Authors
Jasmine J, Amgad S Moussa
Published in
Chimia. Volume 80. Issue 9. Pages 592-596. Sep 30, 2026. Epub Sep 30, 2026.
Abstract
Chemical engineering has evolved by adapting to available tools - from empirical correlations to simulation platforms and high-throughput experimentation. Each transition reshaped what was computationally and experimentally affordable, determining which problems received attention. Artificial intelligence introduces a new shift in this cost structure: specifically, the cost of building mechanistic models and extracting structured knowledge from unstructured data. This perspective argues that AI enables correction of two long-standing imbalances: the limited ability to distinguish exploratory (data-sparse) from exploitative (data-rich) problem modes, and an entrenched preference for experimentation over mechanistic reasoning. We propose a unified framework combining two complementary AI-enabled architectures - the Data Pipeline and the Insight Pipeline - and introduce a four-axis diagnostic for selecting between them based on physical-law knowability, data availability, regime novelty, and reliability constraints. In fine chemical manufacturing, where formalized physical understanding is valuable, yet scarce, large language models offer a practical path to accelerate know-how generation within this framework. A recommended workflow for large language model (LLM)-assisted modeling is outlined, emphasizing auditability, competing hypotheses, uncertainty quantification, and information-rich experimentation. We hope this supports a renewed modeling-first culture in chemical engineering.
PMID:
42831510
Bibliographic data and abstract were imported from PubMed on 05 Oct 2026.
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