Google Scholar]
  23. Jollydig, A.M. and Schmidt, S., A study of the impact on engineering education institutions in France - Analysis of the answers to the survey (Focus numérique) launched by Commission des Titres d’Ingénieur (CTI), 2020. https://www.cti-commission.fr/wp-content/uploads/2020/11/JOLLYDIG-AM.pdf. [Google Scholar]
  24. Kaler, E.W., National Academies of Sciences, Engineering, and Medicine. 2022. New Directions for Chemical Engineering. Washington, DC: The National Academies Press. https://doi.org/10.17226/26342 http://nap.naptionalacademies.org/26342 [Google Scholar]
  25. Khan, F., Rathnayaka, S. and Ahmed, S., Methods and model in process safety and risk management: Past, present and future, Process Safety and Environmental Protection, 2015, 98, 116–147. [CrossRef] [Google Scholar]
  26. Khan, F., Amyotte, P. and Adedigba, S., Process safety concerns in process digitalization, Education for Chemical Engineers, 2021, 34, 33–46. [CrossRef] [Google Scholar]
  27. Kilbride, H. and Sinanan, K., Safety: Digitalizing process safety, The Chemical Engineer, 2022, July/August, 26–29. [Google Scholar]
  28. Laurent, A. and Fabiano, B., A critical perspective on the impact of Industry 4.0’s new professional safety management skills on process safety education, Chemical Engineering Transactions, 2022, 91, 67–72. [Google Scholar]
  29. Laurent, A., Towards process safety 4.0 in the factory of the future, 2023b, Ed. ISTE and Wiley. [CrossRef] [Google Scholar]
  30. Laurent, A., Sécurité 4.0: Eléments d’évaluation dynamique quantitative des risques des procédés, France, 2023a, 122 pages, hal-04315383, https://hal.univ-lorraine.fr/hal-04315383 [Google Scholar]
  31. Lavor, V., De Come, F., Dos Santos, M.T. and Vianna Jr, A.S., Machine learning in chemical engineering: Hands-on activities, Education for Chemical Engineers, 2024, 46, 10–21. [CrossRef] [Google Scholar]
  32. Meyer, Th., Schaer, E., Abildskov, J., Feise, H., Glassey, J., Liauw, M., O’Suilleabhain, C. and Wilk, M., The important role of education in chemical engineering, Chemical Engineering Research and Design, 2022, 187, 164–173. [CrossRef] [Google Scholar]
  33. Pilario, K.E., Teaching classical machine learning as a graduate-level course in chemical engineering: An algorithmic approach, Digital Chemical Engineering, 2024, 11, 100163, 1–11. [CrossRef] [Google Scholar]
  34. Process Net, 2018, Qualifikationsrahmen für Studiengänge und Promotionen in der Verfahrenstechnik, im Bio- und Chemieingenieurwesen, Empfehlungen für Universitäten und Hochschulen für angewandte Wissenschaften, 3rd Edition, Dechema/VDI, Frankfurt (D). [Google Scholar]
  35. Proctor, M. and Chiang, L., Data science and digitalization for chemical engineers, The Chemical Engineer, 2023, May, 36–40. [Google Scholar]
  36. Qian, Y., Vaddiraju, D. and Khan, F., Safety education 4.0 - A critical review and response to the process industry 4.0 need in chemical engineering curriculum, Safety Science, 2023, 161:106069. [CrossRef] [Google Scholar]
  37. Qin, S.J., Process data analytics in the era of Big data, AIChE Journal, 2014, 66(9), 3092–3100. [CrossRef] [Google Scholar]
  38. Reis, M.S., Braatz, M.B. and Chiang, L.H., Big data: Challenges and future research directions, CEP Magazine, 2016, 112, 46–50. [Google Scholar]
  39. Skjold, T., Teaching process safety in the twenty-first century, In “Proceedings of the 15th International Symposium on Hazards Prevention and Mitigation in Industrial Explosions” (ISHPMIE 2024), pages 45–67, June 10-14, 2024, Naples (Italy) [Google Scholar]
  40. Solmaz, S. and Van Gerven, T., Automated integration of extract-based CFD results with AR/VR in engineering education for practitioners, Multimedia Tools and Applications, 2022, 81, 14869–14891. [CrossRef] [Google Scholar]
  41. Stankiewicz, A., Henczka, M. and Molga, E., Teaching chemical engineering in Europe - developments, dilemmas, and practical examples, Chemical and Process Engineering, 2021, 42 (4), 321–335. [Google Scholar]
  42. Tanner, J. and Newbery, C., Digital Twins in the chemical process industries, The Chemical Engineer, 2022, 976, 44–48. [Google Scholar]
  43. Taube, M.A., Udugama, I.A. and Young, B.R., A digital twin approach to distillation control, CEP Magazine, 2024, January, 1–13. [Google Scholar]
  44. Trinh, C., Meimaroglou, D. and Hoppe, S., Machine Learning in chemical product engineering: The state of the art and a guide for newcomers, Processes, 2021, 9, 1456, 1–44, hal- 03367709. [Google Scholar]
  45. Udugama, I.A., Bayer, C., Baroutian, S., Gernaey, K.V., Yu, W. and Young, B.R., Digitalization in chemical engineering: Industrial needs, academic best practice and curriculum limitations, Education for Chemical Engineers, 2022, 39, 94–107. [CrossRef] [Google Scholar]
  46. Udugama, I.A., Atkins, M., Bayer, C., Carson, J., Dikicioglu, D., Gernaey, K.V., Glassey, J., Taylor, M. and Young, B.R., Digital tools in chemical engineering education: the needs and the desires, Education for Chemical Engineers, 2023, 44, 63–70. [CrossRef] [Google Scholar]
  47. Venkatasubramamian, V., Teaching Artificial Intelligence to chemical engineers: Experience from a 35-year- old course, Chemical Engineering Education, 2022, 56 (4), 231–240. [CrossRef] [Google Scholar]
  48. Ventura-Medina, Rdedition E., Tanner, J. and Young, B., Teaching: Educating chemical engineers on digitalization, The Chemical Engineer, 2022, April, 24–27. [Google Scholar]
  49. Vuorikari, R., Kluzer, S. and Punie, Y., DigComp 2.2: The Digital Competence Framework for Citizens - With new examples of knowledge, skills and attitudes, EUR 31006 EN, Publications Office of the European Union, Luxembourg, 2022, ISBN 978-92-76-48882-8, DOI: 10.2760/115376, JRC128415. https://publications.jrc.ec.europa.eu/repository/handle/JRC128415 [Google Scholar]
  50. White, D., Big data: what is it? CEP Magazine, 2016, 112, 33–25. [Google Scholar]
  51. Wu, M., Di Caprio, U., Vermeire, F., Hellinckx, P., Braeken, L., Waldherr, S., and Enis Leblichi, M., An artificial intelligence course for chemical engineers, Education for Chemical Engineers, 2023, 45, 141–150. [CrossRef] [Google Scholar]
  52. Zandi, M., Glassey, J. and Young, B., 2022, Mind the gap: mapping digitalization skills - Early results analysis presented at Chemical Education online symposium, the next 100 years, https://sites.google.com/sheffield.ac.uk/digimap/updates [Google Scholar]

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