Wolf, Alexander B.Alexander B.WolfSkiborowski, MirkoMirkoSkiborowskiBurger, JakobJakobBurger2026-10-092026-10-092026-06-1936th European Symposium on Computer Aided Process Engineering, ESCAPE 2026https://hdl.handle.net/11420/65252This work presents a data-driven surrogate modeling framework for predicting distillation behavior assuming an infinite number of stages and distillation limits informed by residue-curve topology and pinch-point feasibility analysis. The framework provides a direct mapping from feed composition and distillate-to-feed ratio (D/F) to distillate and bottom product compositions, making it suitable for flowsheet synthesis and optimization applications. The approach combines three components: a classifier that identifies feasible singular-point splits, a boundary regression model that predicts D/F limits separating pure- and mixed-product operating regimes, and a neural network that interpolates product compositions in the intermediate regime. The method is demonstrated for the ternary system ethanol, benzene, and water at 1 atm using data generated from rigorous vapor–liquid–liquid equilibrium analysis. Results show that the framework provides reliable predictions for pure splits while retaining smooth interpolation behavior in the mixed regime.en2818-4734202611451152PSE Presshttps://creativecommons.org/licenses/by-sa/4.0/Technology::660: Chemistry; Chemical Engineering::660.2: Chemical EngineeringComputer Science, Information and General Works::006: Special computer methodsConference Paper10.69997/sct.17434510.15480/882.18819