Reverse
osmosis (RO) is a membrane technology that separates dissolved
species from water. RO has been applied for the removal of chemical
contaminants from water for potable reuse applications. The presence
of a wide variety of influent chemical contaminants and the insufficient
rejection of low-molecular-weight neutral organics by RO calls for
the need to develop a model that predicts the rejection of various
organics. In this study, we develop a group contribution method (GCM)
to predict the mass transfer coefficients by fragmenting the structure
of low-molecular-weight neutral organics into small parts that interact
with the RO membrane. Overall, 54 organics including 26 halogenated
and oxygenated alkanes, 8 alkenes, and 20 alkyl and halobenzenes were
used to determine 39 parameters to calibrate for 6 different RO membranes,
including 4 brackish water and 2 seawater membranes. Through six membranes,
approximately 80% of calculated rejection was within an error goal
(i.e., ±5%) from the experimental observation. To extend the
GCM for a reference RO membrane, ESPA2-LD, 14 additional organics
were included from the literature to calibrate nitrogen-containing
functional groups of nitrosamine, nitriles, and amide compounds. Overall,
49 organics (72% of 68 compounds) from calibration and 7 compounds
(87.5% of 8 compounds) from prediction were within the error goal.
This paper uses the monthly China Economic Policy Uncertainty Index as a proxy variable for China's economic policy uncertainty and the SSE Index volatility as a measure of stock market volatility to study the impact of economic policy uncertainty on stock market volatility. Data from January 2007 to October 2021 are studied, in addition to inertia factors, time breakpoints, macro factors and stock market factors respectively to facilitate the impact on stock market volatility. The findings of this paper expand the research boundary and provide a reference for ideas for macro policy formulation and capital market investment.
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