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All issues Volume 664 (2025) E3S Web Conf., 664 (2025) 09011 References
Open Access
Issue
E3S Web Conf.
Volume 664, 2025
4th International Seminar of Science and Applied Technology: “Green Technology and AI-Driven Innovations in Sustainability Development and Environmental Conservation” (ISSAT 2025)
Article Number 09011
Number of page(s) 11
Section Sustainable Development
DOI https://doi.org/10.1051/e3sconf/202566409011
Published online 20 November 2025
  1. N. Cherkasov, S. J. Adams, E. G. A. Bainbridge, J. A. M. Thornton, Continuous stirred tank reactors in fine chemical synthesis for efficient mixing, solids-handling, and rapid scale-up, React. Chem. Eng. (2023). Accessed Sep. 9, 2025. https://pubs.rsc.org/en/content/articlehtml/2023/re/d2re00232a [Google Scholar]
  2. O. K. Sabri, Rethinking sustainability in engineering education: a call for systemic change, Front. Educ. (Lausanne) 10, (2025). https://doi.org/10.3389/feduc.2025.1587430 [Google Scholar]
  3. T. Savage, N. Basha, J. McDonough, J. Krassowski, O. Matar, E. A. del Rio Chanona, Machine learning-assisted discovery of flow reactor designs, Nat. Chem. Eng. 1, 522–531 (2024). https://doi.org/10.1038/s44286-024-00099-1 [Google Scholar]
  4. H. Stagge, M. Gäßler, T. Oyuncu, R. Güttel, nRTD: determining complex residence time distributions from experimental data using convolutional neural networks. Chem. Eng. Sci. 320, 122454 (2026). https://doi.org/10.1016/j.ces.2025.122454 [Google Scholar]
  5. J. A. Ochoa-Tapia, R. Hernandez-Rodriguez, J. Alvarez-Ramirez, Effect of the residence time distribution on the dynamical behavior of isothermal continuous stirred tank reactors: a nonlocal modeling approach, Ind. Eng. Chem. Res. 64, 6433–6444 (2025). https://doi.org/10.1021/acs.iecr.5c00604 [Google Scholar]
  6. Y. Mu, Y. Chen, J. Fan, Y. Wu, Y. Yan, S. Wang, Y. Cheng, Teaching practice on a compact desktop experimental system to enable facile hands-on learning of residence time distribution, Educ. Chem. Eng. 50, 53–58 (2025). https://doi.org/10.1016/j.ece.2024.12.005 [Google Scholar]
  7. A. Deetman, D. Bos, J. Blaakmeer, T. Salet, S. Lucas, An in-line dye tracer experiment to measure the residence time in continuous concrete processing, Mater. Struct. 57, 1–18 (2024). https://doi.org/10.1617/s11527-024-02378-y [Google Scholar]
  8. A. E. Rodrigues, Residence time distribution (RTD) revisited, Chem. Eng. Sci. 230, (2021). https://doi.org/10.1016/j.ces.2020.116188 [Google Scholar]
  9. M. T. Nechita, G. D. Suditu, A. C. Puițel, E. N. Drăgoi, Residence time distribution: literature survey, functions, mathematical modeling, and case study—diagnosis for a photochemical reactor, Processes 11, (2023). https://doi.org/10.3390/pr11123420 [Google Scholar]
  10. M. R. Chapman, M. H. Kwan, G. King, K. E. Jolley, M. Hussain, S. Hussain, A. J. Blacker, Simple and versatile laboratory scale CSTR for multiphasic continuous-flow chemistry and long residence times, Org. Process Res. Dev. 21, 1294–1301 (2017). https://doi.org/10.1021/acs.oprd.7b00173 [Google Scholar]
  11. J. L. da Silva Jr., H. S. Santana, Residence time distribution in reactive and non- reactive flow systems in micro and millidevices, Chem. Eng. Sci. 248, (2022). https://doi.org/10.1016/j.ces.2021.117163 [Google Scholar]
  12. L. Ji, B. Wu, K. Chen, J. Zhu, Experimental study and modeling of residence time distribution in impinging stream reactor with GDB model, J. Ind. Eng. Chem. 16, 646–650 (2010). https://doi.org/10.1016/j.jiec.2009.09.071 [Google Scholar]
  13. C. M. Rebello, G. F. Deiró, H. K. Knuutila, L. C. S. Moreira, I. B. R. Nogueira, Augmented reality for chemical engineering education, Educ. Chem. Eng. 47, 30–44 (2024). https://doi.org/10.1016/j.ece.2024.04.001 [Google Scholar]
  14. L. Schaare, A. Rave, R. Kuwertz, G. Fieg, M. Skiborowski, Axial dispersion modelling of the residence time distribution in a millistructured plate reactor, Chem. Eng. Process. Process Intensif. 213, 110295 (2025). https://doi.org/10.1016/j.cep.2025.110295 [Google Scholar]
  15. T. Freese, N. Elzinga, M. Heinemann, M. M. Lerch, B. L. Feringa, The relevance of sustainable laboratory practices, RSC Sustain. 2, 1300–1336 (2024). https://doi.org/10.1039/d4su00056k [Google Scholar]
  16. A. W. Patwardhan, J. B. Joshi, S. Fotedar, T. Mathew, Optimization of gas–liquid reactor using computational fluid dynamics, Chem. Eng. Sci. 60, 3081–3089 (2005). https://doi.org/10.1016/j.ces.2004.12.034 [Google Scholar]
  17. K. Priyadi, C. T. Lu, H. Sutanto, Optimization of impeller design for stirred tank using computational fluid dynamics, in IOP Conf. Ser. Mater. Sci. Eng., Manado: IOP Publ. Ltd, 567, 012032 (2019). https://doi.org/10.1088/1757-899x/567/1/012032 [Google Scholar]
  18. T. Salmi, P. Tolvanen, K. Eränen, J. Wärnå, Experimental determination and mathematical modelling of residence time distributions by using pieces of urban art, Front. Chem. Eng. 4, (2022). https://doi.org/10.3389/fceng.2022.1054124 [Google Scholar]
  19. N. Chan, M. F. Cunningham, R. A. Hutchinson, Continuous controlled radical polymerization of methyl acrylate with copper wire in a CSTR, Polym. Chem. 2, (2012). https://doi.org/10.1039/c2py00509c [Google Scholar]
  20. A. Herth, R. Verburg, K. Blok, How can campus living labs thrive to reach sustainable solutions? Clean. Prod. Lett. 8, (2025). https://doi.org/10.1016/j.clpl.2024.100078 [Google Scholar]
  21. T. Herink, V. Bělohlav, T. Jirout, Z. Bělohlav, Opportunities of experiential education in chemical technology and engineering, Educ. Chem. Eng. 41, (2022). https://doi.org/10.1016/j.ece.2022.08.003 [Google Scholar]
  22. J. M. Sanchez, Integrating measurement uncertainty analysis into laboratory education for the development of critical thinking and practical skills, J. Chem. Educ. 101, 4783–4789 (2024). https://doi.org/10.1021/acs.jchemed.4c00583 [Google Scholar]
  23. Y. Pan, A. Hashemizadeh, Circular economy-based assessment framework for enhancing sustainability in renewable energy development with life cycle considerations, Environ. Impact Assess. Rev. 103, (2023). https://doi.org/10.1016/j.eiar.2023.107289 [Google Scholar]

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