This study proposed a holistic three-fold scheme that synergistically optimizes the benefits of the Water-Food-Energy (WFE) Nexus by integrating the short/long-term joint operation of a ...multi-objective reservoir with irrigation ponds in response to urbanization. The three-fold scheme was implemented step by step: (1) optimizing short-term (daily scale) reservoir operation for maximizing hydropower output and final reservoir storage during typhoon seasons; (2) simulating long-term (ten-day scale) water shortage rates in consideration of the availability of irrigation ponds for both agricultural and public sectors during non-typhoon seasons; and (3) promoting the synergistic benefits of the WFE Nexus in a year-round perspective by integrating the short-term optimization and long-term simulation of reservoir operations. The pivotal Shihmen Reservoir and 745 irrigation ponds located in Taoyuan City of Taiwan together with the surrounding urban areas formed the study case. The results indicated that the optimal short-term reservoir operation obtained from the non-dominated sorting genetic algorithm II (NSGA-II) could largely increase hydropower output but just slightly affected water supply. The simulation results of the reservoir coupled with irrigation ponds indicated that such joint operation could significantly reduce agricultural and public water shortage rates by 22.2% and 23.7% in average, respectively, as compared to those of reservoir operation excluding irrigation ponds. The results of year-round short/long-term joint operation showed that water shortage rates could be reduced by 10% at most, the food production rate could be increased by up to 47%, and the hydropower benefit could increase up to 9.33 million USD per year, respectively, in a wet year. Consequently, the proposed methodology could be a viable approach to promoting the synergistic benefits of the WFE Nexus, and the results provided unique insights for stakeholders and policymakers to pursue sustainable urban development plans.
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•Propose a holistic scheme to synergistically optimize the benefits of WFE Nexus•NSGA-II optimizes reservoir operation to maximize hydropower output and impoundment.•Water supply is simulated by joint operation of the reservoir and irrigation ponds.•Reduce water shortage rate by 22% & increase hydropower benefit by 9.33m USD/year•The proposed methodology is a viable approach to promoting synergistic benefits.
Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not ...just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space. Recognizing the intricate interplays among these sectors, System Dynamics (SD) and Agent-Based Modeling (ABM) were integrated in a Complex Adaptive System (CAS) to capture the interactions and feedback mechanisms inherent within treated wastewater allocation systems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) serves as the optimization tool, enabling the identification of optimal allocation strategies across various management scenarios over a 25-year simulation period. Our research navigates the complexities of long-term resource management, accounting for each sector's evolving its objectives and guidelines along the whole system objectives and strategies. The outcomes demonstrate how treated wastewater can be effectively distributed to support economic and social equity –as the system objectives-while supporting agricultural and industrial growth and enhancing efficiency and social well-being –reflecting individual agent objectives-within the CAS framework. The research explores four distinct management scenarios, each prioritizing different sectors to address water resource management challenges. Notably, all four scenarios align with the strategies required by the ruler (government), providing strategic guidance to water resource managers for decision-making. The simulation results reveal a scenario where all sectors' demands are met, with Scenario 4 emerging as the most effective. Scenario 4 aligned with the objectives and guidelines of each sector, demonstrating significant improvements in the CY (Agriculture agent index; increased from 0.2 to 0.68), IGI (Industry agent index; increased from 1 to 1.63), and GAI (Urban Green Space agent index; increased from 1 to 1.23) indices over the 25-year simulation period. By providing a strategic blueprint for policymakers and stakeholders, this study contributes significantly to the discourse on sustainable water resource management, presenting a replicable model for similar contexts globally, where judicious allocation of treated wastewater is paramount for achieving harmony between human activity and ecological preservation.
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•A novel method for optimal treated wastewater allocation using System Dynamics and Agent-Based Modeling is introduced.•Optimal allocation strategies with a 25-year NSGA-II simulation are identified.•Strategic guidance for decision-making across four management scenarios is provided.•A replicable model demonstrating the benefits of judicious treated wastewater allocation globally is offered.
The efficacy of novel polycarbonate ultrafiltration, aluminum oxide nanoparticle (Al2O3-NPs) volume fraction, temperature, and water/ethylene glycol (EG) ratio were evaluated to determine the ...thermophysical properties of the membrane. 5%–10% of Al2O3-NPs have been added to the PC. A machine learning approach was used to compare the volume fraction of Al2O3-NPs, the temperature, and the water-to-ethylene glycol (EG) ratio. To determine the impact of Al2O3-NPs loading on the Response Surface Method (RSM), DOE, ANOVA, ANN, MLP, and NSGA-II, the number of aluminum oxide nanoparticles (Al2O3-NPs), temperature, and water/ethylene glycol (EG) on membranes in PC ultrafiltration are evaluated. Based on the Relative Thermal Conductivity Model (RSM), the regression coefficient of Al2O3 in water and EG was 0.9244 and 0.9170 with adjusted regression coefficients. A higher concentration of EG enhances the thermal conductivity of the membrane when the effective parameters are considered. The effect of temperature on the relative viscosity of the membrane led to the conclusion that Al2O3 water/EG can cool at high temperatures while providing no viscosity change. When Al2O3 is dissolved in water and EG, more EG is necessary to optimize the mode of reactivity. Using the MLP model, the calculated R-value is 0.9468, the MSE is 0.001752989 (mean square error), and the MAE is 0.01768558 (mean absolute error). RSM predicted the average thermal conductivity behavior of nanofluid better. The ANN model, however, has proven to be more effective than the RSM in simulating the relative viscosity of nanofluids. The NSGA-II optimized results showed that the minimum relative viscosity and maximum coefficient of thermal conductivity occurred at the lowest water ratio and maximum temperature.
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•Membrane viscosity increases when water is a greater fraction of the base fluid.•MLP and RSM models were developed to predict Thermophysical properties Al2O3 in Water and EG.•The NSGA-II was coupled with RSM and MOPSO to conduct the optimization.•A wide range of applications for membrane technology in industry and in daily life.
Detailed parametric analysis and measurements are required to reduce building energy usage while maintaining acceptable thermal conditions. This research suggested a system that combines Building ...Information Modeling (BIM), machine learning, and the non-dominated sorting genetic algorithm-II (NSGA II) to investigate the impact of building factors on energy usage and find the optimal design. A plugin is developed to receive sensor data and export all necessary information from BIM to MSSQL and Excel. The BIM model was imported to IDA Indoor Climate and Energy (IDA ICE) to execute an energy consumption simulation and then a pairwise test to produce the sample data set. To study the data set and develop a prediction model between building factors and energy usage, 11 machine learning algorithms are used. The best algorithm was Group Least Square Support Vector Machine (GLSSVM), later employed in NSGA II as the building energy consumption fitness function using Dynamo software. An NSGA II multi-objective optimization model is designed to reduce building energy consumption and optimize interior thermal comfort (measured by the predicted percentage of dissatisfied (PPD)). The Pareto front is calculated, and the optimum point approach is used to find the best combination of building envelope characteristics, HVAC setpoints, shading parameters, lighting, and air infiltration. The feasibility and effectiveness of the developed framework are demonstrated using a case study of an upper secondary school building in Norway; the results show that: (1) The GLSSVM has a unique capacity to forecast building energy use with high accuracy: R2 of 0.99, an RMSE of 1.2, MSE of 1.44, and MAE of 0.89; (2) Building energy consumption and thermal comfort may be successfully improved by the GLSSVM-NSGA II hybrid technique, which reduces energy consumption by 37.5% and increases thermal comfort by 33.5%, respectively.
•PV hosting capacity improvement is provided using a multiobjective reconfiguration.•Reconfiguration is studied in a probabilistic framework considering load-scenarios.•Voltage stabilization and loss ...minimization are addressed in presence of harmonics.•NSGA-II and fuzzy decision making are applied for solving the optimization problem.
The current steps toward the implementation of carbon–neutral electrical energy systems lead to high levels of PV penetration especially in residential sectors. However, there are many limitations in the integration of extra PV generation units in modern distribution systems. Hence, supplementary actions are needed for providing the capability of hosting high levels of PV units in future grids. In this study, the application of Distribution System Reconfiguration (DSR) is examined in order to increase PV Hosting Capacity (PVHC) of a harmonically polluted distribution system. However, bringing several services together, DSR is studied in a multiobjective framework to improve the voltage profile and decrease the total energy loss as well as improving the PVHC. Moreover, probabilistic demand scenarios are included in this study through applying different combinations of linear and nonlinear load-levels to provide a more precise assessment of the objectives. Finally, a solution strategy is proposed for the presented multiobjective problem based on the implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and fuzzy decision-making method. The proposed framework is then applied to modified 33-bus and 69-bus distribution systems in presence of nonlinear loads. According to the results, applying the proposed methodology for DSR could successfully increase the PVHC of the harmonic-polluted grid as well as providing voltage profile stabilization and a considerable decrease of the energy loss in the system.
Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising ...approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider's and users' perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider's cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.
•PCR turbulators are used in an air to water heat exchanger.•f increases with increase of PR.•Nu is an increasing function of Re, PR.•η increases with increase of λ.
Forced convective turbulent ...hydrothermal analysis in a double pipe heat exchanger is presented experimentally. Perforated turbulators have been utilized in annulus region. Hot water makes the cold air in outer tube warmer. Various amounts of pitch ratio, open area ratio and Reynolds number are considered. Correlations for Nusselt number, thermal performance and Darcy friction factor are examined. NSGA II is utilized to optimize the design. Physical phenomena are shown by means of FVM. Results reveal that thermal performance enhances with augment of open area ratio. Temperature gradient reduces with augment of pitch ratio. The maximum value of thermal performance obtained at η=1.59 which is occurred for Re=6000,λ=0.07,PR=1.06.
In this paper, a novel vehicular high temperature proton exchange membrane fuel cell (HT-PEMFC) power system integrated with methanol steam reforming (MSR) and Organic Rankine Cycle (ORC) is ...proposed. The system uses waste heat from the HT-PEMFC to provide the MSR subsystem for hydrogen production, and the ORC subsystem recovers the remaining heat to generate electrical power. The thermodynamic model of the system and multi-criteria evaluation are established. Numerical results show that increasing the current density and decreasing the cathode pressure and stoichiometry is favorable for improving the net output power of the system, but also increasing the levelized cost of energy and carbon mass specific emission of the system. Higher operating temperatures and anode pressures are beneficial to overall performance. Moreover, the system is optimized using the NSGA-II algorithm to obtain the three-dimensional (3D) Pareto solution and the optimal set of operating parameters. The optimized system net output power, levelized cost of energy and carbon mass specific emission at the Final Optimal Point are 36.98 kW, 0.2138 $/kWh and 0.5583 kg/kWh, respectively. Compared to the un-optimized system, the system working at optimal parameters has better thermodynamic, economic and environmental performance.
•A novel HT-PEMFC power system integrated with MSR and ORC is proposed for vehicle applications.•The system thermodynamic model and multi-criteria evaluation are established.•The influence of key HT-PEMFC operating parameters on system performance is investigated.•The optimal set of operating parameters for the system is obtained using NSGA-II.
•A data-driven framework is proposed to optimize the sizing of a hybrid energy system.•A modified NSGA-II based on reinforcement learning is utilized to obtain Pareto set.•CRITIC-TOPSIS is used to ...decide the weight of objectives and select the best solution.•A optimal system with LCOE of 0.226 $/kWh, LPSP of 4.01% and PAR of 2.15% is obtained.
This paper proposes a data-driven two-stage multi-criteria decision-making (MCDM) framework to investigate the optimal configuration of a stand-alone wind/PV/hydrogen system. In the first stage, a modified non-dominated sorting genetic algorithm (NSGA)-II based on reinforcement learning is utilized to determine a set of Pareto solutions. The objectives considered are to minimize the levelized cost of energy (LCOE), the loss of power supply possibility (LPSP) and the power abandonment rate (PAR), simultaneously. In the second stage, the Criteria Importance Though Intercrieria Correlation (CRITIC) method is utilized to determine the weight of the three objectives, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach is employed to select the unique best solution from Pareto solutions. To verify the effectiveness, the framework is applied to the wind/PV/hydrogen system located in Aksay Kazak Autonomous County, Gansu Province, China to meet an off-grid industrial park’s load demand of 1603 kWh/day and peak load of 117.17 kW. The result states that the optimal system, which consists of 83.2 kW PV panels, 160 kW wind turbines, 20 kW fuel cells, 54 kW electrolyzers and 450 m3 hydrogen storage tanks, owns the LCOE of 0.226 $/kWh, the LPSP of 4.01% and the PAR of 2.15%.
