Classification of Nature-Inspired Inventive Principles for Eco-innovation and Their Assignment to Environmental Problems in Chemical Industry

Mas’udah,; Livotov, Pavel; Santosa, Sandra; Suryadi, Akhmad

doi:10.1007/978-3-031-42532-5_16

Cited by 1 publication

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite promising outcomes, the methodologies used to generate solution ideas in this study require validation through practical implementation and rigorous evaluation to ensure real-world effectiveness. In addition, the strategies of natural ecosystem components identified by AI could not be translated into abstract inventive principles for engineering in the same way as we (humans) have done in our recent work (Mas'udah et al, 2023). Furthermore, the AI's analysis of the comprehensive interactions within the natural ecosystem often remained incomplete in our experiments.…”

Section: Concluding Remarks and Outlookmentioning

confidence: 91%

“…Therefore, identifying such conflicts of objectives is often crucial in extracting natural solutions. Generally, phases 2 to 4 of the SDA are highly resource-intensive as the identification and classification of natural inventive principles in natural ecosystems is a challenging and time-consuming task that requires expert knowledge and experience (Mas'udah et al, 2023). Additionally, the utilization of conventional methods may also be limited by constraints in understanding the abstract and complex concepts of these natural principles.…”

Section: Solution-driven Design Approachmentioning

confidence: 99%

See 1 more Smart Citation

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

Mas'udah,

Livotov

2024

Proc. Des. Soc.

View full text Add to dashboard Cite

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.

show abstract

Section: Concluding Remarks and Outlookmentioning

confidence: 91%

Section: Solution-driven Design Approachmentioning

confidence: 99%