Inadequacy of current approaches for characterizing membrane transport properties at high salinities

Cost optimal design of osmotic membrane processes requires an accurate estimate of membrane transport parameters across their full operational range. However, standard approaches for estimating these parameters rely on empirical methods, the accuracy of which remains unquantified as a function of temperature, salinity, and measurement error. Herein, we present a systematic accuracy analysis of previously developed methods for estimation of membrane transport properties in reverse osmosis, high-pressure reverse osmosis, forward osmosis, pressure retarded reverse osmosis, and osmotically assisted reverse osmosis. We use a Monte Carlo approach to sample the full range of feasible membrane water permeabilities, salt permeabilities, structural parameters, and operating conditions for these processes. These material and process parameters are then incorporated into a physical transport model for each process. Our analysis shows that the statistical uncertainty of current empirical methods for estimating membrane parameters increases by 5 times from low-salinity to high-salinity conditions. The result of this work demonstrates that empirical methods are inadequate for precisely estimating membrane transport properties at high salinity and highlight a critical need for the development of statistically validated higher accuracy methods.

Automated summary

Accurate estimation of membrane transport parameters is crucial for cost optimal design of osmotic membrane processes. However, current empirical methods for parameter estimation lack quantified accuracy, especially under high salinity conditions. In this study, we systematically analyze the accuracy of previously developed methods for estimating membrane transport properties in various osmotic membrane processes. Our results show a significant increase in uncertainty of parameter estimation from low-salinity to high-salinity conditions, highlighting the need for higher accuracy methods that are statistically validated.