Abstract
The extraction of benzoic acid from natural resins such as Styrax holds considerable industrial significance, given its widespread use in pharmaceuticals, food, and cosmetics. This study introduces an approach to enhance the extraction process by designing a novel installation in this respect. The design roadmap integrates Generative AI with neuro-symbolic AI algorithms. We employ a neuro-symbolic AI framework that merges AI’s generative capabilities for initial design conceptualization with symbolic reasoning, enriched with TRIZ principles and Complex Systems Design Thinking (CSDT) methodologies. This combination aids in navigating complex problem-solving scenarios and promoting an innovative solution. Environmental issues are integrated throughout the design process to ensure that the solution also meets eco-sustainability objectives. Results indicate that the novel design markedly enhances the extraction efficiency of benzoic acid, reduces energy consumption, and lowers waste production. The design’s adaptability for industrial applications has been validated, with future enhancements aimed at incorporating real-time monitoring AI systems for dynamic adjustments based on raw material variability.
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Brad, S., Bartoș, VD., Brad, E., Trifan, CV. (2025). Neuro-Symbolic AI-Driven Inventive Design of a Benzoic Acid Extraction Installation from Styrax Resin. In: Cavallucci, D., Brad, S., Livotov, P. (eds) World Conference of AI-Powered Innovation and Inventive Design. TFC 2024. IFIP Advances in Information and Communication Technology, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-031-75919-2_5
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DOI: https://doi.org/10.1007/978-3-031-75919-2_5
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