A neural model of pinch-based multicomponent distillation for applications in flowsheet synthesis

This 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.

DDC Class

660.2: Chemical Engineering

006: Special computer methods

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https://creativecommons.org/licenses/by-sa/4.0/

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publishedVersion

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LAPSE-2026.0347-1v1-1.pdf

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1.27 MB

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A neural model of pinch-based multicomponent distillation for applications in flowsheet synthesis