CAD to characterization: Machine learning on experimental data for additively manufactured helical distillation columns

Traditional workflows in chemical engineering, from design to characterization, have served the field well. Emerging technologies, such as machine learning, now offer opportunities for faster design iterations and better integration of the various development stages. This study demonstrates how machine learning (ML) can reduce this gap by directly integrating experimental data into predictive models. Focusing on a Additive Manufactured helical micro-distillation column design for separating liquid mixtures, several ML models were trained to predict the number of theoretical stages based on geometric parameters and operating conditions. 11 variants were designed, built, and tested to collect 197 experimental data sets to assess the feasibility of five predictive ML models from experimental data. Among the algorithms tested, the Gradient Boosting Regressor achieved the best performance with a coefficient of determination R 2 = 0 . 7934 . The work highlights the behavior of the model across different regimes, identifies key sources of error, and proposes a hybrid experimental–ML workflow for rapid screening of distillation designs. This approach accelerates process development and reduces the need for extensive experimentation, especially in time-consuming tasks such as distillation. • ML predicts KPIs for complex AM helical distillation columns using real data. • Multiple supervised ML models benchmarked for AM-induced geometric effects. • Multi-output modeling enhances stability and captures physical correlations. • Hybrid ML–experimental workflow identifies high-value regions for testing. • Framework accelerates design and screening of AM-enabled separation units.