Abstract
The synthesis of membrane networks to recover components from liquid mixture is challenging due to an extensive array of feasible network configurations and the added complexity of modeling membrane permeators caused by nonidealities in liquid mixtures. We present a mixed-integer nonlinear programming (MINLP) framework for synthesizing membrane networks to recover multiple components from liquid mixtures. First, we develop a physics-based nonlinear surrogate model to accurately describe crossflow membrane permeation. Second, we propose a richly connected superstructure to represent numerous potential network configurations. Third, the two aforementioned elements are integrated into an MINLP model to determine the optimal network configuration. Finally, the effectiveness of the proposed approach is demonstrated through a range of applications.