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Water is an essential and indispensable component is the pulp- and paper production industry.The increase in energy costs, stricter environmental regulations and water resource shortageshave caused a reduction of the water footprint in the industry as well as an increase in waterrecycling and water circuit closure. Reducing water usage requires an understanding of wherecontaminants originate, as well as which streams are critical to the process and how they impactmill operation. The recirculation of water can cause contaminant accumulation; therefore millsemploy technologies for water treatment in the internal water cycles, the so-called ‘kidneys’.Application of membrane technology is one such option which can improve the recycled waterquality and reduce contaminant buildup.The present study was carried out on a lab-scale for the treatment of a tissue mill effluent usingmembrane separation. A combination of pretreatment methods and various membranes werecompared with regards to separation, flux and fouling. The AlfaLaval M20 device was to treatwastewater samples sent from the mill, where the permeate was recirculated to the feed tank.COD and TOC levels are compared with regards to determining the separation efficiency. Thepermeate flux was measured over the two-hour filtration period, as well as flux recovery todetermine fouling levels. Additionally, some economic aspects of the process are discussed.This study suggests the potential application of a combination of flocculation or centrifugationpretreatment, with reverse osmosis membranes for recycling water to replace freshwater intake.The results also indicate the possibility of using ultrafiltration as kidneys to decreasecontamination buildup for further water loop closure.

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.

The research reported herein describes the study activities performed by University of Central Florida (UCF) on behalf of the Town of Jupiter Water Utilities (Town). The Town recently changed its water treatment operations from a combination of reverse osmosis (RO), lime softening (LS) and anion-exchange (IX) to a combination of RO, IX and nanofiltration (NF). Although this treatment change provided enhanced water to the surrounding community in terms of better contaminant removal and reduced DBP formation potential, integration of the NF process altered finished water quality parameters including pH, alkalinity and hardness. There was concern that these changes could result in secondary impacts related to accelerated corrosion of distribution system components and subsequent regulatory compliance. In addition, replacement of the LS process altered the in-plant blending operations by creating an unstable intermediate blend composed of RO and IX waters. There were concerns that this intermediate blend was affecting the integrity of in-plant hydraulic conveyance components. UCF developed a corrosion monitoring study to assess the potential impacts related to internal corrosion, water quality and regulatory compliance after integrating NF into the existing water supply. The intended purpose was to further highlight the complexities of corrosion, describe a unique approach to corrosion monitoring as well as offer various recommendations for corrosion control in a system that relies on a blended water supply. Research was conducted in three phases to address the in-plant and distribution system corrosion issues separately and identify appropriate corrosion control treatment alternatives. The three test phases included: a baseline conditions assessment to iv compare corrosion of the intermediate RO-IX blend with the finished water blend (ROIX-NF); an in-plant corrosion control evaluation; and a distribution system corrosion control evaluation. A test apparatus was constructed and operated at the Town’s facilities to monitor corrosion activity of mild steel, copper and lead solder metal components. The test apparatus consisted of looped PVC pipe segments housed with electrochemical probes and metal coupons to monitor corrosion rates of the metallic components. Electrochemical probes containing metal electrodes were used to obtain instantaneous corrosion rates by means of the Linear Polarization Resistance (LPR) technique while the metal coupons were gravimetrically evaluated for weight loss. The electrochemical probes permitted daily monitoring of each metal’s corrosion rates while metal coupons were analyzed at the conclusion of testing and used for comparison. Different test waters flowed through the corrosion rack according to each test phase and relative corrosion rates were compared to evaluate corrosion control techniques. Study findings indicated that the intermediate blend was more corrosive, in general, then the final blend; however, research also indicated that the final blend of water was increasing lead and copper concentrations within the distribution system. An orthophosphate corrosion inhibitor was evaluated for in-plant corrosion control. The inhibitor’s performance was assessed by comparing mild steel corrosion rates with and without the chemical. In addition, secondary impacts related to introduction of the chemical were evaluated by pre-corroding the metallic components prior to the introduction of the inhibitor. Results indicated that the inhibitor marginally decreased corrosion rates and increased the turbidity of the water supply. Based on these v observations, it was concluded that the inhibitor was not a viable solution for in-plant corrosion control. To resolve in-plant corrosion issues, recommendations were made for modification of in-plant blending operations to eliminate the corrosive intermediate blend from the process allowing the RO, IX and NF treated waters to be blended in a common location. The effectiveness of a poly/ortho blended phosphate chemical inhibitor was evaluated for reducing lead and copper corrosion to resolve distribution corrosion issues. A 50/50 poly/ortho blend was selected because of its analogous use in similar municipal water facilities. Metallic corrosion rates, particularly lead and copper, were compared with and without the inhibitor to assess the performance of the chemical. Like the previous test phase, the metallic components were pre-corroded prior to the chemical’s introduction to determine if secondary impacts could result from its presence. Results indicated that lead and copper corrosion rates were lower in the presence of the inhibitor, and secondary impacts related to increased turbidity were not observed for this chemical. Based on these results, it was recommended that a poly/ortho blended phosphate be used to decrease lead and copper corrosion within the Town’s distribution system.

The research presented in this dissertation provides the results of a comprehensive assessment of the water treatment requirements for the City of Sarasota. The City’s drinking water supply originates from two sources: (1) brackish groundwater from the Downtown well field, and (2) Floridan surficial groundwater from the City’s Verna well field. At the time the study was initiated, the City treated the brackish water supply using a reverse osmosis process that relied on sulfuric acid for pH adjustment as a pretreatment method. The Verna supply was aerated at the well field before transfer to the City’s water treatment facility, either for softening using an ion exchange process, or for final blending before supply. For the first phase of the study to evaluate whether the City can operate its brackish groundwater RO process without acid pretreatment, a three-step approach was undertaken that involved: (1) pilot testing the plan to reduce the dependence on acid, (2) implementing the plan on the fullscale system with conservative pH increments, and (3) continuous screening for scale formation potential by means of a “canary” monitoring device. Implementation of the study was successful and the annual savings in operating expenditure to the City is projected to be about $120,000. From the acid elimination study, using the relationship between electrical conductivity in water and total dissolved solids in water samples tested, a dynamic approach to evaluate the performance of the reverse osmosis plant was developed. This trending approach uses the mass transfer coefficient principles of the Homogeneous Solution Diffusion Model. Empirical models iv were also developed to predict mass transfer coefficients for solutes in terms of total dissolved solids and sodium. In the second phase of the study, the use of nanofiltration technology to treat aerated Verna well field water was investigated. The goal was to replace the City’s existing ion exchange process for the removal of hardness and total dissolved solids. Different pretreatment options were evaluated for the nanofiltration pilot to remove colloidal sulfur formed during pre-aeration of the groundwater. Sandfilters and ultrafiltration technology were evaluated as pretreatment. The sandfilter was inadequate as a pre-screen to the nanofiltration pilot. The ultrafiltration pilot (with and without a sandfilter as a pre-screen) proved to be an adequate pretreatment to remove particulates and colloids, especially the sulfur colloids in the surficial groundwater source. The nanofiltration pilot, was shown to be an efficient softening process for the Verna well field water, but it was impacted by biofoulants like algae. The algae growth was downstream of the ultrafiltration process, and so chlorination was used in the feed stream of the ultrafiltration process with dechlorination in the nanofiltration feed stream using excess bisulfite to achieve stable operations. Non-phosphonate based scale inhibitors were also used to reduce the availability of nutrients for biofilm growth on the nanofiltration membranes. The combined ultrafiltration-nanofiltration option for treatment of the highly fouling Verna water samples is feasible with chlorination (to control biofouling) and subsequent dechlorination. Alternatively, the study has shown that the City can also more economically and more reliably use ultrafiltration technology to filter all water from its Verna well field and use its current ion exchange process for removal of excess hardness in the water that it supplies

Reverse osmosis (RO) is widely used for water reuse and desalination. Although RO membranes are known for their high salt rejection and practical permeate flux, their performance can be impaired by fouling, and their removal of some disinfection byproducts and their precursors (e.g., bromide, N-Nitrosodimethylamine [NDMA]) does not meet drinking water standards. RO membrane modifications have been widely studied to overcome these limitations. In this research, RO membranes were grafted with cationic polymers to induce a positive charge on the RO membrane surface. This modification aimed at enhancing the rejection of negatively charged bromide ions by removing them from solution by binding them to the membrane surface. The results showed that the modified (positively charged) RO membranes achieved lower rejection (82% rejection) for bromide ions compared to the unmodified ones (94.5% rejection). This behavior was likely a result of increased concentration polarization of the bromide ions at the membrane surface and/or increase in porosity of the modified membranes. Calculations based on the film theory indicate that the concentration of bromide ions at the surface of the modified membrane was 1371 ppm compared to 1307 ppm at the surface of the unmodified membrane. Evidently, the polymer attraction energy was not sufficient to keep the bromide ions attached to the membrane surface and prevent their diffusion across the membrane. Although the goal of the modification in the current study (i.e., enhancing removal of bromide ions) was not met, the permeate flux of the modified membrane was improved compared to the unmodified one. The literature suggests that increasing flux after modification is likely a result of increase in membrane pore size and hydrophilicity. In addition to the experimental work conducted in this study, a multi-criteria decision analysis was performed to prioritize research on surface modifications of reverse osmosis membranes. It was found that surface modifications have been mainly focused on reducing membrane fouling and to a much lower extent on removal of disinfection byproducts and their precursors. The RO membrane modification alternatives for fouling reduction and N-Nitrosodimethylamine (NDMA) removal were ranked based on multiple criteria using the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This multi-criteria decision analysis process resulted in the identification of the top five promising modifications to reduce fouling and improve NDMA rejection. Grafting and coating the RO membranes with complex polymeric salts were the highest ranked modification approaches to reduce fouling. Heat-treatment of RO membranes achieved the highest NDMA rejection (98%); however, this technique was the second highest ranked modification approach for NDMA removal because it scored lower for other evaluation criteria.

Increasing water reuse opportunities for communities has become increasingly important as access to clean water is becoming more scarce. Reverse Osmosis (RO) is an advanced treatment technology used in water recycling wastewater for potable reuse applications. RO is a promising technology; however, the membranes have limitations including their high energy demand and their susceptibility to membrane fouling. The main objective of this study was to develop a reproducible method for the fabrication of RO membranes with enhanced flux and reduced susceptibility to fouling. Literature contains numerous publications on fabrication of thin film composite (TFC) RO membranes with high performance. However, the reports lacked all the details needed to fabricate a TFC RO membrane, making it difficult to replicate those published fabrication protocols. Based on the efforts of this study, the membrane fabrication procedures utilized did not yield the same quality and performance as reported in these articles. In this study, five TFC RO control membranes were replicated and compared. The membranes produced an average water flux of 21.9 ± 3.6 L/m2h (LMH) and an average salt rejection of 97.6% ± 2.0%. Based on these results, it was concluded that a reproducible fabrication technique was developed for fabricating consistent and reliable TFC RO membranes. Furthermore, this study investigated the role of fouling on TFC RO membrane performance. Enhancing membrane resistance to fouling helps maintain membrane selectivity, lifespan, and permeability. There has been an increasing interest in the modification of the RO membranes for enhanced hydrophilicity, which leads to improvements in fouling resistance. In this study, a positive and high charge density polymer, polyethylenimine (PEI), was introduced into the membrane matrix in varying layers of the membrane structure. PEI-1 was fabricated in-situ by grafting the PEI onto the polysulfone (PSf) support, while PEI-2 was fabricated via grafting of the PEI onto the membrane PA surface. The resulting membranes were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), Atomic Force Microscopy (AFM), and Goniometry. PEI-2 produced a more hydrophilic membrane when compared to PEI-1, however, PEI-1 performed better in terms of flux and selectivity. Multiple model foulants were used for investigating the modified membrane fouling performance. These model foulants were tested at varying concentrations, pH values, and with and without the presence of Ca2+ ions. The model foulants used were bovine serum albumin (BSA), sodium alginate, and humic acid. None of the model foulants resulted in a decrease in performance for the membrane over the duration of the tests (up to 13 hours). Future research is needed to develop a robust protocol for testing the fouling of the produced RO membranes within a reasonable timeframe.

Yes / Reverse Osmosis (RO) has already proved its worth as an efficient treatment method in chemical and environmental engineering applications. Various successful RO attempts for the rejection of organic and highly toxic pollutants from wastewater can be found in the literature over the last decade. Dimethylphenol is classified as a high-toxic organic compound found ubiquitously in wastewater. It poses a real threat to humans and the environment even at low concentration. In this paper, a model based framework was developed for the simulation and optimisation of RO process for the removal of dimethylphenol from wastewater. We incorporated our earlier developed and validated process model into the Species Conserving Genetic Algorithm (SCGA) based optimisation framework to optimise the design and operational parameters of the process. To provide a deeper insight of the process to the readers, the influences of membrane design parameters on dimethylphenol rejection, water recovery rate and the level of specific energy consumption of the process for two different sets of operating conditions are presented first which were achieved via simulation. The membrane parameters taken into consideration include membrane length, width and feed channel height. Finally, a multi-objective function is presented to optimise the membrane design parameters, dimethylphenol rejection and required energy consumption. Simulation results affirmed insignificant and significant impacts of membrane length and width on dimethylphenol rejection and specific energy consumption, respectively. However, these performance indicators are negatively influenced due to increasing the feed channel height. On the other hand, optimisation results generated an optimum removal of dimethylphenol at reduced specific energy consumption for a wide sets of inlet conditions. More importantly, the dimethylphenol rejection increased by around 2.51% to 98.72% compared to ordinary RO module measurements with a saving of around 20.6% of specific energy consumption.

Yes / Reverse Osmosis (RO) can be considered as one of the most widely used technologies used to abate the existence of highly toxic compounds from wastewater. In this paper, a multilayer artificial neural network (MLANN) with Genetic Algorithm (GA) have been considered to build a comprehensive mathematical model, which can be used to predict the performance of an individual RO process in term of chlorophenol removal from wastewater. The MLANN model has been validated against 70 observational experimental datasets collected from the open literature. The MLANN model predictions have outperformed the predictions of several structures developed for the same chlorophenol removal using RO process based on performance in terms of coefficient of correlation, coefficient determination (R2) and average error (AVE). In this respect, two structures (4-2-2-1) and (4-8-8-1) were also used to study the effect of a number of neurons in the hidden layers based on the difference between the measured and ANN predicted values. The model responses clearly confirm the successfulness of estimating the chlorophenol rejection for network structure 4-8-8-1 based on a wide range of the control variables. This also represents a high consistency between the ANN model predictions and the experimental data.

This dissertation investigates potential applications for Machine Learning (ML) and real-time fouling monitors in Reverse Osmosis (RO) desalination. The main objective was to develop a framework that minimizes the cost of membrane fouling by deploying AI-generated cleaning patterns and real-time fouling monitoring. Membrane manufacturers and researchers typically recommend cleaning (standard operating procedure – SOP) when normalized permeate flow, a performance metric tracking the decline of permeate flow/output from its initial baseline with respect to operating pressure, reaches 0.85-0.90 of baseline values. This study used estimates of production cost, internal profitability metrics, and permeate volume output to evaluate and compare the impact of time selection for cleaning intervention. The cleanings initiated when the normalized permeate flow reached 0.85 represented the control for cleaning intervention times. In deciding optimal times for cleaning intervention, a Deep Reinforcement Learning (RL) agent was trained to signal cleaning between 0.85-0.90 normalized with a cost-based reward system. A laboratory-scale RO flat membrane desalination system platform was developed as a model plant, and data from the platform and used to train the model and examine both simulated and actual control of when to trigger membrane cleaning, replacing the control operator's 0.85 cleaning threshold. Compared to SOP, the intelligent operator showed consistent savings in production costs at the expense of total permeate volume output. The simulated operation using the RL initiated yielded 9% less permeate water but reduced the cost per unit volume ($/m3) by 12.3%. When the RL agent was used to initiate cleaning on the laboratory-scale RO desalination system platform, the system produced 21% less permeate water but reduced production cost ($/m3) by 16.0%. These results are consistent with an RL agent that prioritizes production cost savings over product volume output. / Doctor of Philosophy / The decreasing supply of freshwater sources has made desalination technology an attractive solution. Desalination—or the removal of salt from water—provides an opportunity to produce more freshwater by treating saline sources and recycled water. One prominent form of desalination is Reverse Osmosis (RO), an energy intensive process in which freshwater is forced from a pressurized feed through a semipermeable membrane. A significant limiting cost factor for RO desalination is the maintenance and replacement of semipermeable RO membranes. Over time, unwanted particles accumulate on the membrane surface in a process known as membrane fouling. Significant levels of fouling can drive up costs, negatively affect product quality (permeate water), and decrease the useful lifetime of the membrane. As a result, operators employ various fouling control techniques, such as membrane cleaning, to mitigate its effects on production and minimize damage to the membrane. This dissertation investigates potential applications for Machine Learning (ML) and real-time fouling monitors in Reverse Osmosis (RO) desalination. The main objective was to develop a framework that minimizes the cost of membrane fouling by deploying AI-generated cleaning patterns and real-time fouling monitoring. Membrane manufacturers and researchers typically recommend cleaning (standard operating procedure – SOP) when normalized permeate flow, a performance metric tracking the decline of permeate flow/output from its initial baseline with respect to operating pressure, reaches 0.85-0.90 of baseline values. This study used estimates of production cost, internal profitability metrics, and permeate volume output to evaluate and compare the impact of time selection for cleaning intervention. The cleanings initiated when the normalized permeate flow reached 0.85 represented the control for cleaning intervention times. In deciding optimal times for cleaning intervention, a Deep Reinforcement Learning (RL) agent was trained to signal cleaning between 0.85-0.90 normalized with a cost-based reward system. A laboratory-scale RO flat membrane desalination system platform was developed as a model plant, and data from the platform and used to train the model and examine both simulated and actual control of when to trigger membrane cleaning, replacing the control operator's 0.85 cleaning threshold. Compared to SOP, the intelligent operator showed consistent savings in production costs at the expense of total permeate volume output. The simulated operation using the RL initiated yielded 9% less permeate water but reduced the cost per unit volume ($/m3) by 12.3%. When the RL agent was used to initiate cleaning on the laboratory-scale RO desalination system platform, the system produced 21% less permeate water but reduced production cost ($/m3) by 16.0%. These results are consistent with an RL agent that prioritizes production cost savings over product volume output.