Keywords

ASTM D 45160, Masstransfer, Membrane, NOM, Nanofiltration, Reverse osmosis, Surface characteristics

Abstract

Five articles describing the impact of surface characteristics, and development of mass transfer models for diffusion controlled membrane applications are published in this dissertation. Article 1 (Chapter 3) describes the impact of membrane surface characteristics and NOM on membrane performance for varying pretreatment and membranes during a field study. Surface charge, hydrophobicity and roughness varied significantly among the four membranes used in the study. Membrane surface characteristics, NOM and SUVA measurements were used to describe mass transfer in a low pressure RO integrated membrane system. Inorganic and organic solute and water mass transfer coefficients were systematically investigated for dependence on membrane surface properties and NOM mass loading. Inorganic MTCs were accurately described by a Gaussian distribution curve. Water productivity, NOM rejection and inorganic rejection increased as membrane surface charge and NOM loading increased. Inorganic MTCs were also correlated to surface hydrophobicity and surface roughness. The permeability change of identical membranes was related to NOM loading, hydrophobicity and roughness. Organic fouling as measured by water, organic and inorganic mass transfer was less for membranes with higher hydrophilicity and roughness. Article 2 (Chapter 4) describes the development of a diffusion controlled solute mass transfer model to assess membrane performance over time. The changing mass transfer characteristics of four low-pressure reverse osmosis (LPRO) membranes was correlated to feed stream water quality in a 2000 hour pilot study. Solute mass transfer coefficients (MTCs) were correlated to initial solute MTCs, solute charge, feed water temperature, monochloramine loading and organic loading (UV254). The model can be used to predict cleaning frequency, permeate water quality and sensitivity of permeate water quality to variation of temperature, organic and monochloramine mass loading. Article 3 (Chapter 5) describes a comparison of the long standing method of assessing membrane performance (ASTM D 45160 and another approach using mass transfer coefficients (MTCs) from the homogenous solution diffusion model (HSDM) using a common data set, water productivity and standardized salt passage. Both methods were shown to provide identical assessments of water productivity, however different assessments of salt passage. ASTM D 4516 salt passage is normalized for pressure and concentration and does not show the effects of flux, recovery, temperature or specific foulants on salt passage. However the MTC HSDM method is shown to consider all those effects and can be easily used to predict membrane performance at different sites and times of operation, whereas ASTM D 45160 can not. The HSDM MTC method of membrane evaluation is more versatile for assessment of membrane performance at varying sites and changing operational conditions. Article 4 (Chapter 6) describes the development of a fully integrated membrane mass transfer model that considers concentration, recovery and osmotic pressure for prediction of permeate water quality and required feed stream pressures. Osmotic pressure is incorporated into the model using correction coefficients that are calculated from boundary conditions determined from stream osmotic pressures of the feed and concentrate streams. Comparison to homogenous solution diffusion model (HSDM) with and without consideration of osmotic pressure and verification of IOPM using independently developed data from full and pilot scale plants is presented. The numerical simulation and statistical assessment show that osmotic pressure corrected models are superior to none-osmotic pressure corrected models, and that IOPM improves model predictability. Article 5 (Chapter 7) describes the development and comparison of a modified solution diffusion model and two newly developed artificial neural network models to existing mechanistic or empirical models that predict finished water quality for diffusion controlled membranes, which are generally restricted to specific solute MTCs that are site and stage specific. These models compensate for the effects of system flux, recovery and feed water quality on solute MTC and predict permeate water quality more accurately than existing models.

Notes

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Graduation Date

2004

Semester

Spring

Advisor

Taylor, James S.

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil and Environmental Engineering

Degree Program

Civil and Environmental Engineering

Format

application/pdf

Identifier

CFE0000026

URL

http://purl.fcla.edu/fcla/etd/CFE0000026

Language

English

Release Date

May 2004

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

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