(73b) Molecular Simulations and Machine Learning for Multicomponent Adsorption
We performed high-throughput Monte Carlo simulations in the Gibbs ensemble to predict real fluid properties of liquid, vapor, and supercritical fluids in equilibrium with zeolite adsorbents. We examined unary adsorption and competitive adsorption of mixtures with up to eight components. These simulations provide numerous discrete data points that are used to fit an artificial neural network, which provides a single, self-consistent, continuous, and differentiable function describing the properties of all fluids and adsorbed phases across the full range of realistic process conditions. Properties and their derivatives from the neural network can enable accurate predictions for process optimization and control.