Boosting autonomous process design and intensification with formalized domain knowledge

This work looks at how to add formal domain knowledge to self‑running tools for designing and intensifying chemical processes. Usually, computer‑aided process engineering uses manual rules and repeated simulations, which makes it hard to find unusual, intensified options. If we organize the knowledge with formal ontologies and semantic‑web tools, autonomous agents can explore complex design spaces in a systematic way. The method puts chemical unit operations, thermodynamic limits, and intensification principles into a standard knowledge graph. That knowledge base steers optimization algorithms and design generators, stops them from creating flowsheets that break thermodynamic rules, and speeds up finding new intensified processes. The framework can automatically build reaction‑separation systems and gives

a clear way to assess hybrid unit operations. Implementation work focuses on creating semantic models, linking them to process simulators, and testing how well knowledge‑driven search algorithms perform compared with traditional rule‑based design. Overall, this research provides a systematic base for moving from manual, experience‑driven synthesis to fully automated, knowledge‑backed process intensification.

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