Top

Published in:

2025 | OriginalPaper | Chapter

Harnessing Generative AI for Sustainable Innovation: A Comparative Study of Prompting Techniques and Integration with Nature-Inspired Principles

Authors : Mas’udah, Pavel Livotov, Björn Kokoschko

Published in: World Conference of AI-Powered Innovation and Inventive Design

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Amidst growing environmental challenges and the imperative for sustainable solutions, this study explores how generative artificial intelligence (AI) can drive innovation in process engineering. It investigates the effectiveness of different prompting techniques and their integration with nature-inspired principles (NIP) in fostering sustainable innovation. The study employs a comparative methodology to assess the effectiveness of two distinct prompting techniques: basic and AI-automated prompting. It also examines the influence of integrating NIP derived from various natural ecosystems on the generated solutions. Experiments were conducted using a generative AI model and analysing the output, focusing on the number of unique and overlapping ideas. Furthermore, the quality of AI-generated solution concepts was evaluated using four parameters, such as feasibility, novelty, usefulness, and sustainability, each rated on a scale of 0 to 2. Three case studies within the process engineering domain were used, each representing a different problem-solving scenario. The results showed that the integration of NIP, particularly through the “one by one” strategy in AI-automated prompting, significantly increased the number of unique ideas compared to basic prompting, demonstrating its effectiveness in enhancing idea diversity and quality. Concepts generated through this approach also scored higher in novelty and sustainability, aligning with sustainable innovation goals. These findings have practical implications for developing innovative and sustainable engineering solutions, particularly in the early phases of design, offering insights into effective strategies for leveraging AI in eco-innovation.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Integrating Generative AI with TRIZ for Evolutionary Product Design

next chapter Neuro-Symbolic AI-Driven Inventive Design of a Benzoic Acid Extraction Installation from Styrax Resin

Livotov, P., Mas’udah, Chandra Sekaran, A.P.: Learning eco-innovation from nature: towards identification of solution principles without secondary eco-problems. In: Cavallucci, D., Brad, S., Livotov, P. (eds.) TFC 2020. IFIP AICT, vol. 597, pp. 172–182. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61295-5_14

Bianciardi, A., Becattini, N., Cascini, G.: How would nature design and implement nature-based solutions?. Nat.-Based Solut. 3, 100047 (2023). https://doi.org/10.1016/j.nbsj.2022.100047. ISSN 2772-4115

Mas’udah, Santosa, S., Livotov, P., Chandra Sekaran, A.P., Rubianto, L.: Nature-inspired principles for sustainable process design in chemical engineering. In: Borgianni, Y., Brad, S., Cavallucci, D., Livotov, P. (eds.) TFC 2021. IFIP Advances in Information and Communication Technology, vol. 635, pp. 30–41. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86614-3_3

Zhu, Q., Luo, J.: Generative pre-trained transformer for design concept generation: an exploration. In: Proceedings of the Design Society, vol. 2, pp. 1825–1834. Cambridge University Press (2022). https://doi.org/10.1017/pds.2022.185

Zhu, Q., Luo, J.: Generative transformers for design concept generation. J. Comput. Inf. Sci. Eng. 23(4), art. 041003 (2023). https://doi.org/10.1115/1.4056220

Mas’udah, Livotov, P.: Nature’s lessons, AI’s power: sustainable process design with generative AI. In: Proceedings of the Design Society, vol. 4, pp. 2129–2138. Cambridge University Press (2024). https://doi.org/10.1017/pds.2024.215

ChatGPT Homepage. https://chat.openai.com/. Accessed 24 June 2024

Zhang, C., Lu, J., Zhao, Y.: Generative pre-trained transformers (GPT)-based automated data mining for building energy management: advantages, limitations and the future. J. Energy Built Environ. 5(1), 143–169 (2024). https://doi.org/10.1016/j.enbenv.2023.06.005CrossRef

Zhu, Q., Zhang, X., Luo, J.: Biologically inspired design concept generation using generative pre-trained transformers. J. Mech. Design 145(4), art. 041409 (2023)

10.

AskNature Homepage. https://asknature.org/. Accessed 12 Aug 2024

11.

Gemini Homepage. https://gemini.google.com/. Accessed 24 June 2024

12.

Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual design generation using large language models. In: Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 6. Boston, Massachusetts, USA (2023). https://doi.org/10.1115/DETC2023-116838

13.

White, J., et al.: A prompt pattern catalog to enhance prompt engineering with ChatGPT. ArXiv (2023). https://doi.org/10.48550/arXiv.2302.11382

14.

Coppens, M.-O.: Nature inspired chemical engineering for process intensification. Ann. Rev. Chem. Biomol. Eng. 12, 187–215 (2021). https://doi.org/10.1146/annurev-chembioeng-060718-030249CrossRef

15.

Trogadas, P., Coppens, M.-O.: Chapter 2 - Nature-inspired chemical engineering: a new design methodology for sustainability. In: Szekely, G., Livingston, A. (eds.) Sustainable Nanoscale Engineering, pp. 19–31. Elsevier, Amsterdam (20202019). https://doi.org/10.1016/B978-0-12-814681-1.00002-3

16.

Mas’udah, Livotov, P., Santosa, S., Sekaran, A.P.C., Takwanto, A., Pachulska, A.M.: Eco-feasibility study and application of natural inventive principles in chemical engineering design. In: Nowak, R., Chrząszcz, J., Brad, S. (eds.) TFC 2022. IFIP AICT, vol. 655, pp. 382–394. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17288-5_32

17.

Altshuller, G.S.: Creativity as an Exact Science. The Theory of the Solution of Inventive Problems. Gordon & Breach Science Publishers, New York (1984)

18.

Savelli, S., Abramov, O.Y.: Nature as a source of function-leading areas for FOS-derived solutions. TRIZ Rev. J. Int. TRIZ Assoc. MATRIZ 1(1), 86–98 (2019)

19.

Fayemi, P.-E., Gilles, M., Gazo, C.: Innovative technical creativity methodology for bio-inspired design. In: Cavallucci, D., De Guio, R., Koziołek, S. (eds.) TFC 2018. IAICT, vol. 541, pp. 253–265. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02456-7_21CrossRef

20.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)

21.

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., et al.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. Comput. Surv. 55(9), 1–35 (2023)CrossRef

22.

Mas’udah, Livotov, P., Santosa, S., Suryadi, A.: 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.) TFC 2023. IFIP Advances in Information and Communication Technology, vol. 682, pp. 211–225. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42532-5_16

23.

Shah, J.J., Kulkarni, S.V., Vargas-Hernandez, N.: Evaluation of idea generation methods for conceptual design: effectiveness metrics and design of experiments. J. Mech. Des. 122(4), 377–384 (2000)CrossRef

24.

Baffo, I., Leonardi, M., Bossone, B., Camarda, M.E, D’Alberti, V., Travaglioni, M.: A decision support system for measuring and evaluating solutions for sustainable development. Sustain. Futures 5, 100109 (2023). https://doi.org/10.1016/j.sftr.2023.100109. ISSN 2666-1888

Title: Harnessing Generative AI for Sustainable Innovation: A Comparative Study of Prompting Techniques and Integration with Nature-Inspired Principles
Authors: Mas’udah
Pavel Livotov
Björn Kokoschko
Publisher: Springer Nature Switzerland
Book: World Conference of AI-Powered Innovation and Inventive Design
Print ISBN: 978-3-031-75918-5

Electronic ISBN: 978-3-031-75919-2

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-3-031-75919-2_4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"