Reverse Osmosis Desalination Plant Research Articles

Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

AbstractThe reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.

Performance prediction model for desalination plants using modified grey wolf optimizer based artificial neural network approach

Desalination represents an effective method for alleviating water scarcity, applying algorithmic techniques to predict the performance of reverse osmosis (RO) desalination plants, Modified Grey Wolf Optimizer (MGWO) based Artificial Neural Networks (ANN) can predict the performance of membrane distillation (MD) equipment. Four experimental inputs are selected: feed salt concentration(35-140g/h), feed flow rate(400-600L/h), evaporator inlet temperature (60-80℃), and condenser inlet temperature (20-30℃). The permeate flux (L/h m2) is selected as the experimental output. Ten prediction models were proposed and compared with the existing models (ANN, WOA-ANN, and GWO-ANN). The results showed that the MGWO-ANN model-5 showed the best regression results: R2=99.3%, mean square error (MSE)=0.004. This model outperformed the existing ANN model (R2=98.8%, MSE=0.060), WOA-ANN model (R2=99.1%, MSE=0.005) and GWO-ANN model (R2=98.9%, MSE=0.007). Model-5 has a single hidden layer (H=1), 13 hidden nodes (n=13), 10 search agents (SA=10), and 75%-20%-05% dataset division. Its residual error is within acceptable limits (spanning -0.1 to 0.2). Optimizing the number of hidden layer nodes (n) and the number of search agents (SA) can improve the training efficiency and prediction accuracy of the model, and the MGWO-ANN model is capable of more accurately predicting the performance of desalination plants.

Integration study of a reverse osmosis desalination plant in a hydroelectric pumping station

The archipelagos of Macaronesia (Canary Islands, Azores, Madeira and Cape Verde) have isolated energy systems (IES). This makes the island systems dependent on themselves for energy production. In the case of an island like Gran Canaria, energy production is mainly obtained from: a) Wind and solar energy, 19% of the total energy produced, b) Energy obtained from the burning of fossil fuels in the energy production equipment of the existing power plants. Thermal power plants, 81% of the total energy produced. It is necessary to find a solution to the current production system, which is already partially obsolete and must be renewed and/or dismantled, in order to avoid "Zero Energy", which implies a change in the production cycle. In addition, the incorporation of the "Chira-Soria" pumped-storage hydroelectric power plant into the Gran Canaria electricity system represents another, even more important, change in the dynamics followed up to now. Basically, this plant, hydraulically stabilized by means of a seawater desalination plant, incorporates energy storage by storing water at high altitude to be turbined under the right conditions. The new situation with this incorporation will be analysed and the option of integrated operation in the overall energy system of Gran Canaria will be assessed.

Non-target screening and prioritization of organic contaminants in seawater desalination and their ecological risk assessment

The impact of desalination brine on the marine environment is a global concern. Regarding this, salinity is generally accepted as the major environmental factor in desalination concentrate. However, recent studies have shown that the influence of organic contaminants in brine cannot be ignored. Therefore, a non-targeted screening method based on comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC × GC-qMS) was developed for identifying organic contaminants in the desalination brine. A total of 404 compounds were tentatively identified from four seawater desalination plants (three reverse osmosis plants and one multiple effect distillation plant) in China. The identified compounds were prioritized based on their persistence, bioaccumulation, ecotoxicity, usage, and detection frequency. Twenty-one (21) compounds (seven phthalates, ten pesticides, four trihalomethanes) were then selected for further quantitative analysis and ecological risk assessment, including compounds from the priority list along with substances from the same chemical classes. Ecologically risky substances in brine include diisobutylphthalate and bis(2-Ethylhexyl) phthalate, atrazine and acetochlor, and bromoform. Most of the contaminants come from raw seawater, and no high risk contaminants introduced by the desalination process have been found except for disinfection by-products. In brine discharge management, people believed that all pollution in raw seawater was concentrated by desalination process. This study shows that not all pollutants are concentrated during the desalination process. In this study, the total concentration of pesticide in the brine increased by 58.42%. The concentration of ∑PAEs decreased by 13.65% in reverse osmosis desalination plants and increased by 10.96% in the multi-effect distillation plant. The concentration of trihalomethane increased significantly in the desalination concentrate. The change in the concentration of pollutants in the desalination concentrate was related to the pretreatment method and the chemical characteristics of the contaminants. The method and results given in this study hinted a new idea to identify and control the environmental impact factors of brine.

Effects of brine discharges on newly hatched larvae of the rocky-shore keystone gastropod Concholepas concholepas

In response to drought challenges in Chile, desalination plants are becoming crucial. Consequently, the implementation of more osmosis reverse desalination plants in central-northern Chile has been promoted, but the consequences of brine discharges on local organisms are largely unknown. The Chilean abalone (Concholepas concholepas) represent the most economically important marine resource exploited along the Chilean coast. In this study, newly hatched larvae of C. concholepas were used to investigate the effects of brine discharges of a reverse osmosis desalination plant on coastal organisms. Under laboratory conditions, larvae were exposed to various dilutions of a brine to assess the consequences on lethal (survival) and sublethal (swimming performance and metabolism) traits. The results indicated that brine discharges had no effects on larval survival. However, undiluted brines (ca. 56 PSU) and salinities above 38 PSU led to increased metabolic rates and a reduction in the swimming performance of the larvae. We suggest mitigating the negative effects of brines on newly hatched C. concholepas larvae by situating discharge points in highly hydrodynamic zones or as far as possible from mating and larval hatching areas. Additionally, we recommended using diffusers to enhance mixing and dilute brine to sub-lethal levels (below 48 PSU).

Cost Studies of Reverse Osmosis Desalination Plants in the Range of 23,000–33,000 m3/day

The analysis of energy consumption in reverse osmosis desalination plants is the most important and relevant factor to study, because this parameter indicates the level of efficiency and competitiveness of the plant. The direct consequence of the high specific energy consumption (SEC) of a desalination plant in the production of water is one of the main obstacles to the exponential expansion of this technology worldwide. The methodological procedure used to carry out the work is based on the analysis of energy consumption, maintenance costs, staff, membranes, and reagents of three desalination plants with a production of more than 23,000 m3/day located in the Canary Islands (Spain); all data are obtained from real analyses collected “in situ” from 2015 to 2018. One of the main objectives of the current research on desalination plants is to reduce the SEC of seawater desalination plants (SWRO), incorporating energy recovery systems (ERS) and high efficiency pumps (HEP), and to implement different operational configurations with the aim of minimizing the energy requirements necessary to obtain a good product quality at minimum production cost.

Integrated stratigraphical, hydrogeochemical, and geophysical studies to characterize the shallow groundwater aquifers in Wadi Araba area, western Gulf of Suez, Egypt

The construction of the New Galala City besides great mining (e.g., konoozmisr) and Seawater Reverse Osmosis Desalination Plant projects in the semiarid Gulf of Suez region of Egypt increases the demand for groundwater resources. So, the current research provides integrated stratigraphical, hydrogeochemical, and geophysical studies to investigate the shallow groundwater aquifers in Wadi Araba area on the western side of the Gulf of Suez. The stratigraphic column starts with pre-Cambrian basement rocks which are non-conformably overlain by sedimentary succession of Phanerozoic Erathem. A detailed land magnetic profile has been conducted and processed to estimate the aquifer’s geometrical parameters. This includes the subsurface spatial extension and thickness to delineate the prevailed subsurface structures in the study area. Lithostratigraphically, these aquifers are represented by the Paleozoic- lower Cretaceous siliciclastic-dominated sediments of the Nubia Group and fractured carbonate-dominated facies of the upper Cretaceous Galala Formation. Lithological characters, sedimentary facies (fluvial to shallow and open marine), microfacies types, and diagenesis processes (e.g., compaction, cementation, dissolution, and fracturing) are the main factors that affect the quality of the shallow aquifers in the study area. The main microfacies of the studied aquifers are quartz arenite, subarkose, sublitharenite, mudstone/wackestone, and packstone/grainstone. These facies are characterized by different primary and secondary porosities. According to the water sampling standard methods, nineteen groundwater samples were collected from the available hand-dug wells and springs from the Nubian sandstone aquifer and the Upper Cretaceous aquifer, respectively. Inductively coupled plasma–mass spectrometry (ICP–MS), titration, spectrophotometer and a flame photometer were used to obtain a complete chemical analysis. The results of hydrochemical analyses indicated that total dissolved solids (TDS) and concentrations of major ions as well in the lower Cretaceous aquifer increase toward the Gulf of Suez and in general higher than that of springs of the Upper Cretaceous Aquifer. The predominant factors influencing groundwater chemistry include seawater intrusion, evaporation, mineral alteration, dissolution of fossils, and ion exchange. Groundwater in the study area is unsuitable for drinking except in one sample and moderately suitable for irrigation with caution for higher salinity in samples located near the Gulf of Suez.

Developing a framework for sustainability assessment of Reverse Osmosis desalination plants in Gaza Strip

Developing a framework for sustainability assessment of Reverse Osmosis desalination plants in Gaza Strip

Techno-Economic Analysis of Reverse Osmosis Desalination Plant Powered by Hybridized PV and CSP Systems for Irrigation Water

Large market opportunities exist for solar powered Reverse Osmosis (RO) desalination technologies in fertile but arid areas with large solar and sea water resources. A challenge to realizing these markets is the variable nature of solar resources, which for the desalination plant can lead to high water costs due to low capacity factors (CF) and increased maintenance costs due to repeated start-ups and shut-downs. A potential solution is to power RO plants using both PV and CSP with Thermal Energy Storage (TES) with an aim to reduce shut-downs, and increase CF. In this study, three solar energy systems to power RO are considered: 1) PV only; 2) CSP with central receiver (CR) and TES; 3) PV and CSP with CR and TES. Two RO operational strategies are considered: 1) nominal load only; 2) variable load between minimal and nominal. The performance of these systems is simulated for Mersin, Turkey, using TMY data. The PV and CSP with TES system and variable RO operation achieved the levelized cost of water (LCOW) 1.92 USD m-3 with an RO CF of 60.8%.

A New Disruptive Technology for Zero-Brine Discharge: Towards a Paradigm Shift

The desalination of aquifers and seawaters is a viable choice to meet primarily domestic and industrial global water requirements. It removes salts from seawater to obtain freshwater with sufficient quality for different purposes, as well as a highly salt-concentrated waste stream known as brine. This residue is usually returned to the ocean, provoking, among other impacts, changes in temperature, salinity and oxygen and overall local aquatic ecosystem stress, as well as social rejection. Desalination in inland aquifers is more complicated because brine disposal is complicated or impossible. The current study presents a new zero-brine discharge technology able to achieve ecological liquid purification through distillation for the separation of the dissolved solids as crystallized salts (Adiabatic Sonic Evaporation and Crystallization, ASE&C). This new technology was used with seawater and three types of brine to test how it would work when coupled with reverse osmosis desalination plants. Analysis of the byproducts after treatment of the seawater and the different brines are presented here. A basic economic approach to calculating potential revenues is also presented. The results of the analyses revealed a complete depuration of water as distilled water, and crystallized solids with highly concentrated commercial salts (with different composition depending on their origin). The estimated economic value of annual revenue (taking into account only seven element recoveries and treatment of a volume of 1000 m3/d) for three types of brines ranged between 1 and 11 million euros, compared to between 3.6 and 9.3 million euros when ASE&C is employed with seawater. The treatment of greater volumes for seawater desalination would increase these numbers significantly. ASE&C supposes a solution coupled (or not) to desalination plants to reduce the ecological impacts associated with brine discharges to zero, obtaining two significant commercial byproducts: (seawater: freshwater and commercial elements Br, Ca, Cs, Cl, NaOH, Mg, N, K, Rb, Na, Sr, Li, U, B, Sr, Ga, etc.; aquifers: a larger list than for saltwater, depending on the nature of the water body). It can solve environmental issues associated with brine discharge, with null CO2 emissions (renewable energy) and profitable (i.e., with no costly pretreatment) technology.

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

This study examines the relationship between input conditions and the prediction of boron rejection in full-scale seawater reverse osmosis (SWRO) desalination plants using ensemble-based machine learning. While reverse osmosis is the dominant desalination technology, limited research has focused on analyzing plant performance under actual operating conditions. To address this gap, we developed and implemented machine learning algorithms to forecast boron permeability coefficient values, which are indicative of boron rejection concentrations in the permeate. Our analysis utilizes data from a SWRO desalination plant in southeast Spain, examining various input variables and their influence on the prediction of these parameters. The results demonstrate that our ensemble-based machine learning approach can predict boron permeability coefficient values with a reasonable margin of error of 1 mgL−1, as evidenced by mean average error (MAE) and mean absolute percentage error (MAPE) values of 7.93·10–8 and 11.8 %, respectively. In conclusion, an innovative application of artificial intelligence algorithms in the field of water purification under real operational conditions has been introduced, thus introducing valuable insights into the use of machine learning algorithms for forecasting boron rejection concentrations in full-scale SWRO desalination plants. The findings lay the foundation for future researches exploring automated and deep-learning methods in water purification.

Performance analysis and techno-economic assessment of a developed cooling/preheating small PVT-RO desalination plant

Middle East and North Africa (MENA) countries are experiencing rapid population growth, so water and electricity consumption plays a crucial role in the sustainable development of these countries. To overcome the water scarcity and electricity problems facing the MENA region, the developed cooling/preheating small PVT-RO desalination plants have been proposed as a practical solution. To achieve sustainable water and energy development in the MENA region, this study presents a commendable and highly efficient renewable energy project for freshwater production and electricity generation to solve the energy crisis and water scarcity in the MENA countries. Therefore, this study aims to develop a cooling/preheating small PVT-RO desalination plant to facilitate freshwater supply to remote regions and produce electricity. This was done by connecting photovoltaic/thermal (PVT) collectors with reverse osmosis (RO) desalination systems, where seawater is used as a medium to cool photovoltaic cells to increase electric power generation and at the same time recover thermal energy and use it in the initial heating of feed seawater before it is fed into the RO plants, thus increasing its productivity. The results indicate that using the photovoltaic thermal panels as preheating units will lead to a 0.135 kWh/m3 reduction in the rate of specific electricity consumption for the RO desalination plant, as well as increase the electricity generation from PVT panels by a rate of 8%. The economic feasibility presented that the proposed developed cooling/preheating small PVT-RO desalination plant represents an effective technology that reduced the freshwater cost by a rate of 49.5%.

Comparison of the cumulative exergy demand of phase change and reverse osmosis desalination plants with environmental impacts approach

In the scope of the current study, an investigation into the cumulative exergy demand index was conducted for freshwater production across multiple distillation technologies. These technologies encompassed Waterfall Cascading Distillation with Linear Parabolic Solar Concentrator (WCDLPC), Distillation with Linear Parabolic Concentrator and Vacuum Pump (DLPCVP), Distillation with Stand-Alone Point-Focus Parabolic Solar Concentrator (DSPPC), Reverse Osmosis with Non-renewable Energy (RONRE), and Reverse Osmosis with Solar Renewable Energy (ROSRE).The functional unit was established as the production of one ton of freshwater using various desalination technologies. The Outcomes of the analysis pertaining to the cumulative exergy demand in diverse saline water desalination technologies revealed notable findings. Specifically, in the Reverse Osmosis Desalination System with the Conventional System as well as in other desalination technologies (ROSR, WCDLP, DLPCVP and DSPPC), the electricity factor and the photovoltaic panel factor respectively exhibited the most substantial contributions to the cumulative exergy demand. Remarkably, it was observed that the RONRE technology exhibited the highest cumulative exergy demand, reaching 107.04 MJ, while the ROSRE technology demonstrated the lowest demand at 5.98 MJ. Furthermore, the integration of renewable solar energy in lieu of non-renewable electricity within the reverse osmosis technology exhibited a remarkable reduction in cumulative exergy demand, achieving a reduction of 17.90 times. Similarly, the substitution of the waterfall cascade system with a vacuum system in the WCDLPC technology resulted in a 2.69 times reduction in cumulative exergy demand.

Sustainable solution for water crisis in Indian coastal district: A multi-criteria approach to evaluating solar PV powered reverse osmosis desalination

Visakhapatnam, poised to become the executive capital of Andhra Pradesh, is confronting escalating population growth and water scarcity challenges. With over 19 lakh residents and an 11.96% growth rate, the city’s water demand is expected to outstrip its current supply capacity. The Greater Visakhapatnam Municipal Corporation relies on diverse water sources, but encroachments on water bodies and industrial pollution exacerbate the scarcity issue. Against this backdrop, desalination emerges as a sustainable solution, leveraging the city’s extensive coastline. This study explores the technical, economic, environmental, social, and political dimensions of implementing a solar-powered Reverse Osmosis (RO) desalination plant in Visakhapatnam. While various desalination methods exist, solar-powered RO is highlighted for its ecological sustainability, utilizing abundant and renewable solar energy. The study employs the Multi-Criteria Decision-making Approach (MCDA) to comprehensively evaluate the proposed solution. The application of fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) aids in identifying the optimal solution under uncertain conditions. Furthermore, a graph theory and matrix approach are introduced to analyze the decision-making problem. The attribute digraph visually represents the relationships between different criteria, while the permanent matrix quantifies the relative importance of these criteria. The study location selection process involves linear normalization and a permanent matrix computation, ultimately ranking Chinna Rushikonda as the top choice for implementing a solar photovoltaic-powered RO desalination plant. The integration of MCDA, fuzzy TOPSIS, and graph theory provides a comprehensive and robust methodology for decision-making in complex projects, ensuring sustainable and well-informed choices.

Integrated Pumped Hydro Reverse Osmosis System optimization featuring surrogate model development in Reverse Osmosis modeling

The pressure head generated by the upper reservoir of a pumped hydro energy storage system can be sufficient for creating the pressure gradient required for a reverse osmosis desalination plant. Combined with the fact that many drought-stricken coastal areas have nearby mountains at the necessary elevation for these upper (mountaintop) reservoirs, a symbiotic relationship can be ascertained through the co-location of a pumped storage hydropower (PSH) system with a reverse osmosis (RO) desalination system. Merging PSH and RO into one Integrated Pumped Hydro Reverse Osmosis System (IPHROS) instead of implementing each individually could result in a number of benefits, including reduced capital investment, lower maintenance costs, and a natural mechanism for diluting the highly saline brine discharge generated from the RO process. This paper extends the work of Slocum et al. in 2016, who first introduced the concept of IPHROS, by optimizing the amount of seawater sent to and diverted from the upper reservoir for maximal energy recapture and freshwater production, respectively, while also seeking to maximize the RO system recovery ratio. For this multiobjective optimization, a new reverse osmosis model is created that utilizes a blend of empirical and fundamental equations based on the solution–diffusion model of membrane transport and boundary layer effects that naturally occur along reverse osmosis membranes. Additionally, surrogate models are developed to predict the permeate flowrate and fractional salt rejection rate for a Seamaxx™-440 RO element. Optimizing the presented IPHROS model reveals a 16% decrease in the break even time for IPHROS compared to PSH and RO being implemented individually, and that at the best design with regards to the energy, freshwater, and RO system recovery objectives, 79.5 million kWh of energy and 5.79 million cubic meters of fresh water can be delivered to a population, significant amounts for a population seeking to transition to a renewable energy-based grid and alleviate dire freshwater conditions. Enhanced modeling and optimization, as was initiated in this paper, will eventually aid in IPHROS’ large-scale adoption into energy and freshwater infrastructures.

Optimization of sustainable seawater desalination: Modeling renewable energy integration and energy storage concepts

Many arid and semi-arid regions of the world face growing freshwater scarcity, requiring increased utilization of seawater desalination to augment the existing freshwater resources. Seawater reverse osmosis (RO) is currently one of the most deployed technologies, due to its electrical energy efficiency and comparatively low costs. However, the required energy for desalination processes is often still converted from fossil sources. Given the growing demand for seawater desalination and the energy sector’s parallel decarbonization, the substitution by renewable energy sources (RES) is a critical issue. The volatility, lower availability of renewable energy, and cost for required electrical energy storage (EES) are all obstacles to the decarbonization of large-scale desalination plants and may affect production volume and water production costs negatively.This paper presents a mathematical model to determine a cost-optimal energy conversion and EES mix for the design and operation of large-scale seawater RO desalination plants. The linear program ensures satisfaction of the plant’s energetic demands. Furthermore, the model allows to investigate the general feasibility of integrating RES into large-scale desalination processes, which are examined by means of a case study while employing location-specific weather data. Different types of energy conversion technologies and the effect of EES on operating behavior, greenhouse gas emissions, and water unit costs are investigated. Specifically, the indirect storage of electrical energy via the smart management of a desalination plant’s oversized RO membranes is examined and compared to battery energy storage (BES).Results indicate that RES cannot meet an RO desalination plant’s full and constant electrical load demand without a high BES capacity, which is probably not feasible from a technical and economic point of view. The economic penalty from energy storage costs on one unit of desalinated water ranges from 3.10 $/m3 up to 5.38 $/m3. However, operating an oversized RO desalination plant intermittently could reduce the energy-related costs substantially and potentially provide a more appropriate way of adapting to a volatile supply of energy from renewable sources in order to decarbonize the desalination processes. Specific emissions of desalinated water can be reduced from 1.5 kg CO2 eq./m3 when relying mostly on combined cycle gas turbines down to 0.124 kg CO2 eq./m3 by intermittent operation and fully relying on renewable energy sources.

The Role of Cogeneration in the Electrification Pathways towards Decarbonization

The global call for an environmentally friendly, sustainable, and reliable energy system looks for the optimal integration of different technologies to allow a smooth and economically viable transition towards electrification. In this context, small, medium, and large industrial processes are relevant contributors to global CO2 emissions production due to the simultaneous requirement of electricity, heating, and cooling power generally obtained through fossil fuel combustion. In this context, Combined Heat and Power Energy converters based on internal combustion engines, such as reciprocating engines, gas turbines, and gas turbine combined cycles, and external combustion, such as backpressure and condensing steam power plants, are the most suitable solutions for the efficient and reliable generation of the above-mentioned assets. Typically, the industrial demand for heat and electricity differs in terms of heat-to-power ratio when compared to the heat-to-power ratio of the CHP plant, and this has led to requiring the selection of a control strategy to follow, partially or fully, the heat load or the electric load. In this paper, the authors propose an operating and design strategy addressed to fully covering the heat load demands by the heat generated by the CHP, allowing the system to have an excess of electricity generated. This electricity can be used for different purposes, as regards the novel electrification roadmap. Indeed, the authors have explored four configurations in which the excess of the CHP-generated electricity can be exported to the national grid, used for high-tension fast-charging electromobility systems, for running reverse osmosis desalination plants, and for the production of alternative fuels such as hydrogen. The authors propose a methodology for providing an extensive environmental techno-economic assessment that looks at 2050 CO2 targets. Accordingly, the environmental techno-economic assessment results are presented and discussed by considering the Net Present Value, payback period, and CO2 emission savings.

Impact of SWMM Fouling and Position on the Performance of SWRO Systems in Operating Conditions of Minimum SEC.

Due to water stress in the world in general desalination technologies are becoming increasingly important. Among the available technologies, reverse osmosis (RO) is the most widespread due to its reliability and efficiency compared to other technologies. The main weakness of RO is the loss of performance due to membrane fouling, which usually affects the water permeability coefficient (A), causing it to decrease. In RO desalination plants, fouling does not affect all spiral wound membrane modules (SWMMs) in the pressure vessels (PVs) in the same way. This will depend on the type of fouling and the position of the SWMM inside the PV. In this study, the impact of A and the position of the SWMM on the performance of the RO system is analyzed. For this purpose, decrements of up to 50% have been assumed for the seven SWMMs in series considering nine commercial SWMM models. The operating point analyzed is that which minimizes the specific energy consumption (SEC), a point obtained in a previous work carried out by the authors. The results show how the impact of A on the SWMM in the first position is more significant than the impact on modules that are in another position for the nine SWRO models studied. A drop of 50% in the coefficient A of the first element produces a permeate loss in the pressure pipe between 0.67 and 1.35 m3 d-1. Furthermore, it was observed that the models with the lowest coefficient A exhibited the highest performance losses in terms of permeate production when A was decreased.

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

Brackish water/seawater reverse osmosis (RO) desalination is a key solution to global freshwater scarcity. However, the water recovery (Rw) in conventional RO desalination plants is limited by the operating pressure (∆P). As a novel staged RO technology, low-salt-rejection RO (LSRRO) can potentially enhance the Rw in the desalination plants. In this study, we perform process modeling and techno-economic analysis to evaluate the viability of upgrading current RO desalination plants with LSRRO. Based on the results from process modeling, in typical brackish water RO (BWRO) and seawater RO (SWRO) desalination plants, adding an LSRRO stage can effectively enhance the Rw by 21 and 53 %, respectively, and the energy efficiencies are not compromised with the enhanced Rw. From techno-economic analysis, upgrading RO desalination plant with LSRRO is highly viable. Specifically, after adding an LSRRO stage in a BWRO plant, the Rw can be enhanced from 71 to 81 % without notably increasing the levelized cost of water (LCOW), and adding an LSRRO stage in the SWRO plant with zero brine discharge can reduce the LCOW by 30 %. The findings in our study highlight the vast potential of adopting LSRRO in brackish water/seawater desalination plants, and shed lights on the design of future desalination plants.

Forecasting of Permeate Conductivity using MLR and ANN Methods of Boujdour Seawater Reverse Osmosis Desalination Plant

Background: For many years, seawater desalination technique has been operational to deal with water scarcity. In Boujdour region, located near the Atlantic Ocean southwest of Morocco, most water drinking is produced by a reverse osmosis seawater desalination plant. The permeate conductivity prediction is used to evaluate the performance of desalination plants. Objective: The present paper focuses on the modeling and comparison of the Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) for the prediction of permeate conductivity for a one-year period. Methods: Six input variables are considered, including turbidity, temperature, pH, feed conductivity, feed flow, and transmembrane pressure (TMP). Firstly, the MLR identifies the most important variables influencing the permeate conductivity with the aim of developing a regression model for the dependent and independent parameters. Secondly, the ANN method is examined to analyze the performance of desalination plant. A study of the effect of the number of neurons and the number of hidden layers on the efficiency of the neural network has been made. Results and Conclusion: Results confirm that the MLR and ANN models forecast the permeate conductivity with a suitable coordination coefficient of the real and predicted values. ANN model has been successfully tested for reliability with a correlation coefficient R2 of 99.097% and a mean square error (MSE) of 0.002607.