Complex thermodynamic models such as the perturbed chain statistical associating fluid theory (PC-SAFT) model describe the phase equilibria in a chemical process in a very precise way; however, because of their implicit and complex nature, the application of such models in process simulation and optimization can lead to a high computational effort, which may prevent the direct application of such models in process simulation and optimization. In this contribution, we replace the iterative calculation of the fugacity coefficient using PC-SAFT with explicit surrogate models that are trained using a novel adaptive sampling method.