A Rescheduling Approach for a Large-scale Reverse Osmosis Desalination Plant under Uncertain Fresh Water Demand

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

The rescheduling problem of seawater reverse osmosis (SWRO) desalination plant under uncertain freshwater demand is studied. A rescheduling model of this problem is built, which is a bi-objective optimization with criterions of efficiency and stability. A rolling horizon scheduling (RHS) procedure is used to solve this problem. The framework of RHS is structured. A differential evolution (DE) algorithm is used to compute the solution, and its steps are described. The simulation results of a large-scale SWRO desalination plant with fluctuations in freshwater demand show that the performance of RHS is satisfied.

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

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

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

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

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.

The integration of desalination plants and mineral production

Influence of site-specific parameters on environmental impacts of desalination

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

Environmental life cycle assessment of seawater reverse osmosis desalination plant powered by renewable energy

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

Economic feasibility study for MF system as a pretreatment of SWRO in test bed desalination plant

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

Fouling control in SWRO desalination during harmful algal blooms: A historical review and future developments