Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-18T17:07:43.891Z Has data issue: false hasContentIssue false
  • English
  • Français

Nature's lessons, AI's power: sustainable process design with generative AI

Published online by Cambridge University Press:  16 May 2024

Mas'udah Mas'udah  and
Pavel Livotov
Mas'udah Mas'udah*
Affiliation:
Offenburg University of Applied Sciences, Germany
Pavel Livotov
Affiliation:
Offenburg University of Applied Sciences, Germany
*
[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In the realm of process engineering, the pursuit of sustainability is paramount. Traditional approaches can be time-consuming and often struggle to address modern environmental challenges effectively. This article explores the integration of generative AI, as a powerful tool to generate solution ideas and solve problems in process engineering using a Solution-Driven Approach (SDA). SDA applies nature-inspired principles to tackle intricate engineering challenges. In this study, generative AI is trained to understand and use the SDA patterns to suggest solutions to complex engineering challenges.

Keywords

process designsustainabilityartificial intelligence (AI)GPTGemini
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2129 - 2138
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Bianciardi, A., Becattini, N., Cascini, G. (2023), “How would nature design and implement nature-based solutions?”, Nature-Based Solutions, Vol. 3, 100047, ISSN 2772-4115. https://doi.org/10.1016/j.nbsj.2022.100047CrossRefGoogle Scholar
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D. et al. (2020), “Language models are few-shot learners”, Advances in neural information processing systems, 33, pp. 1877–1901.Google Scholar
Coppens, M.-O. (2021), "Nature inspired chemical engineering for process intensification", Ann. Rev. Chem. Biomol. Eng. 12, 187–215. https://doi.org/10.1146/annurev-chembioeng-060718-030249CrossRefGoogle ScholarPubMed
Helms, M., Vattam, S. S., and Goel, A. K. (2009), “Biologically inspired design: process and products,” Design studies, 30(5), 606-622. https://dx.doi.org/10.1016/j.destud.2009.04.003CrossRefGoogle Scholar
Hu, X., Tian, Y., Nagato, K., Nakao, M., Liu, A. (2023), “Opportunities and challenges of ChatGPT for design knowledge management”, Procedia CIRP, Vol. 119, pp. 21-28, ISSN 2212-8271. https://doi.org/10.1016/j.procir.2023.05.001CrossRefGoogle Scholar
Jin, Y. (2021), Artificial intelligence developed through nature-inspired AI research. [online] Innovation News Network. Available at: https://www.innovationnewsnetwork.com/artificial-intelligence-developed-through-nature-inspired-ai-research/16621/ (accessed 03.11.2023).Google Scholar
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H. et al. (2023), “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing”, Comput. Surveys 55(9), 1–35.Google Scholar
Livotov, P., Mas'udah, , Chandra Sekaran, A.P. (2020), “Learning eco-innovation from nature: towards identification of solution principles without secondary eco-problems”, In: Cavallucci, D., Brad, S., Livotov, P. (eds.) Systematic Complex Problem Solving in the Age of Digitalization and Open Innovation. TFC 2020. IFIP AICT, Springer, Cham, vol. 597, pp. 172–182. https://doi.org/10.1007/978-3-030-61295-5_14Google Scholar
Maree, W., Kloppers, L., Hangone, G., Oyekola, O. (2017), “The effects of mixtures of potassium amyl xanthate (PAX) and isopropyl ethyl thionocarbamate (IPETC) collectors on grade and recovery in the froth flotation of a nickel sulfide ore”, South African Journal of Chemical Engineering, Vol. 24, pp. 116-121, ISSN 1026-9185. https://doi.org/10.1016/j.sajce.2017.07.001CrossRefGoogle Scholar
Mas'udah, , Livotov, P., Santosa, S., Sekaran, C., Takwanto, A. et al. (2022), “Eco-feasibility study and application of natural inventive principles in chemical engineering design”, In: Nowak, R., Chrz ąszcz, J., Brad, S. (eds.) Systematic Innovation Partnerships with Artificial Intelligence and Information Technology. TFC 2022. IFIP AICT, Springer, Cham, Vol. 655, pp. 382–394. https://doi.org/10.1007/978-3-031-17288-5_32Google Scholar
Mas'udah, , Livotov, P., Santosa, S., Suryadi, A. (2023), “Classification of Nature-Inspired Inventive Principles for Eco-innovation and Their Assignment to Environmental Problems in Chemical Industry", In: Cavallucci, D., Livotov, P., Brad, S. (eds) Towards AI-Aided Invention and Innovation. TFC 2023. IFIP Advances in Information and Communication Technology, Springer, Cham, Vol 682, pp. 211-225. https://doi.org/10.1007/978-3-031-42532-5_16CrossRefGoogle Scholar
Mas'udah, , Santosa, S., Livotov, P., Chandra Sekaran, A.P., Rubianto, L. (2021), “Nature-inspired principles for sustainable process design in chemical engineering”, In: Borgianni, Y., Brad, S., Cavallucci, D., Livotov, P. (eds.) Creative Solutions for a Sustainable Development. TFC 2021. IFIP Advances in Information and Communication Technology, Springer, Cham, Vol. 635, pp. 30–41. https://doi.org/10.1007/978-3-030-86614-3_3Google Scholar
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L. et al. (2022), “Training language models to follow instructions with human feedback”, Computer Science (preprint). https://doi.org/10.48550/arXiv.2203.02155CrossRefGoogle Scholar
Ray, S.S., Peddinti, P.R.T., Verma, R.K., Puppala, H., Kim, et al. (2024), “Leveraging ChatGPT and Bard: What does it convey for water treatment/desalination and harvesting sectors?”, Desalination, Vol. 570, 117085, ISSN 0011-9164. https://doi.org/10.1016/j.desal.2023.117085CrossRefGoogle Scholar
Shah, J. J., Kulkarni, S. V., and Vargas-Hernandez, N. (2000), “Evaluation of idea generation methods for conceptual design: effectiveness metrics and design of experiments”, J. Mech. Des., 122(4), pp. 377–384CrossRefGoogle Scholar
Shah, J. J., Smith, S. M., and Vargas-Hernandez, N., (2003), “Metrics for measuring ideation effectiveness”, Design studies, 24(2), pp. 111–134.CrossRefGoogle Scholar
Trogadas, P., Coppens, M.-O. (2020), “Chapter 2 - Nature-inspired chemical engineering: a new design methodology for sustainability”, In: Szekely, G., Livingston, A. (eds.) Sustainable Nanoscale Engineering, Elsevier, Amsterdam, pp. 19–31. https://doi.org/10.1016/B978-0-12-814681-1.00002-3CrossRefGoogle Scholar
Zhu, Q., and Luo, J. (2022), “Generative pre-trained transformer for design concept generation: an exploration”, Proceedings of the Design Society, 2, pp. 1825–1834. https://doi.org/10.1017/pds.2022.185CrossRefGoogle Scholar
Zhu, Q., and Luo, J. (2023), “Generative transformers for design concept generation”, Journal of Computing and Information Science in Engineering, 23(4), art. 041003. https://doi.org/10.1115/1.4056220Google Scholar
Zhu, Q., Zhang, X., and Luo, J. (2023), “Biologically inspired design concept generation using generative pre-trained transformers”, Journal of Mechanical Design, 145(4), art. 041409.CrossRefGoogle Scholar