Al Dur SWRO plant: a double challenge for pre- and post-treatments

Al Dur SWRO plant: a double challenge for pre- and post-treatments

Seawater pretreatment by continuous sand filter for seawater RO (reverse osmosis) desalination plant

Chapter 2 - Water Desalination by (Nonsolar) Renewable Energy-Powered RO Systems

Ion-exchange membrane electrodialytic salt production using brine discharged from a reverse osmosis seawater desalination plant

Waste energy recovery in seawater reverse osmosis desalination plants. Part 1: Review

Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filter-reverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.

ANN based-model for estimating the boron permeability coefficient as boric acid in SWRO desalination plants using ensemble-based machine learning

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.

This paper studies energy consumption management of seawater Reverse Osmosis (RO) desalination plants to maintain and enhance the Voltage Stability (VS) of Power Systems (PS) with Photovoltaic (PV) plant integration. We proposed a voltage-based management algorithm to determine the maximum power consumption for RO plants. The algorithm uses power flow study to determine the RO plant power consumption allowed within the voltage-permissible limits, considering the RO process constraints in order to maintain the desired fresh water supply. Three cases were studied for the proposed RO plant: typical operation with constant power consumption, controlled operation using ON/OFF scheduling of the High-Pressure Pumps (HPPs) and controlled operation using Variable Frequency Drive (VFD) control. A modified IEEE 30-bus system with a variable load was used as a case study with integration of three PV plants of 75 MWp total power capacity. The adopted 33.33 MW RO plant has a maximum capacity of 200,000 m3/day of fresh water production. The results reveal that while typical operation of RO plants can lead to voltage violation, applying the proposed load management algorithm can maintain the vs. of the PS. The total transmission power loss and power lines loading were also reduced. However, the study shows that applying VFD control is better than using ON/OFF control because the latter involves frequent starting up/shutting down the RO trains, which consequently requires flushing and cleaning procedures. Moreover, the specific energy consumption (SEC) and RO plant recover ratio decreases proportionally to the VFD output. Furthermore, the power consumption of the RO plant was optimized using the PSO technique to avoid unnecessary restriction of RO plant operation and water shortage likelihood.

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

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

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

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

Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant

Exergo-economic analysis of a seawater reverse osmosis desalination plant with various retrofit options