A critical review of definitions for exergetic efficiency in reverse osmosis desalination plants

A theoretical analysis on upgrading desalination plants with low-salt-rejection reverse osmosis

Capital cost estimation of RO plants: GCC countries versus southern Europe

With a reverse osmosis (RO) desalination plant designed to satisfy only the contracted-for water supply, the water company would be missing out on potential benefits that could have been obtained selling water in periods of high demand. On the other hand, sizing the RO desalination plant to produce water to satisfy peak demand means incurring additional costs as well as having the plant partially idle during periods of average or low demand. A model was developed using Excel macros to perform dynamic programming to optimize the capacity expansion of an RO desalination plant. The objective function is to maximize the present value of the total net benefits over the lifetime of the RO desalination plant. The model can be used to test different scenarios to capture time-variant tourism demand and price uncertainties on investment decisions. This study focuses on tourism dominated arid coastal regions, using Sharm El Sheikh (Sharm) in South Sinai, Egypt, as an example.19 RO plants in Sharm were surveyed and data were collected including unit production costs, O&M costs, energy consumption rates, contracted-for water supply, and utilization. Unit production cost of an RO desalination plant varies according to the degree of operation of the plant. This fact has to be taken into consideration when calculating the costs of RO desalination and when deciding on the plant capacity in order to maximize the total net benefit. Using the collected data, cost functions were developed for O&M costs as a function of utilization and plant capacity. The cost model calculated similar values to the actual total net benefit for one of the surveyed RO plant taken as an example. Using the optimization model, the maximum total net benefit is obtained with a smaller installed capacity than the actual case. A modified pricing structure is suggested in the paper that ties the water selling price to consumption in an effort to reduce demand in excess of contracted-for water supply aiding the water company to fulfill its contractual commitments to all users. However, price elasticity has to be taken into consideration to determine the impact of price change on water demand.

The exergetic efficiency as a performance evaluation tool in reverse osmosis desalination plants in operation

Today, reverse osmosis membranes are the leading technology for new desalination installations, however, a challenge facing widespread application of RO technology is membrane fouling. In the present study, we used an environmentally friendly green inhibitor as anti-scaling and anti-biofouling in reverse osmosis (RO) desalination plants. The influence of Sargassum sp., Corallina mediterranea, and Avicennia marina on RO membrane mineral scaling was evaluated using gypsum as a model scalant. Antibacterial properties for three marine extracts from Sargassum sp., C. mediterranea, and Avicennia marina were investigated with Gram-positive bacteria (ArthrobactersulfureusYACS14, Staphylococcus aureus) and Gram-negative bacteria (VibrioanguillarumMVM425, Escherichia coli). The antimicrobial results were detected for the two selected extracts as the most potent extracts (ethyl acetate, methanol crude extracts of the Avicennia marina leaves). Data showed that ratios of 3 and 5% recorded the highest suppression percentages (100%) for all tested bacteria including bacterial community collected from Eastern Harbor. On the other side, data confirmed that the anti-scalant properties by 100 ppm of Avicennia marina leave extract giving 85% of scale inhibition. The effect of Avicennia marina leaves extract for calcium sulfate dihydrate (gypsum) scaling on selected reverse osmosis (RO) membrane surfaces was investigated. The effect of different concentrations of Avicennia marina leaves extract was observed in the extent of surface scale coverage and surface crystal size among the membrane studied.


 Seawater intake and its treatments are one of the main upstream processes of every seawater desalination plant (RO, ED, MSF, MED). However, the process has turned out to be of utmost importance for reverse osmosis (RO) desalination plant. It is to be sure that sufficient and steady flow and quality of water is available to the RO desalination plant. Prior to RO feed water, the seawater intake pre-treatment process has to be tailored and the quality of seawater intake to be treated either subsurface intake or open surface intakes, particularly when treating open surface intakes seawater (OSIS) with exceedingly unpredictable quality. According to the well-established membrane manufacturer and supplier, the RO membrane warranty and guarantee are depended on seawater intake quality and its pre-treatment. Thus, the current state-of-the-art RO membranes life and performance success for desalination processing depend upon OSIS pre-treatment processing techniques. This article is emphasizing an overview on recent OSIS and its pre-treatment techniques for RO desalination plant.

A feasibility study of a small-scale photovoltaic-powered reverse osmosis desalination plant for potable water and salt production in Madura Island: A techno-economic evaluation

A case study of designing of a reverse osmosis (RO) desalination plant using a Solar Photovoltaic (PV) system is investigated in this work. The RO system is a desalination plant providing pure water to the Shoiaba power generation plant. The system consists of a PV array connected to an inverter for day time or batteries for night time. The PV is designed to meet the high-pressure pumps’ load that is about 13649 kWh a day. Because the plant is operated 24 hours a day the PV panels are divided into two parts, one to cover the day time load and the second to cover night load that is stored in batteries. Based on weather conditions of solar radiation of the shortest day and maximum ambient temperature the PV is sizing and a storage system is determined. The system is modeled by the TNSYS software to simulate the performance of the system during the year. The annual performance of system proves that the system is able to meet the required load during the year. It can be concluded that it is a great opportunity to install photovoltaic panels and increase the efficiency of Reverse Osmosis Desalination Plant.

the adaption of renewable energy (RE) in electric systems (ESs) involves many technical challenges when its share exceeds certain levels. Various strategies are used to manage these challenges. The reverse osmosis (RO) desalination plants can play a significant role in RE integration because of their high energy consumption and flexibility of operation. This paper investigates how RO desalination plants can help in integrating RE resources in ESs. First, the impact of different levels of RE penetration on IEEE 30-bus system was studied in terms of voltage profile. Then, the adaption of an RO plant in the system as a flexible load was introduced to reduce the effects of RE penetration. The consumption of RO plant was decreased during the absence of RE and increased at the midday hours to absorb the surplus power generated by RE. This prevented the ES voltage profile to fall below the permissible limits. The results showed that, the coordination between RO plants and ESs operation had a significant positive role.

Drinking water supply through reverse osmosis desalination plants in Ramanathapuram district of Tamil Nadu, India

Modified PID control with H∞ loop shaping synthesis for RO desalination plants

Modeling, control, and dynamic performance analysis of a reverse osmosis desalination plant integrated within hybrid energy systems

Saline groundwater is the primary water source for agricultural development in the United Arab Emirates (UAE). Many small-scale reverse osmosis (RO) desalination plants have been installed to desalinize saline groundwater for use in irrigating vegetables (mainly in green houses), forages, date palm and fruit trees. Twelve plants in inland areas and three plants in coastal areas were studied to evaluate the existing brine disposal practices. The capacity of ROs varied from 28 to 325 m3 d-1. Pre-treated brackish groundwater, salinity varying from 4 to 37 dS m-1, was used as feed water. Higher groundwater salinity was observed in coastal areas due to sea-water intrusion. Chemical analysis of brine and soils at the disposal sites showed trace existence of heavy metals. The methods of brine disposal include (i) surface disposal (to excavated/non-excavated pits or mountain terrain or steep edge of sand dunes), (ii) well injection or dug well, (iii) pipeline discharge to sea beach, (iv) irrigation of salt-tolerant plants or blending brine with feed water for irrigating date palm, (v) use in cooling pads of green houses, and (vi) discharge to wadi beds. Among the disposal methods, surface disposal and dug well near the RO plants are critical as feed water can be further polluted by brine and chemicals used in the desalination process. These disposal practices could be replaced by environmental friendly methods such as non-leaking evaporation ponds and biosaline agriculture.

Brine disposal from reverse osmosis desalination plants in Oman and the United Arab Emirates

This paper presents a methodology and practical guidelines for developing predictive models for large-scale commercial water desalination plants by (1) a data-based approach using neural networks based on the backpropagation algorithm and (2) a model-based approach using process simulation with advanced software tools ASPEN PLUS and SPEEDUP and compares the relative merits of the two approaches. This study utilizes actual operating data from two of the largest multistage flash (MSF) and reverse osmosis (RO) desalination plants in the world. Our resulting neural network and process simulation models are capable of accurately predicting the actual operating data from commercial MSF desalination plants, but the accuracy of a neural network model depends on both the proper selection of input variables and the broad range of data with which the network is trained. A neural network model can handle noisy data more effectively than statistical regression and performs better in predicting the performance variables of both MSF and RO desalination plants. Our neural network model compares favorably with recent neural network models developed by others in accurately predicting actual operating data from commercial MSF desalination plants. When compared to a data-based neural network, a properly validated model-based process simulation (as in the case of MSF desalination plants) can more effectively quantify the effects of varying operating variables on the desalination performance variables. When it is difficult to develop a model-based process simulation (as in the case of RO desalination plants), we can use a data-based neural network to accurately predict the desalination performance variables.