Justin T. Turnage,Andres P. Hyer,Kimberly E. Penzer,James K. Ferri
{"title":"Machine-Learning-Enabled\u0000Design of Lab-Scale Continuous\u0000Distillation Systems for Pharmaceutical Process Development","authors":"Justin T. Turnage,Andres P. Hyer,Kimberly E. Penzer,James K. Ferri","doi":"10.1021/acs.oprd.6c00279","DOIUrl":"https://doi.org/10.1021/acs.oprd.6c00279","url":null,"abstract":"Abstract Predictive tools for continuous distillation design lag behind upstream pharmaceutical synthesis─including AI-enabled digital tooling for retrosynthetic route design─leaving column specification dependent on computationally expensive simulation or extensive experimentation. This work presents a machine-learning surrogate framework trained entirely on first-principles mechanistic simulations, requiring no experimental distillation data, and eliminating the need for commercial tools such as ChemCad or AspenPlus. Three surrogate models for binary tert-butylamine/alcohol systems (methanol, ethanol, isopropanol) predict distillate and bottoms compositions with mean absolute errors below 0.005 mol/mol and R2 > 0.999, completing a single prediction in under 1 ms─a 24-fold speedup. A coupled Pareto optimizer returns capital-versus-operating-cost trade-off curves in under 10 s, with over 93% of designs verified within ±0.010 mol/mol. Applied to a pharmaceutical case study, the framework correctly identifies isopropanol as the optimal solvent without prior thermodynamic knowledge. The system-agnostic architecture could readily integrate with open-source large language model platforms to further lower the barrier to implementation.","PeriodicalId":55,"journal":{"name":"Organic Process Research & Development","volume":"71 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}