Stand-Alone Direct Current Power Network Based on Photovoltaics and Lithium-Ion Batteries for Reverse Osmosis Desalination Plant

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

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

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.

Seawater desalination is a vital source of drinking water, especially in coastal and remote areas. However, its sustainability is constrained by the high energy requirement. The need for fresh water supplies continues to rise due to its intensive use in many development sectors, such as agriculture and industry, as well as the continued increase in population. This has led to the idea of using nuclear power in seawater desalination to reduce the stress on the main electrical grid and enhance sustainable. The paper's goal is to optimize a reverse osmosis (RO) desalination plant to produce 100,000 m3 of fresh water daily. The best membrane is selected by testing 10 FilmTec membranes, with a focus on achieving optimal product quality (TDS) while maintaining an acceptable level of specific energy consumption (SEC). The study aims to address the challenge of delivering potable water by designing and modeling a standalone desalination plant powered by small modular reactors (SMRs). According to ROSA's analysis, the optimal RO desalination unit consists of two stages with a total of 175 membranes. The FilmTec SW30XHR-400 is identified as the best option based on superior water quality. This membrane has a specific energy consumption of 5.17 kWh/m3 and a low TDS of 141.4 mg/L. The total power consumption of the RO plant is approximately 21.5 MW; therefore, the KAREM-25 MWe reactor has been selected to be coupled with the RO desalination plant.

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

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.

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


 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.

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

Exergy analysis of a reverse osmosis desalination plant in California

Chapter 30 - Simulation tools for membrane scaling in reverse osmosis desalination plants

This report has been prepared as part of an effort to design and build a Modeling and Simulation (M&S) framework to assess the economic viability of a Nuclear-Renewable Hybrid Energy System (N-R HES). In order to facilitate dynamic M&S of such an integrated system, research groups in multiple national laboratories and universities have been developing various subsystems as dynamic physics-based components using the Modelica programming language. In Fiscal Years (FYs) 2015, Idaho National Laboratory (INL) performed a dynamic analysis of two region-specific N-R HES configurations, including the gas-to-liquid (natural gas to Fischer-Tropsch synthetic fuel) and brackish water Reverse Osmosis (RO) desalination plants as industrial processes. In FYs 2016–2017, INL developed two additional subsystems in the Modelica framework: (1) a high-temperature steam electrolysis plant as a high priority industrial plant to be integrated with a light water reactor within an N-R HES and (2) a gas turbine power plant as a secondary energy supply. In FY 2018, the RO desalination system model developed in FY 2015 has been updated such that the model is compatible with the most recent version of the ThermoPower library. Special attention has been given to the controller settings based on process models, aiming to improve process dynamics and controllability. A dynamic performance analysis of the updated RO desalination plant was carried out to evaluate the technical feasibility (load-following capability) of such a system operating under highly variable conditions requiring flexible output. Simulation results involving several case studies show that the suggested control scheme could maintain the controlled variables (including the variable electrical load and RO feed pressure) within desired limits under various plant operating conditions. The results also indicate that the proposed RO plant could provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess plant capacity within an N-R HES. For a small-scale energy storage system, a sensible Thermal Energy Storage (TES) model has been developed in the Modelica Framework in FY 2018.

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.

In alignment with the global issue of increasing clean water demand while aiming at achieving sustainability and enhancing energy efficiency. The implementation of Artificial Neural Networks (ANN) in Reverse Osmosis (RO) desalination plants has exhibited a substantial success. This study introduces a new approach that implements Deep Neural Network (DNN) with multi-output, employing Adam optimizer, to predict two critical parameters for desalination plants: output water quality in terms of Total Dissolved Solids (TDS) and the required membrane pressure during the plant’s operation. As these two predicted targets are the main reference parameters when evaluating the production quality and energy consumption for RO desalination plants. The consideration of multiple membrane configurations and feed flow rates, which are relatively unexplored features in ANN research on desalination, is the key innovation of this study. The multi-output DNN achieved an R2 score of 0.99, signifying a high level of prediction accuracy of the model, which was additionally validated by using experimental data to endorse the proposed model. The integration of the DNN model in RO desalination plants will enhance output water quality and operational efficiency, which will be reflected in reduced energy consumption. The model’s capability to adapt to multiple plants’ parameters and membrane configurations will lead to resource optimization, reduce environmental impacts, and boost profitability, while consistently producing high-quality water. This will benefit the industry through providing advanced water treatment solutions. This innovation underscores the potential of AI-driven solutions in revolutionizing desalination technologies and fostering sustainable water management practice.