{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T12:53:02Z","timestamp":1791550382677,"version":"4.3.4"},"reference-count":0,"publisher":"Swiss Chemical Society","issue":"9","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Chimia"],"abstract":"<jats:p>Chemical engineering has evolved by adapting to available tools \u2013 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 \u2013 the Data Pipeline and the Insight Pipeline \u2013 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.<\/jats:p>","DOI":"10.2533\/chimia.2026.592","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T23:39:24Z","timestamp":1790725164000},"page":"592-596","source":"Crossref","is-referenced-by-count":0,"title":["Building Shared Futures: Chemical Engineering and AI as Partners in Process Development"],"prefix":"10.2533","volume":"80","author":[{"given":"Jasmine","family":"J","sequence":"first","affiliation":[{"name":"Global Process Technology, Syngenta, Muenchwilen, CH-4333"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amgad S.","family":"Moussa","sequence":"additional","affiliation":[{"name":"Global Process Technology, Syngenta, Muenchwilen, CH-4333"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1442","published-online":{"date-parts":[[2026,9,30]]},"container-title":["CHIMIA"],"original-title":[],"link":[{"URL":"https:\/\/www.chimia.ch\/chimia\/article\/download\/2026_592\/2026_592","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.chimia.ch\/chimia\/article\/download\/2026_592\/2026_592","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T12:02:20Z","timestamp":1791547340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.chimia.ch\/chimia\/article\/view\/2026_592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,30]]},"references-count":0,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,30]]}},"URL":"https:\/\/doi.org\/10.2533\/chimia.2026.592","relation":{},"ISSN":["2673-2424","0009-4293"],"issn-type":[{"value":"2673-2424","type":"electronic"},{"value":"0009-4293","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9,30]]}}}