Dissertations / Theses on the topic 'Reverse osmosis networks'

The optimization of a reverse osmosis (RO) system includes optimization of the design of the individual membrane modules, the system structure and the operating conditions of the system. Most previous studies considered either the optimal design of individual modules only or optimization of system structure and operating conditions for fixed module dimensions. This thesis developed a method to simultaneously optimize the module dimensions, system structure and operating conditions. The method comprised rules for generating a general superstructure for an RO system given the number of modules along with rules for generating technically and mathematically feasible sub-structures. The superstructure was based on maximum connectivity between unit operations. A connectivity matrix was used to represent the superstructure. The matrix was useful for checking sub-structure's feasibility and deriving a model for the sub-structure's optimization, comprising the minimum number of variables and constraints which minimized computational time and increased accuracy. For optimization, a nonlinear objective function of the annualized profit of the RO system was formulated, consisting of the revenue obtained from permeate sales, capital costs of the unit operations and operating costs for the system. It was found that RO system optimization is a nonconvex optimization problem. The most effective optimization procedure involved a combination of evolutionary computation, which was good for locating the global optimum, and a gradient-based method, which was superior in finding the exact optimum. Small population size, adaptive mutation rate and steady state replacement were the most efficient parameter settings for the evolutionary computation. Optimal design of two-stage RO systems with and without energy recovery, bypass and recycle streams was studied. Dimensions of predicted optimal modules approached those of current commercial modules but with much shorter feed channels. The mathematical optimum also had higher operating pressures. The optimum system structure was a series arrangement with different module dimensions in each stage. A sensitivity analysis showed that trends in the optimal design were similar when unit costs changed. An investigation of the scalability of the method for a three-stage RO system revealed several weaknesses. These are probably surmountable with the addition of more RO system specific knowledge.

Una de las aplicaciones más importante de los procesos de filtración por membrana es en el área de tratamiento de agua por ultrafiltración, nanofiltración u ósmosis inversa. Entre los problemas más serios encontrados en estos procesos destaca la aparición de los fenómenos de ensuciamiento y envejecimiento de las membranas que limitan la eficacia de la operación tanto en la separación de los solutos, como en el flujo de permeado, afectando también el ciclo de vida de las membranas.
Para reducir el coste de la producción y mejorar la robustez y eficacia de estos procesos es imprescindible disponer de modelos capaces de representar y predecir la eficiencia y el comportamiento de las membranas durante la operación. Una alternativa viable a los modelos teóricos, que presentan varias particularidades que dificultan su postulado, la constituyen los modelos basados en el análisis de los datos experimentales, entre cuales destaca el uso de las redes neuronales. Dos metodologías han sido evaluadas e investigadas, una constando en la caracterización de las interacciones entre las membranas y los compuestos orgánicos presentes en el agua de alimentación, y la segunda basada en el modelado de la dinámica de operación de las plantas de desalinización por ósmosis inversa.
Relaciones cuantitativas estructura‐propiedad se han derivado usando redes neuronales de tipo back‐propagation, para establecer correlaciones entre los descriptores moleculares de 50 compuestos orgánicos de preocupación para la salud pública y su comportamiento frente a 5 membranas comerciales de ósmosis inversa, en términos de permeación, absorción y rechazo. Para reducir la dimensión del espacio de entrada, y para evitar el uso de la información redundante en el entrenamiento de los modelos, se han usado tres métodos para seleccionar el menor número de los descriptores moleculares relevantes entre un total de 45 que caracterizan cada molécula. Los modelos obtenidos se han validado utilizando un método basado en el balance de materia, aplicado no solo a los 50 compuestos utilizados para el desarrollo de los modelos, sino que también a un conjunto de 143 compuestos orgánicos nuevos. La calidad de los modelos obtenidos es prometedora para la extensión de la presente metodología para disponer de una herramienta comprensiva para entender, determinar y evaluar el comportamiento de los solutos orgánicos en el proceso de ósmosis inversa. Esto serviría también para el diseño de nuevas y más eficaces membranas que se usan en este tipo de procesos.
En la segunda parte, se ha desarrollado una metodología para modelar la dinámica de los procesos de ósmosis inversa, usando redes neuronales de tipo backpropagation y Fuzzy ARTMAP y datos experimentales que proceden de una planta de desalinización de agua salobre Los modelos desarrollados son capaces de evaluar los efectos de los parámetros de proceso, la calidad del agua de alimentación y la aparición de los fenómenos de ensuciamiento sobre la dinámica de operación de las plantas de desalinización por osmosis inversa. Se ha demostrado que estos modelos se pueden usar para predecir el funcionamiento del proceso a corto tiempo, permitiendo de esta manera la identificación de posibles problemas de operación debidas a los fenómenos de ensuciamiento y envejecimiento de las membranas. Los resultados obtenidos son prometedores para el desarrollo de estrategias de optimización, monitorización y control de plantas de desalinización de agua salobre. Asimismo, pueden constituir la base del diseño de sistemas de supervisón capaces de predecir y advertir etapas de operación incorrecta del proceso por fallos en el mismo, y actuar en consecuencia para evitar estos inconvenientes.
One of the more serious problems encountered in reverse osmosis (RO) water treatment processes is the occurrence of membrane fouling, which limits both operation efficiency (separation performances, water permeate flux, salt rejection) and membrane life‐time. The development of general deterministic models for studying and predicting the development of fouling in full‐scale reverse osmosis plants is burden due to the complexity and temporal variability of feed composition, diurnal variations, inability to realistically quantify the real‐time variability of feed fouling propensity, lack of understanding of both membrane‐foulants interactions and of the interplay of various fouling mechanisms. A viable alternative to the theoretical approaches is constituted by models developed based on direct analysis of experimental data for predicting process operation performance. In this regard, the use of artificial neural networks (ANN) seems to be a reliable option. Two approaches were considered; one based on characterizing the organic compounds passage through RO membranes, and a second one based on modeling the dynamics of permeate flow and separation performances for a full‐scale RO desalination plant.
Organic solute sorption, permeation and rejection by RO membranes from aqueous solutions were studied via artificial neural network based quantitative structure‐property relationships (QSPR) for a set of 50 organic compounds for polyamide and cellulose acetate membranes. The separation performance for the organic molecules was modeled based on available experimental data achieved by radioactivity measurements to determine the solute quantity in feed, permeate and sorbed by the membrane. Solute rejection was determined from a mass balance on the permeated solution volume. ANN based QSPR models were developed for the measured organic sorbed (M) and permeated (P) fractions with the most appropriate set of molecular descriptors and membrane properties selected using three different feature selection methods. Principal component analysis and self‐organizing maps pre‐screening of all 50 organic compounds defined by 45 considered chemical descriptors were used to identify the models applicability domain and chemical similarities between the organic molecules. The ANN‐based QSPRs were validated by means of a mass balance test applied not only to the 50 organic compounds used to develop the models, but also to a set of 143 new compounds. The quality of the QSPR/NN models developed suggests that there is merit in extending the present compound database and extending the present approach to develop a comprehensive tool for assessing organic solute behavior in RO water treatment processes. This would allow also the design and manufacture of new and more performing membranes used in such processes.
The dynamics of permeate flow rate and salt passage for a RO brackish water desalination pilot plant were captured by ANN based models. The effects of operating parameters, feed water quality and fouling occurrence over the time evolution of the process performance were successfully modeled by a back‐propagation neural network. In an alternative approach, the prediction of process performance parameters based on previous values was achieved using a Fuzzy ARTMAP analysis. The neural network models built are able to capture changes in RO process performance and can successfully be used for interpolation, as well as for extrapolation prediction, fact that can allow reasonable short time forecasting of the process time evolution. It was shown that using real‐time measurements for various process and feed water quality variables, it is possible to build neural network models that allow better understanding of the onset of fouling. This is very encouraging for further development of optimization and control strategies. The present methodology can be the basis of development of soft sensors able to anticipate process upsets.

An accurate model for RO process has significant importance in the simulation and optimization proposes. A steady state model of RO process is developed based on solution diffusion theory to describe the permeation through membrane and thin film approach is used to describe the concentration polarization. The model is validated against the operation data reported in the literature. For the sake of clear understanding of the interaction of feed temperature and salinity on the design and operation of RO based desalination systems, simultaneous optimization of design and operation of RO network is investigated based on two-stage RO superstructure via MINLP approach. Different cases with several feed concentrations and seasonal variation of seawater temperature are presented. Also, the possibility of flexible scheduling in terms of the number of membrane modules required in operation in high and low temperature seasons is investigated A simultaneous modelling and optimization method for RO system including boron removal is then presented. A superstructure of the RO network is developed based on double pass RO network (two-stage seawater pass and one-stage brackish water pass). The MINLP problem based on the superstructure is used to find out an optimal RO network which will minimize the total annualized cost while fulfilling a given boron content limit. The effect of pH on boron rejection is investigated at deferent seawater temperatures. The optimal operation policy of RO system is then studied in this work considering variations in freshwater demand and with changing seawater temperature throughout the day. A storage tank is added to the RO layout to provide additional operational flexibility and to ensure the availability of freshwater at all times. Two optimization problems are solved incorporating two seawater temperature profiles, representing summer and winter seasons. The possibility of flexible scheduling of cleaning and maintenance of membrane modules is investigated. Then, the optimal design and operation of RO process is studied in the presence of membrane fouling and including several operational variations such as variable seawater temperature. The cleaning schedule of single stage RO process is formulated as MINLP problem using spiral wound modules. NNs based correlation has been developed based on the actual fouling data which can be used for estimating the permeability decline factors. The correlation based on actual data to predict the annual seawater temperature profile is also incorporated in the model. The proposed optimization procedure identified simultaneously the optimal maintenance schedule of RO network including its design parameters and operating policy. The steady state model of RO process is used to study the sensitivity of different operating and design parameters on the plant performance. A non-linear optimization problem is formulated to minimize specific energy consumption at fixed product flow rate and quality while optimizing the design and operating parameters. Then the MINLP formulation is used to find the optimal designs of RO layout for brackish water desalination. A variable fouling profile along the membrane stages is introduced to see how the network design and operation of the RO system are to be adjusted Finally, a preliminary control strategy for RO process is developed based on PID control algorithm and a first order transfer function (presented in the Appendix).

With the increase in population and the scarcity of fresh water in the Middle East desalination has taken an important role in the provision of water for everyday use and for industrial purposes. Reverse osmosis water treatment process is of particular interest as it is one of the key processes in a desalination plant. The modelling of this process and the prediction of permeate flow is useful in better understanding the process. In the present study, an artificial neural network based model was developed based on plant data for the prediction of permeate flow performance. Plant data was collected and a number of variables determined. Principal component analysis was then carried and factor loadings obtained to identify the main variables. Once the main input variables were obtained a statistical analysis of the data was done in order to remove outliers present in the data. This was done because the presence of outliers in data to be analysed using ANN models renders the models ineffective in prediction of an output. Once the removal of outliers was done, the data was then analysed using the developed model. 1081 sets of data were originally used with twelve input variables. After principal component analysis was done the input variables were reduced to five with one output variable. With the removal of outliers 981 sets of data were obtained and these were then used in the model. The model was able to predict the output accurately with r2 at 0.97. Key factors determined from the process were that to obtain an optimum network one has to consider the epoch size, the transfer function, the learning rate and finally the number of nodes in the hidden layers. The number of hidden layers also had an effect on the overall prediction of the data. It is also important when using ANN models to obtain the correct input variables and to remove any outliers that are present in the data in order to be able to predict the output. The use of plant data severely limited optimisation of the process due to it already being heavily optimised.

This dissertation introduces desalination processes in general and multistage flash (MSF) and reverse osmosis (RO) in particular. It presents the fundamental and practical aspects of neural networks and provides an overview of their structures, topology, strengths, and limitations. This study includes the neural network applications to prediction problems of large-scale commercial MSF and RO desalination plants in conjunction with statistical techniques to identify the major independent variables to optimize the process performance. In contrast to several recent studies, this work utilizes actual operating data (not simulated) from a large-scale commercial MSF desalination plant (48 million gallonsper day capacity, MGPD) and RO plant (15 MGPD) located in Kuwait and the Kingdom of Saudi Arabia, respectively. We apply Neural Works Professional II/Plus (NeuralWare, 1993) and SAS (SAS Institute Inc., 1996) software to accomplish this task. This dissertation demonstrates how to apply modular and equation-solving approaches for steady-state and dynamic simulations of large-scale commercial MSF desalination plants using ASPEN PLUS (Advanced System for Process Engineering PLUS) and SPEEDUP (Simulation Program for Evaluation and Evolutionary Design of Unsteady Processes) marketed by Aspen Technology, Cambridge, MA. This work illustrates the development of an optimal operating envelope for achieving a stable operation of a commercial MSF desalination plant using the SPEEDUP model. We then discuss model linearization around nominal operating conditions and arrive at pairing schemes for manipulated and controlled variables by interaction analysis. Finally, this dissertation describes our experience in applying a commercial software, DynaPLUS, for combined steady-state and dynamic simulations of a commercial MSF desalination plant. This dissertation is unique and significant in that it reports the first comprehensive study of predictive modeling, simulation, and optimization of large-scale commercial desalination plants. It is the first detailed and comparative study of commercial desalination plants using both artificial intelligence and computer-aided design techniques. The resulting models are able to reproduce accurately the actual operating data and to predict the optimal operating conditions of commercial desalination plants.
Ph. D.

This work introduces a systematic seawater reverse osmosis (SWRO) membrane network synthesis approach, based on the coordinated use of process superstructure representations and global optimization. The approach makes use of superstructure formulations that are capable of extracting a globally optimal design as a performance target, by taking into consideration desired process conditions and constraints that are typically associated with reverse osmosis systems. Thermodynamic insights are employed to develop lean network representations so that any underperforming solutions can be eliminated a priori. This essentially results in considerable improvement of the overall search speed, compared to previously reported attempts. In addition, the approach enables the extraction of structurally different design alternatives. In doing so, distinct membrane network design classes were established by partitioning the search space, based on network size and connectivity. As a result, corresponding lean superstructures were then systematically generated, which capture all structural and operational variants within each design class. The overall purpose is thus to enable the extraction of multiple distinct optimal designs, through global optimization. This mainly helps provide design engineers with a better understanding of the design space and trade-offs between performance and complexity. The approach is illustrated by means of a numerical example, and the results obtained were compared to previously related work. As anticipated, the proposed approach consistently delivered the globally optimal solutions, as well as alternative efficient design candidates attributed to different design classes, with reduced CPU times. This work further capitalizes on the developed representation, by accounting for detailed water quality information, within the SWRO desalination network optimization problem. The superstructures were modified to incorporate models that capture the performance of common membrane elements, as predicted by commercially available simulator tools, e.g. ROSA (Dow) and IMSDesign (Hydranautics). These models allow tracing of individual components throughout the system. Design decisions that are supported by superstructure optimization include network size and connectivity, flow rates, pressures, and post treatment requirements. Moreover, a detailed economic assessment capturing all the significant capital and operating costs associated in SWRO processes, including intake, pre and post treatment has also been accounted for. These modifications were then illustrated using a case study involving four seawater qualities, with salinities ranging from 35 to 45 ppt. The results highlight the dependency of optimal designs on the feed water quality involved, as well as on specified permeate requirements.

no
In this work, a model based MINLP (mixed integer nonlinear programming) optimisation framework is developed for evaluating boron rejection in a reverse osmosis (RO) desalination process. A mathematical model (for the RU process) based on solution diffusion model and thin film theory is incorporated in the optimisation framework. A superstructure of the RU network is developed which includes two passes: (a) seawater pass containing normal two-stage RU system housing seawater membrane modules and (b) the brackish water pass (BW) accommodating brackish water membrane modules. For fixed freshwater demand, the objective of this work is to demonstrate the effectiveness of the MINLP approach for analyzing and optimizing the design and operation of RU network while attaining desired limit on boron concentration in the freshwater produced. The effect of seasonal variation in seawater temperature and pH on boron removal efficiency is also discussed.

Yes
The water permeability constant, (Kw) is one of many important parameters that affect optimal design and operation of RO processes. In model based studies, e.g.within the RO process model, estimation of Kw is therefore important. There are only two available literature correlations for calculating the dynamic Kw values. However, each of them are only applicable for a given membrane type, given feed salinity over a certain operating pressure range. In this work, we develop a time dependent neural network (NN) based correlation to predict Kw in RO desalination processes under fouling conditions. It is found that the NN based correlation can predict the Kw values very closely to those obtained by the existing correlations for the same membrane type, operating pressure range and feed salinity. However, the novel feature of this correlation is that it is able to predict Kw values for any of the two membrane types and for any operating pressure and any feed salinity within a wide range. In addition, for the first time the effect of feed salinity on Kw values at low pressure operation is reported. While developing the correlation, the effect of numbers of hidden layers and neurons in each layer and the transfer functions is also investigated.

Yes
The removal of pollutants such as N-nitrosamine present in drinking and reuse water resources is of significant interest for health and safety professionals. Reverse osmosis (RO) is one of the most promising and efficient methodologies for removing such harmful organic compounds from wastewater. Having said this, the literature confirms that the multi-stage RO process with retentate reprocessing design has not yet achieved an effective removal of N-nitrosodimethylamine-D6 (NDMA) from wastewater. This research emphasizes on this particular challenge and aims to explore several conceptual designs of multi-stage RO processes for NDMA rejection considering model-based techniques and compute the total recovery rate and energy consumption for different configurations of retentate reprocessing techniques. In this research, the permeate reprocessing design methodology is proposed to increase the process efficiency. An extensive simulation analysis is carried out using high NDMA concentration to evaluate the performance of each configuration under similar operational conditions, thus providing a deep insight on the performance of the multi-stage RO permeate reprocessing predictive design. Furthermore, an optimisation analysis is carried out on the final design to optimise the process with a high NDMA rejection performance and the practical recovery rate by manipulating the operating conditions of the plant within specified constraints bounds. The results show a superior removal of NDMA from wastewater.

no
The optimal operation policy of flexible RO systems is studied in this work. The design and operation of RO process is optimized and controlled considering variations in water demands and changing seawater temperature throughout the day. A storage tank is added to the system layout to provide additional operational flexibility and to ensure the availability of freshwater to customer at all times. A steady state model for the RO process is developed and linked with a dynamic model for the storage tank. The membrane modules are divided into a number of groups to add flexibility in operation to RO network. The total operating cost of the RO process is minimized in order to find the optimal layout and operating variables at discreet time intervals for three design scenarios. (C) 2013 Elsevier Ltd. All rights reserved.

Yes
Reverse osmosis (RO) has long been recognised as an efficient separation method for treating and removing harmful pollutants, such as dimethylphenol in wastewater treatment. This research aims to study the effects of RO network configuration of three modules of a wastewater treatment system using a spiral-wound RO membrane for the removal of dimethylphenol from its aqueous solution at different feed concentrations. The methodologies used for this research are based on simulation and optimisation studies carried out using a new simplified model. This takes into account the solution-diffusion model and film theory to express the transport phenomena of both solvent and solute through the membrane and estimate the concentration polarization impact respectively. This model is validated by direct comparison with experimental data derived from the literature and which includes dimethylphenol rejection method performed on a small-scale commercial single spiral-wound RO membrane system at different operating conditions. The new model is finally implemented to identify the optimal module configuration and operating conditions that achieve higher rejection after testing the impact of RO configuration. The optimisation model has been formulated to maximize the rejection parameters under optimal operating conditions of inlet feed flow rate, pressure and temperature for a given set of inlet feed concentration. Also, the optimisation model has been subjected to a number of upper and lower limits of decision variables, which include the inlet pressure, flow rate and temperature. In addition, the model takes into account the pressure loss constraint along the membrane length commensurate with the manufacturer’s specifications. The research clearly shows that the parallel configuration yields optimal dimethylphenol rejection with lower pressure loss.

No
The water permeability constant, (K-w), is one of the many important parameters that affect optimal design and operation of RO processes. In model based studies, e.g. within the RO process model, estimation of W-w is therefore important There are only two available literature correlations for calculating the dynamic K-w values. However, each of them is only applicable for a given membrane type, given feed salinity over a certain operating pressure range. In this work, we develop a time dependent neural network (NN) based correlation to predict K-w in RO desalination processes under fouling conditions. It is found that the NN based correlation can predict the K-w values very closely to those obtained by the existing correlations for the same membrane type, operating pressure range and feed salinity. However, the novel feature of this correlation is that it is able to predict K-w values for any of the two membrane types and for any operating pressure and any feed salinity within a wide range. In addition, for the first time the effect of feed salinity on Kw values at low pressure operation is reported. Whilst developing the correlation, the effect of numbers of hidden layers and neurons in each layer and the transfer functions is also investigated. (C) 2014 Elsevier B.V. All rights reserved.

Yes
Reverse Osmosis (RO) membrane process has been commonly used for clarification and concentration of apple juice processes, due to significant advance in membrane technology, requirements for low energy and cost, and effective retention of aroma components. In this paper, a multi-stage RO industrial full-scale plant based on the MSCB 2521 RE99 spiral-wound membrane module has been used to simulate the process of concentrating apple juice and to identify an optimal multi-stage RO process for a specified apple juice product of high concentration measured in Brix. The optimisation problem is formulated as a Nonlinear Programming (NLP) problem with five different RO superstructures to maximise the apple juice concentration as well as the operating parameters such as feed pressure, flow rate and temperature are optimised. A simple lumped parameter model based on the solution-diffusion model and the contribution of all sugar species (sucrose, glucose, malic acid, fructose and sorbitol) to the osmotic pressure is assumed to represent the process. The study revealed that the multi-stage series RO process can optimise the product concentration of apple juice better than other configurations. It has been concluded that the series configuration of twelve elements of 1.03 m2 area improves the product apple juice concentration by about 142% compared to one element. Furthermore, the feed pressure and flow rate were found to have a significant impact on the concentration of the apple juice.

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2015
The scarcity of water and strict environmental regulations have made sustainable engineering a prime concern in the process and manufacturing industries. Water minimisation involves the reduction of freshwater use and effluent discharge in chemical plants. This is achieved through water reuse, water recycle and water regeneration. Optimisation of the water network (WN) superstructure considers all possible interconnections between water sources, water sinks and regenerator units (membrane systems). In most published works, membrane systems have been represented using the “black-box” approach, which uses a simplified linear model to represent the membrane systems. This approach does not give an accurate representation of the energy consumption and associated costs of the membrane systems. The work presented in this dissertation therefore looks at the incorporation of a detailed reverse osmosis network (RON) superstructure within a water network superstructure in order to simultaneously minimise water, energy, operating and capital costs. The WN consists of water sources, water sinks and reverse osmosis (RO) units for the partial treatment of the contaminated water. An overall mixed-integer nonlinear programming (MINLP) framework is developed, that simultaneously evaluates both water recycle/reuse and regeneration reuse/recycle opportunities. The solution obtained from optimisation provides the optimal connections between various units in the network arrangement, size and number of RO units, booster pumps as well as energy recovery turbines. The work looks at four cases in order to highlight the importance of including a detailed regeneration network within the water network instead of the traditional “black-box’’ model. The importance of using a variable removal ratio in the model is also highlighted by applying the work to a literature case study, which leads to a 28% reduction in freshwater consumption and 80% reduction in wastewater generation.
GR2016

Water crises are predicted to be amongst the risks of highest concern for the next ten years, due to availability, accessibility, quality and management issues. Knowledge of the microbial communities indigenous to drinking water is essential for treatment and distribution process control, risk assessment and infrastructure design. Drinking water distribution systems (DWDSs) ideally should deliver to the consumer water of the same microbial quality as that leaving a treatment plant (“biologically stable” according to WHO). At the start of this Ph.D. program water microbiology comprised conventional culturedependent methods, and no studies were available on microbial communities from source to tap. A method combining 16S rRNA gene pyrosequencing with flow cytometry was developed to accurately detect, characterize, and enumerate the microorganisms found in a water sample. Studies were conducted in seven fullscale Dutch DWDSs which transport low-AOC water without disinfectant residuals, produced from fresh water applying conventional treatment. Full-scale studies were also conducted at the desalination plant and DWDS of KAUST, Saudi Arabia where drinking water is produced from seawater applying RO membrane treatment and then transported with chlorine residual. Furthermore, biological stability was evaluated in a wastewater reuse application in the Netherlands. When low-AOC water was distributed without disinfectant residuals, greater bacterial richness was detected in the networks, however, temporal and spatial variations in the bacterial community were insignificant and a substantial fraction of the microbiome was still shared between the treated and transported water. This shared fraction was lower in the system transporting water with chlorine residual, where the eukaryotic community changed with residence time. The core microbiome was characterized and dominant members varied between the two systems. Biofilm and deposit-associated communities were found to drive tap water microbiology regardless of water source and treatment scheme. Network flushing was found to be a simple method to assess water microbiology. Biological stability was not associated with safety. The biological stability concept needs to be revised and quantified. Further research is needed to understand microbial functions and processes, how water communities affect the human microbiome, and what the “drinking” water microbiome is like in undeveloped countries.
The research presented in this doctoral dissertation was financially supported by and conducted in collaboration with Delft University of Technology (TU Delft) and Evides Waterbedrijf in the Netherlands.