Eco-Feasibility Study and Application of Natural Inventive Principles in Chemical Engineering Design

Mas’udah, Mas’udah; Livotov, Pavel; Santosa, Sandra; Sekaran, Arun Prasad Chandra; Takwanto, Anang; Pachulska, Agata M.

doi:10.1007/978-3-031-17288-5_32

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The methodology employed in this study adopts the Solution-Driven Approach (SDA) outlined in Table 1, which builds upon our recent study on learning eco-innovation from nature (Livotov et al, 2020). This SDA was modified from the method introduced by Helms et al (2009) and utilized to identify ecoinventive principles in natural systems for solving eco-engineering contradictions, as previously demonstrated in our research (Mas'udah et al, 2021;Mas'udah et al, 2022). The iterative algorithm begins by systematically searching for biological ecosystems that operate under constant or temporary environmental stress, such as high or low temperatures, extreme sun radiation or other harmful energy fields, as well as exposure to toxic substances or hazardous organisms within the environment etc.…”

Section: Solution-driven Design Approachmentioning

confidence: 99%

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

Mas'udah,

Livotov

2024

Proc. Des. Soc.

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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.

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