Surrogate modeling of phase equilibrium calculations using adaptive sampling

Abstract

Equation of state models as the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) model are accurate and reliable prediction models for phase equilibria. But due to their iterative nature, they are difficult to apply in chemical process optimization, because of long computation times. To overcome this issue, surrogate modeling – replacing a complex model by a black-box model – can be used. A novel surrogate modeling strategy for phase equilibria is presented, combining the training of a classifier model with regression models for the phase composition using a mixed adaptive sampling method. We discuss the selection of the parameters of the sampling algorithm and a suitable stop criterion for the example ternary liquid-liquid equilibrium system of n-decane, dimethylformamide and 1-dodecene in detail. The sequential mixed adaptive sampling method is compared to the one-shot Latin hypercube sampling design.