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This document summarizes a study that used artificial neural networks to estimate infinite dilute diffusion coefficients of binary systems involving supercritical fluids, liquids, and gases. The study compiled a dataset of 231 systems and 4078 data points involving these types of mixtures. Separate three-layer feedforward neural networks were trained and tested on each type of system (supercritical fluid, liquid, gas) with inputs including critical properties, molecular sizes, temperature, and pressure. The

Author

reza

Language

EN