Search Results (90)

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article

Water Supply and Distribution Networks (WSDNs) offer underexplored potential for energy recovery. While many studies confirm their technical feasibility, few assess the long-term operational compatibility and economic viability of such solutions. This study evaluates the energy recovery potential of the Brussels Capital Region’s WSDN using four years (2019–2022) of operational data. Rather than focusing on available technologies, the analysis examines whether the real behavior of the network supports sustainable energy extraction. The approach includes network topology identification, theoretical power modeling, and detailed flow and pressure analysis. The Brussels system, composed of a Water Supply Network (WSN) and a Water Distribution Network (WDN), reveals strong disparities: the WSN offers localized opportunities for energy recovery, while the WDN presents significant operational constraints that limit economic viability. Our findings suggest that day-ahead electricity markets provide more suitable valorization pathways than flexibility markets. Most importantly, the study highlights the necessity of long-term behavioral analysis to avoid misleading conclusions based on short-term data and to support informed investment decisions in the urban water–energy nexus. Full article

In bulk liquid or on solid surfaces, nanobubbles (NBs) are gaseous domains at the nanoscale. They stand out due to their extended (meta)stability and great potential for use in practical settings. However, due to the high energy cost of bubble generation, maintenance issues, membrane bio-fouling, and the small actual population of NBs, significant advancements in nanobubble engineering through traditional mechanical generation approaches have been impeded thus far. With the introduction of the electric field approach to NB creation, which is based on electrostrictive NB generation from an incoming population of “electro-fragmented” meso-to micro bubbles (i.e., with bubble size broken down by the applied electric field), when properly engineered with a convective-flow turbulence profile, there have been noticeable improvements in solid-state operation and energy efficiency, even allowing for solar-powered deployment. Here, these innovative methods were applied to a selection of upstream and downstream activities in the oil–water–fuels nexus: advancing core flood tests, oil–water separation, boosting the performance of produced-water treatment, and improving the thermodynamic cycle efficiency and carbon footprint of internal combustion engines. It was found that the application of electric field NBs results in a superior performance in these disparate operations from a variety of perspectives; for instance, ~20 and 7% drops in surface tension for CO₂- and air-NBs, respectively, a ~45% increase in core-flood yield for CO₂-NBs and 55% for oil–water separation efficiency for air-NBs, a rough doubling of magnesium- and calcium-carbonate formation in produced-water treatment via CO₂-NB addition, and air-NBs boosting diesel combustion efficiency by ~16%. This augurs well for NBs being a potent agent for sustainability in the oil and fuels sector (whether up-, mid-, or downstream), not least in terms of energy efficiency and environmental sustainability. Full article

Energy-efficient and cost-effective water desalination systems can significantly replenish freshwater reserves without further stressing limited energy resources. Currently, the majority of the desalination systems are operated by non-renewable energy sources such as fossil fuel power plants. The viability of any desalination process depends primarily on the type and amount of energy it utilizes and on the product recovery. In recent years, membrane distillation (MD) and forward osmosis (FO) have drawn the attention of the scientific community because of FO’s low energy demand and the potential of MD operation with low-grade heat or a renewable source like geothermal, wind, or solar energy. Despite the numerous potential advantages of MD and FO, there are still some limitations that negatively affect their performance associated with the water–energy nexus. This critical review focuses on the hybrid forward osmosis–membrane distillation (FO-MD) processes, emphasizing energy demand and product quality. It starts with exploring the limitations of MD and FO as standalone processes and their performance. Based on this, the importance of combining these technologies into an FO-MD hybrid process and the resulting strengths of it will be demonstrated. The promising applications of this hybrid process and their advantages will be also explored. Furthermore, the performance of FO-MD processes will be compared with other hybrid processes like FO–nanofiltration (FO-NF) and FO–reverse osmosis (FO-RO). It will be outlined how the FO-MD hybrid process could outperform other hybrid processes when utilizing a low-grade heat source. In conclusion, it will be shown that the FO-MD hybrid process can offer a sustainable solution to address water scarcity and efficiently manage the water–energy nexus. Full article

Climate change disrupts global food systems by affecting water, energy, ecosystems, and agricultural productivity. Building climate resilience demands integrated approaches that recognize interdependencies among water, energy, food, and environmental (WEF-E) systems. This review synthesizes current research on how the WEF-E nexus can guide climate adaptation strategies. It highlights interdisciplinary solutions—such as solar-powered irrigation, agrivoltaics, agroforestry, conservation agriculture, and nature-based water management—that enhance resource efficiency, stabilize yields, and reduce environmental degradation. Effective implementation requires governance innovation, stakeholder participation, and coherent cross-sector policies. The paper also outlines research priorities, including the development of resilience metrics, modeling tools, and inclusive decision-making mechanisms. Emphasizing both adaptation and mitigation, the WEF-E nexus offers a transformative lens for sustainable, equitable, and climate-resilient food systems. As climate pressures intensify, advancing this integrated framework presents both an urgent necessity and a strategic opportunity to align food security with environmental stewardship. Full article

Water scarcity under climate change and increasingly stringent water conservation policies may trigger energy security concerns. The current study develops an optimization model to investigate the impacts of water conservation policies and hydrological uncertainties on the regional energy transition process in Shanxi Province, China. The dual-control policies on total water consumption and water intensity are systematically examined for their differential constraints and stimulative effects on various power generation types. Hydrological time series analysis methods are employed to project future water resource variations in Shanxi Province and evaluate their implications for power system optimization. The results indicate that (1) total water constraint policies are more stringent than water intensity constraint policies; (2) changes in water resource availability impose greater restrictions on coal power development than those imposed by current water conservation policies; and (3) when total water resources decrease by approximately 43.5% compared with 2020 levels, Shanxi Province may face electricity shortages. These findings suggest that water conservation policy formulation should be coordinated with regional power sector development planning, while also considering potential energy security risks posed by potential future reductions in water resources. Full article

The study investigates the impact of water use in energy production in industrial plants, considering the interdependence between water and energy, or the water–energy nexus, to promote sustainable water and energy management. More specifically, it focuses on the industrial sector, particularly on electricity production in thermoelectric power plants, which require large amounts of water for cooling in its production cycle. The field of analysis is set in Italy, referring to the applications of the European Industrial Emissions Directive and Italian regulations that govern water and energy usage. The focus is on large combustion plants, which need to be monitored by national authorities. The Italian situation is outlined, exposing consumption data from major thermoelectric power plants in 2021 through 2023, highlighting the water usage trend and electricity production. In 2023, total water use for these installations was 9,892,719,965 m³—mainly from seawater—with an overall production of electric energy of 117,239,954 MWh, with a relevant fuel consumption from natural gas (18,544,742,774 Sm³). It also analyzed the application of best available techniques to reduce water consumption, recycle water flows, and minimize the environmental impact of power plants. Finally, the main fuels used in these plants, such as natural gas, coal, and biomass, are presented, along with the environmental performance of the power plants based on water use per unit of energy produced. Full article

Sustainable water management requires integrated approaches balancing competing demands and environmental sustainability. This study introduces the Sustainable Water Optimization Tool (SUWO), an open-source, Python-based simulation-optimization framework for basin-scale surface-water-resources management. SUWO employs the water–energy–food–ecosystem (WEF-E) nexus approach, utilizing a multi-objective genetic algorithm (MOGA) to generate Pareto-optimal solutions and facilitate a trade-off analysis among water uses through simulations of reservoir operations, hydro-energy production, irrigation, and flow regulation. SUWO integrates scenario analysis with multi-criteria decision making (MCDM), enabling the evaluation of various management, climate, and environmental scenarios. The framework was applied to the Sakarya River Basin (SRB) in Türkiye, a rapidly developing region pressured by water infrastructure development, hydroelectric power plants (HEPPs), and irrigation expansion. The SUWO-SRB model showed that while Non-dominated Sorting Genetic Algorithm II (NSGA-II) generally exhibited superior performance, NSGA-III presented a competitive alternative. The optimization results were analyzed across four management scenarios under varying hydrological conditions and environmental management classes (EMCs) for the near future. The model results highlight WEF-E nexus trade-offs. Maximizing energy production often impacts irrigation and the ecosystem, while prioritizing sustainable irrigation can reduce energy output. Dry conditions reduce hydropower and irrigation capacity, emphasizing water scarcity vulnerabilities. Ecological deviation negatively correlates with anthropogenic factors. Full article

The rising demand for both water and energy has intensified the urgency of addressing the water–energy nexus. Energy is required for water treatment and distribution, and energy production processes require water. The increasing demand for energy requires substantial amounts of water, primarily for cooling. The emergence of new persistent contaminants has necessitated the use of advanced, energy-intensive water treatment methods. Coupled with the energy demands of water distribution, this has significantly strained the already limited energy resources. Regrettably, no straightforward, universal model exists for estimating water usage and energy consumption in power and water treatment plants, respectively. Current approaches rely on data from direct surveys of plant operators, which are often unreliable and incomplete. This has significantly undermined the efficiency of the plants as these surveys often miss out on complex interactions, lack robust predictive power and fail to account for dynamic temporal changes. The study thus aims to evaluate the potential of mathematical modeling and simulation in the water–energy nexus. It formulates a mathematical framework and subsequent simulation in Java programming to estimate the water use in hydroelectric power and geothermal energy, the energy consumption of the advanced water treatment processes focusing on advanced oxidation processes and membrane separation processes and energy demands of water distribution. The importance of mathematical modeling and simulation in the water–energy nexus has been extensively discussed. The paper then addresses the challenges and prospects and provides a way forward. The findings of this study strongly demonstrate the effectiveness of mathematical modeling and simulation in navigating the complexities of the water–energy nexus. Full article

Two-phase evaporative spray cooling technology can significantly reduce power consumption in data centre cooling applications. However, the literature lacks an established methodology for assessing the overall performance of such evaporation systems in terms of the water-energy nexus. The current study develops a Lagrangian–Eulerian computational fluid dynamics (CFD) modelling approach to examine the functionality of these two-phase evaporative spray cooling systems. To replicate a modular system, a hollow spray cone nozzle with Rosin–Rammler droplet size distribution is simulated in a turbulent convective natural-air environment. The model was validated against the available experimental data from the literature. Parametric studies on geometric, flow, and climatic conditions, namely, domain length, droplet size, water mass flow rate, temperature, and humidity, were performed. The findings indicate that at elevated temperatures and low humidity, evaporation results in a bulk temperature reduction of up to 12 °C. A specific focus on the climatic conditions of Dublin, Ireland, was used as an example to optimize the evaporative system. A new formulation for the coefficient of performance (COP) is established to assess the performance of the system. Results showed that doubling the injector water mass flow rate improved the evaporated mass flow rate by 188% but reduced the evaporation percentage by 28%, thus reducing the COP. Doubling the domain length improved the temperature drop by 175% and increased the relative humidity by 160%, thus improving the COP. The COP of the evaporation system showed a systematic improvement with a reduction in the droplet size and the mass flow rate for a fixed domain length. The evaporated system COP improves by two orders of magnitude (~90 to 9500) with the reduction in spray Sauter mean diameter (SMD) from 292 μm to 8–15 μm. Under this reduction, close to 100% evaporation rate was achieved in comparison to only a 1% evaporation rate for the largest SMD. It was concluded that the utilization of a fine droplet spray nozzle provides an effective solution for the reduction in water consumption (97% in our case) for data centres, whilst concomitantly augmenting the proportion of evaporation. Full article

The swift expansion of the global population and economy has spurred growing requirements for energy and water in recent decades. Inefficient energy and water consumption, however, has led to an increase in CO₂ emissions. Hence, the socio-economic development of a country must consider the interconnections between energy, water and carbon, as there are mutual dependencies among these three elements. This work considers the nexus between energy, water and carbon in the design of integrated energy–water systems using a new automated targeting modeling (ATM) framework. ATM incorporates the advantages of the insight-based Pinch method and a mathematical programming approach to provide visual understanding for a thorough analysis of the problem while guaranteeing accurate solutions. Minimum targets of power and water based on the integrated network operation were established by the ATM, with corresponding carbon emissions. A specific goal of annual carbon emissions reduction was set as the constraint and the ATM optimized the capacities of the components in the system accordingly to achieve minimum overall cost. The application of ATM on an industrial plant case study shows that a target of 45% reduction in the carbon discharge amount was achieved by shifting to greener fuel in the energy system at a minimum overall cost increase of 0.45% only. The framework can assist users in meeting power and water loads in their plant while planning for the appropriate decarbonization efforts at the minimum possible cost. Full article

Selecting the appropriate desalination and renewable energy technologies is crucial for the success of desalination projects, as each technology offers distinct advantages and disadvantages tailored to specific project requirements. This research investigates the application of both the analytic hierarchy process and fuzzy logic techniques to develop four decision-making models: two for selecting the optimal desalination technology and two for selecting the optimal renewable energy technology in coastal communities. For desalination technology selection, the analytic hierarchy process model is structured into four hierarchical levels: the main goal, criteria, sub-criteria, and alternatives. The criteria level encompasses four groups, while the sub-criteria level comprises 26 factors. The alternatives considered are reverse osmosis, electrodialysis, and multi-stage flash. In parallel, the analytic hierarchy process model for renewable energy technology selection is similarly structured, with four criteria groups and 24 sub-criteria factors. The alternatives evaluated include photovoltaic, concentrated solar power, and wind energy. Additionally, fuzzy logic models are developed for both desalination and renewable energy technology selection. These models enhance the decision-making framework by incorporating the uncertainty and vagueness that are inherent in real-world scenarios. The integration of analytic hierarchy process and fuzzy logic methodologies provide a robust approach to identifying optimal technologies, thereby supporting sustainable development in Egypt’s water–energy nexus. The research outcomes highlight the effectiveness of integrating analytic hierarchy process and fuzzy logic in decision-making processes, offering decision-makers systematic and reliable approaches for selecting the most suitable technologies to achieve sustainability in water–energy nexus projects. The results of the research indicate that the best alternative for desalination was reverse osmosis, and for renewable energy was photovoltaics. Full article

This study presents an advanced co-optimization model for water–energy nexus systems (WENSs), illustrating considerable benefits in both energy conservation and cost reduction through synergistic operations. Case studies compare the co-optimized operations of a 33-bus power distribution network (PDN) coupled with a commercial-scale 15-node water distribution network (WDN) via water pumps and a standalone operations of a PDN and WDN, revealing that co-optimization notably decreases the operational costs for both networks by 23% and 49%, respectively, leading to substantial daily savings. In addition, this paper summarizes the current problems based on previous research, delineating the challenges in the co-optimization and management of WENSs, such as modeling inaccuracies, uncertainty management, and multi-stakeholder governance, providing meaningful insights and potential directions for future research. Full article

Water desalination systems integrated with stand-alone hybrid energy sources offer a remarkable solution to the water–energy challenge. Given the complexity of these systems, selecting an appropriate energy management system is crucial. In this regard, employing artificial intelligence techniques to develop and validate an energy management system can be an effective approach for handling such intricate systems. Therefore, this paper presents an ANN-based energy management system (ANNEMS) for a pumping and desalination system connected to an isolated hybrid renewable energy source. Thus, a parametric sensitivity algorithm was developed to identify the optimal neural network architecture. The water–energy management-based supervised multi-layer perceptron neural network demonstrated effective power sharing within a short time frame, achieving accuracy criteria of RMSE, R, and R² between the actual and estimated electrical power of the three motor pumps. The ANNEMS is defined to facilitate real-time power sharing distribution among the various system motor pumps on the test bench, considering the generated power profile and water tank levels. The proposed strategy employs power field oriented control to maintain DC bus voltage stability. Experimental results from the implementation of the proposed ANNEMS are provided. Therein, the power levels of the three motor pumps demonstrated consistent adherence to their reference values. In summary, this study highlights the significance of selecting appropriate energy management for real-time experimental validation. Full article

Everyday activity incurs carbon footprints, which are classified as personal, production, organizational and national, and may be assessed by input–output analysis (IOA), life-cycle assessment (LCA), or the combination of LCA and IOA methods. Notwithstanding international standards, like ISO 14064 and Publicly Available Specification (PAS) released for standardization, carbon footprint results can vary and sometimes lack consistency that due to variations in data sources, crossover boundary definitions, and operational boundaries for indirect emissions. The novelty of this study is the direct utilization of condensed water in an existing cooling water system, without the need for prior wastewater treatment, as typically required for greywater. The lack of practical case studies exploring the water–energy nexus in the context of reclaiming condensed water for evaporative cooling tower systems makes this research particularly significant. This highlights that condensed water can be a straightforward and cost-effective solution for both water conservation and energy savings. This case study highlights the benefits of reclaiming condensed water as supplementary cooling water, which proved effective in water quality treatment and dilution augmentation, considering that a higher cycle of concentration (CoC) was achieved, leading to reduced bleed-off that resulted in a water saving of 44% for make-up and 80% for bleed-off water, and energy savings from 6.9% to 13.1% per degree Celsius of condensing refrigerant temperature (CRT). The analytical assessment revealed that reclaiming condensed water is a promising answer for green building and is a by-product of condensation without extra power demands, avoiding the generation of an increased carbon footprint and exacerbation of greenhouse gas (GHG) emissions from freshwater resource extraction, and for the production of energy-efficient devices or substitutions. By eliminating the need for wastewater treatment, this research enhances the practicality and feasibility of direct use of condensed water in various applications. This approach not only promotes sustainability by conserving water and energy but also renews interest among proponents of green building practices. It has the potential to accelerate the adoption of this method and integrate it into green building designs. Full article