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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Reverse Osmosis Optimization

This technology evaluation was prepared by Pacific Northwest National Laboratory on behalf of the U.S. Department of Energy’s Federal Energy Management Program (FEMP). The technology evaluation assesses techniques for optimizing reverse osmosis (RO) systems to increase RO system performance and water efficiency. This evaluation provides a general description of RO systems, the influence of RO systems on water use, and key areas where RO systems can be optimized to reduce water and energy consumption. The evaluation is intended to help facility managers at Federal sites understand the basic concepts of the RO process and system optimization options, enabling them to make informed decisions during the system design process for either new projects or recommissioning of existing equipment. This evaluation is focused on commercial-sized RO systems generally treating more than 80 gallons per hour.

osmosis, optimization↗
DOE OSTI2013

Reverse Osmosis Optimization

This technology evaluation was prepared by Pacific Northwest National Laboratory on behalf of the U.S. Department of Energy’s Federal Energy Management Program (FEMP). The technology evaluation assesses techniques for optimizing reverse osmosis (RO) systems to increase RO system performance and water efficiency. This evaluation provides a general description of RO systems, the influence of RO systems on water use, and key areas where RO systems can be optimized to reduce water and energy consumption. The evaluation is intended to help facility managers at Federal sites understand the basic concepts of the RO process and system optimization options, enabling them to make informed decisions during the system design process for either new projects or recommissioning of existing equipment. This evaluation is focused on commercial-sized RO systems generally treating more than 80 gallons per hour.

McMordie Stoughton, Kate L.↗
DOE OSTI2018

Cost Optimization of Osmotically Assisted Reverse Osmosis

We develop a nonlinear optimization model to identify minimum cost designs for osmotically assisted reverse osmosis (OARO), a multistaged membrane-based process for desalinating high-salinity brines. The optimization model enables comprehensive evaluation of a complex process configuration and operational decision space that includes nonlinear process performance and implicit relationships among membrane stages, saline sweep cycles, and makeup, purge, and recycle streams. Here, the objective function minimizes cost, rather than energy or capital expenditures, to accurately account for the trade-offs in capital and operational expenses inherent in multistaged membrane processes. Generally, we find that cost-optimal OARO processes minimize the number of stages, eliminate the use of saline makeup streams, purge from the first sweep cycle, and successively decrease stage membrane area and sweep flow rates. The optimal OARO configuration for treating feed salinities of 50–125 g/L total dissolved solids with water recoveries between 30–70% results in costs less than or equal to $6 per m3 of product water. Sensitivity analysis suggests that future research to minimize OARO costs should focus on minimizing the membrane structural parameter while maximizing the membrane burst pressure and reducing the membrane unit cost.

42 ENGINEERING↗
DOE OSTI2017

Technoeconomic Optimization of Waste Heat Driven Forward Osmosis for Flue Gas Desulfurization Wastewater Treatment

With the Environmental Protection Agency’s recent Effluent Limitation Guidelines for Steam Electric Generators, power plants are having to install and operate new wastewater technologies. Many plants are evaluating desalination technologies as possible compliance options. However, the desalination technologies under review that can reduce wastewater volume or treat to a zero-liquid discharges standard have a significant energy penalty to the plant. Waste heat, available from the exhaust gas or cooling water from coal-fired power plants, offers an opportunity to drive wastewater treatment using thermal desalination technologies. One such technology is forward osmosis (FO). Forward osmosis utilizes an osmotic pressure gradient to passively pull water from a saline or wastewater stream across a semi-permeable membrane and into a more concentrated draw solution. This diluted draw solution is then fed into a distillation column, where the addition of low temperature waste heat can drive the separation to produce a reconcentrated draw solution and treated water for internal plant reuse. The use of low-temperature waste heat decouples water treatment from electricity production and eliminates the link between reducing water pollution and increasing air emissions from auxiliary electricity generation. In order to evaluate the feasibility of waste heat driven FO, we first build a model of an FO system for flue gas desulfurization (FGD) wastewater treatment at coal-fired power plants. This model includes the FO membrane module, the distillation column for draw solution recovery, and waste heat recovery from the exhaust gas. We then add a costing model to account for capital and operating costs of the forward osmosis system. We use this techno-economic model to optimize waste heat driven FO for the treatment of FGD wastewater. We apply this model to three case studies: the National Energy Technology Laboratory (NETL) 550 MW model coal fired power plant without carbon capture and sequestration, the NETL 550 MW model coal fired power plant with carbon capture and sequestration, and Plant Bowen in Eularhee, Georgia. For each case, we identify the design that minimizes the cost of wastewater treatment given the safely recoverable waste heat. We benchmark the cost minimum waste-heat forward osmosis solutions to two conventional options that rely on electricity, reverse osmosis and mechanical vapor recompression. Furthermore, we quantify the environmental damages from the emissions of carbon dioxide and criteria air pollutants for each treatment option. With this information we can assess the trade-offs between treatment costs, energy consumption, and air emissions between the treatment options.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Cost optimization of low-salt-rejection reverse osmosis

Low-salt-rejection reverse osmosis (LSRRO) is an emerging membrane-based desalination technology for concentrating brines with potentially lower energy consumption and cost than thermally driven processes. We assess the technoeconomic performance of LSRRO by using an optimization model that minimizes the levelized cost of water (LCOW). We develop detailed models for the RO module and other equipment using WaterTAP (Water treatment Technoeconomic Assessment Platform), an open-source simulation and optimization software for water treatment. We improve upon previous LSRRO cost optimization studies by optimizing the salt permeability for each stage, which has significant implications for technoeconomic performance. We present the cost-optimal design, operation, and performance for three cases: 35 g/L TDS feed at 70% recovery, 70 g/L at 55%, and 125 g/L at 35%. The cost-optimal LCOW and specific energy consumption (SEC) for these cases are 0.70, 1.89, and 7.41 $/m 3 and 3.9, 8.4, and 30.4 kWh/m 3 , respectively. For each case, we use sensitivity analyses to provide guidance on key factors that impact economic viability. We also present the cost-optimal LCOW and SEC for feed concentrations between 5 and 220 g/L TDS and recoveries between 30 and 90%. So, we compare LSRRO to other high-salinity desalination technologies and find that it may be cost-competitive below feed concentrations of 125 g/L.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Autonomous online optimization of a closed-circuit reverse osmosis system

As freshwater becomes increasingly scarce, many industrial and municipal water utilities look at premise-scale water treatment and reuse to meet water demand. Closed-circuit reverse osmosis (CCRO) has been proposed as a promising process design to do so. This sequencing batch process enables operation at higher brine salinity levels by means of a recycle flow. Optimal operation requires that the maximum salinity level at the membrane surface represents an optimal trade-off between brine disposal costs and energy efficiency. This maximum salinity level may change over time as the feed water composition changes and electricity markets fluctuate. In this article, we present the results of the experimental evaluation of an automatic technique for continuous online optimization, known as extremum seeking control. This technique has a long history in the process control community but has received little traction so far in the water industry. We modify this technique to enable its use for online optimization of CCRO, specifically to account for its sequential batch operation. We challenge the optimization schemes through several experimental tests and illustrate the advantages and drawbacks of extremum-seeking control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Black-box optimization of a closed-circuit reverse osmosis system for desalination through extremum seeking control

We study the minimization of the cost function of a closed-circuit desalination reverse osmosis (CCRO) system, which is used for producing drinking water. The cost function is a trade-off between the specific energy consumption and the specific brine management cost. The extremum-seeking controller (ESC) manipulates the critical electric conductivity of the brine to minimize the cost function. Experiments were designed in which the composition of the feed salinity is increased to test the controller’s ability to converge to a new optimum. The ESC was able to shift the critical conductivity to a new optimum, which resulted in a 9% improvement in the cost function.

Chowdhury, Dhruba↗
DOE OSTI2024

Cost-optimal selection of pH control for mineral scaling prevention in high recovery reverse osmosis desalination

Conference proceedings for Foundations of Computer Aided Process Design (FOCAPD) 2024 that details a cost optimization model for pH control to prevent mineral scaling in high recovery reverse osmosis. This work applies a framework for incorporating detailed water chemistry predictions such as precipitation, pH change, and mineral scaling tendencies with process-scale optimization. It demonstrates that the acid choice for pH control can have a significant impact on pretreatment costs.

Amusat, Oluwamayowa↗
DOE OSTI2022

Optimization-based modeling and analysis of brine reflux osmotically assisted reverse osmosis for application toward zero liquid discharge systems

Significant amounts of high-salinity wastewater generated by water-intensive industrial activities such as shale oil and gas production have raised serious environmental concerns in recent years. Existing and emerging desalination technologies offer promise to manage these high salinity wastewater streams while simultaneously producing fresh water that could be diverted for beneficial uses. Osmotically assisted reverse osmosis (OARO) is one such emerging desalination technology capable of handling hypersaline brines and achieving high recoveries. However, rigorous modeling and analysis is needed to evaluate the process performance, energy consumption, and treatment cost of various OARO configurations. Here, this work presents detailed modeling and analysis of brine-reflux OARO (BR-OARO) system and compares it with other commonly discussed configurations, including cascading osmotically mediated reverse osmosis (COMRO), consecutive loop OARO, and split feed counterflow RO, through a cost optimization-based framework. We analyze and compare the treatment costs, membrane area, specific energy consumption, and design parameters of the aforementioned configurations with the ultimate goal of achieving zero liquid discharge (ZLD). The results indicate that the BR-OARO system with treatment cost of 5.1 US $/m 3 of produced water with 10% salinity outperforms other configurations in terms of number of stages needed, treatment cost, membrane area, and energy consumption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Assessing the Accuracy of Property Model Predictions for Cost Optimization of Desalination Technologies

Accurate modeling of seawater thermophysical and thermodynamic properties is critical for optimizing desalination processes. This study compares three seawater property models, a Reaktoro multicomponent model, the thermophysical seawater properties library from the Massachusetts Institute of Technology, and a simplified sodium chloride model, in the context of levelized cost of water (LCOW) minimization for reverse osmosis (RO) and mechanical vapor compression systems. Process simulations and cost optimizations reveal that although all three models yield comparable LCOW and specific energy consumption (SEC) estimates under baseline conditions, deviations among their predictions increase with salinity. Relative differences in LCOW and SEC reach up to 6% and 8%, respectively. RO results show greater variability due to differences in osmotic pressure predictions, which affect pressure constraints at high recoveries. Computational performance varies substantially; specifically, Reaktoro simulations are up to 28 times slower than empirical models due to their detailed equilibrium calculations. These results suggest that empirical models offer acceptable accuracy for routine desalination process design, while Reaktoro provides advantages in scenarios requiring detailed speciation, such as scaling or pH adjustment studies. These findings underscore the importance of selecting appropriate property models based on the modeling objective of desalination applications and motivate future work integrating thermodynamic rigor with empirical efficiency.

Physical and chemical properties↗
DOE OSTI2026

Superstructure Optimization for Brine Valorization from Brackish Water Desalination

This poster presents preliminary results from a superstructure optimization framework developed to identify cost-optimal brine valorization configurations for brackish water desalination plants across diverse U.S. regional feed chemistries. The study uses brackish groundwater compositions from Arizona, California, Florida, New Mexico, and Texas. Using Pyomo Generalized Disjunctive Programming (GDP) within the WaterTAP modeling environment, the optimization framework simultaneously evaluates thousands of candidate treatment configurations, spanning nanofiltration, reverse osmosis, and chemical precipitation, to minimize the levelized cost of water (LCOW) while meeting water recovery targets and product recovery constraints. Results across eight representative feed clusters demonstrate water recovery rates of 57–87% and net LCOW values ranging from -$0.032/m³ (net revenue-positive) to $0.80/m. Notably, no single process configuration was optimal across all feed types, underscoring the necessity of feed-specific optimization. Products targeted include calcium carbonate (CaCO₃) at $0.01/kg and sodium chloride (NaCl) at $0.10/kg, both at 95% purity, with product revenues offsetting treatment costs in several scenarios. The work advances NAWI's process systems engineering capabilities for multi-configuration screening.

58 GEOSCIENCES↗
DOE OSTI2019

Thermal desalination via supercritical CO 2 Brayton cycle: Optimal system design and techno-economic analysis without reduction in cycle efficiency

The supercritical carbon dioxide (sCO 2 ) Brayton cycle is an upcoming power cycle potentially providing higher energy efficiency and more compact turbomachinery compared to traditionally used Brayton and Rankine cycles. The heat rejected from the sCO 2 Brayton cycle is quite hot (>70 degrees C) and must be cooled before flowing back to the compressor. However, this heat is ideal for producing fresh water with a thermal desalination system. This paper focuses on integrating multi-effect distillation (MED) with the sCO 2 stream exiting the recuperator. A new methodology for integration of MED with the sCO 2 Brayton cycle is proposed for simultaneous production of power and fresh water. The distillate produced does not affect the sCO 2 cycle efficiency, i.e., distillate is produced without being a parasitic load to the power plant compared to conventional integration of MED with steam Rankine cycle. To maximize the energy recovery from the waste heat stream exiting the sCO 2 cycle, a new methodology is formulated and discussed in the Appendix. Integration of MED with a 115 MW e sCO 2 power plant (49.2% efficient) produces 2813 m3/day of distillate and the cost of distilled water is 1.1 $/m 3 . Further, to enhance the distillate production, an innovative MED configuration is proposed, and for the same cycle operating condition with an electrical output 115 MW e , the net distillate production increases by 59% to 4472 m 3 /day at 1 $/m 3 . The cost of desalinated water for the integrated system is 17.8% lower than that of a standalone reverse-osmosis plant having the same capacity. Parametric analysis is performed to calculate the optimal design parameters for the integrated MED-sCO 2 system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2015

A molecular theory for optimal blue energy extraction by electrical double layer expansion

We proposed the electrical double layer expansion (CDLE) as a promising alternative to reverse electrodialysis (RED) and pressure retarded osmosis (PRO) processes for extracting osmotic power generated by the salinity difference between freshwater and seawater. The performance of the CDLE process is sensitive to the configuration of porous electrodes and operation parameters for ion extraction and release cycles. In our work, we use a classical density functional theory (CDFT) to examine how the electrode pore size and charging/discharging potentials influence the thermodynamic efficiency of the CDLE cycle. The existence of an optimal charging potential that maximizes the energy output for a given pore configuration is predicted, which varies substantially with the pore size, especially when it is smaller than 2 nm. Finally, the thermodynamic efficiency is maximized when the electrode has a pore size about twice the ion diameter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Desal.jl

SAND2026-22942O Desal.jl is a Julia software package for lightweight reverse-osmosis desalination simulation. It supports static and dynamic operation of power-limited desalination systems without requiring optimization dependencies. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Michelen Strofer, Carlos [Sandia National Lab. (SN↗