Reusable surrogate models for distillation columns

Surrogate modeling is a powerful methodology in chemical process engineering, frequently employed to accelerate optimization tasks. Despite their popularity, most surrogate models are trained for a narrow range of fixed chemical systems and operating conditions, which limits their reusability. This work introduces a paradigm shift towards reusable surrogates by developing a single model for distillation columns that generalizes across a vast design space. The key enabler is a novel ML-fueled modelfluid representation which allows for the generation of datasets of more than 1000000 samples. This allows the surrogate to generalize not only over column specifications but also over the entire chemical space of homogeneous ternary vapor–liquid mixtures. We validate the model's accuracy and demonstrate its practical utility in a case study on entrainer distillation, where it successfully screens and ranks candidate entrainers, significantly reducing the computational effort compared to rigorous optimization.