The increasing drinking water demand in many countries leads to an increase in the use of desalination plants, which are considered a great solution for water treatment processes. Reverse osmosis (RO) and electro-dialysis (ED) systems are the most popular membrane processes used to desalinate water at high salinity. Both systems work by separating the ionic contaminates and disposing of them as a brine solution, but ED uses electrical current as a driving force while RO uses osmotic pressure. A direct comparison of reverse osmosis and electro-dialysis systems is needed to highlight process development similarities and variances. This work aims to provide an overview of previous studies on reverse osmosis and electro-dialysis systems related to membrane module and design processes; energy consumption; cost analysis; operational problems; efficiency of saline removal; and environmental impacts of brine disposal. RO system uses osmotic pressure as a driving force to force water through the membrane with less energy than other desalination systems. The enhancements in membrane materials and power recovery of the unit have massively decreased the price of RO units. ED system uses an electrical current to push dissolved ions across ion exchange membranes. The results of this review showed that desalination plants must be integrated with renewable energy to reduce power consumption and costs related to energy. Various technologies, including treatment processes and disposal methods, must be used to control concentrated solutions resulting from desalination processes because 5 to 33% of the total cost of the desalination process is associated with brine disposal.

The water supply network inside the building is of high importance due to direct contact with the user that must be optimally designed to meet the water needs of users.  This work aims to review previous research and scientific theories that deal with the design of water networks inside buildings, from calculating the amount of consumption and the optimal distribution of the network, as well as ways to rationalize the use of water by the consumer.  The process of pumping domestic water starts from water treatment plants to be fed to the public distribution networks, then reaching a distribution network inside the building till it is  provided to the user.  The design of the water supply network inside the building is mainly affected by the amount of water consumed in the building. On this basis, the pipes' dimensions and the water tank's volume are determined. The operating pressure of the water supply network inside the building is calculated to overcome the height difference and the friction inside the pipes and provide sufficient pressure to operate the most remote fixture.  The most important results of the research are that the optimal use of the water distribution network inside the buildings is achieved by the correct design and implementation using skilled labor, materials, and devices of high quality and rationalization of water consumption by the user.

There is a great operational risk to control the day-to-day management in water treatment plants, so water companies are looking for solutions to predict how the treatment processes may be improved due to the increased pressure to remain competitive. This study focused on the mathematical modeling of water treatment processes with the primary motivation to provide tools that can be used to predict the performance of the treatment to enable better control of uncertainty and risk. This research included choosing the most important variables affecting quality standards using the correlation test. According to this test, it was found that the important parameters of raw water: Total Hardness, Calcium, Magnesium, Total Solids, Nitrite, Nitrates, Ammonia, and Silica are to be used to construct the specific model, while pH, Fluoride, Aluminium, Nitrite, Nitrate, Ammonia, Silica, and Orthophosphate of the treated water were eliminated from the analysis. For modeling the coagulation and flocculation process temperature, Alkalinity and pH of raw water were the depended variables of the model. As for the modeling process turbidity of the treated water was used as the output variable. In general, the linear models including model-driven type, (Multivariate multiple regression, MMR and Multiple linear regression, MLR) have slightly higher prediction efficiencies than the, data-driven type (artificial neural network, ANNM). The coefficients of determination (R2) reached 66 to 85% for the MMR and MLR models and 65 to 81% for the ANN models.