Empowering Automated Process Analysis through LLM-Based Literature Mining, Flowsheet Digitization, and Simulation
TL;DR
It is shown that the integrated pipelines presented can faithfully collect and digitize chemical process information by comparing to expert-curated datasets and manually drawn flowsheets and are on par with expert interpretations with significantly less manual effort.
Abstract
The chemical industry needs to transition from predominantly linear, carbon-emitting production routes to circular, carbon-reusing processes. Therefore, every current and future production process needs to be critically evaluated and potentially re-designed. Today, process design and assessment rely on detailed process simulations [1]. However, constructing these simulations remains a bottleneck, demanding a high degree of expertise and manual work. Here, we present an automated workflow which gathers process knowledge, generates process simulations, and evaluates process performance. The automated workflow consists of two data pipelines: The first pipeline systematically extracts information from literature [2] and prepares a knowledge base of established, industrially relevant chemical processes down to the level of unit operations and thermodynamic properties. This knowledge is aggregated into one text per process and fed into the second pipeline, “text2flowsheet” [3], which digitizes expert-level flowsheet graphs from natural-language descriptions. Both pipelines leverage LLMs' language comprehension capabilities, while the flowsheet digitization is further grounded in rigorous thermodynamic calculations. The digitized flowsheet graphs are systemically translated into simulations within an established commercial process simulator. Potential simplifications necessary to achieve convergence are recorded transparently. Missing information on operating parameters is augmented by systematic, unit-by-unit black-box optimization. We show that our integrated pipelines can faithfully collect and digitize chemical process information by comparing to expert-curated datasets and manually drawn flowsheets. Furthermore, the generated process simulations are on par with expert interpretations with significantly less manual effort. We present case studies illustrating how the results of the automatically generated process simulations can be used to assess process sustainability and derive optimization potential.