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Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) can be applied to a net work structure systems. Considering twostage network, this paper im proves a leaderfollower structure. Likewise, it is not possible to reduce the production of undesirable outputs without any cost. The motiva tion of this study is handling a fair treatment of the undesirable outputs in leaderfollower structure. In the presence of the additional inputs in the second stage, this approach searches the proportional abatement in desirable and undesirable factors. Besides, it focuses on the less sacrifice of the desirable products. Moreover, the theoretical contributions of the proposed model have been illustrated in 13 poultry industry and 25 power plants in Iran. [ABSTRACT FROM AUTHOR]

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices. • A novel machine learning (KELM-INFO) approach for forecasting TDS is created. • The KELM-INFO is validated by four well-known machine learning methods. • The V-KELM-INFO model is assessed against the metaheuristic-based V-KELM models. • This work provides insight into improving the accuracy of model-based methods. [ABSTRACT FROM AUTHOR]

In this paper, a new thermodynamic cycle for power and fresh water production is presented, which includes MED-TVC desalinations and Allam cycles due to the important influence they have on the electric power industry in controlling and limiting greenhouse emissions. First, the Allam cycle analyzed and the appropriate location for heat-extraction identified to produce the required water vapor for the desalination system. It was determined that the heat site suitable for use in the desalination cycle is the heat from the outlet of the Compressor, which is estimated as 100 MW. The MED-TVC Desalination, one of the most suitable and most economical desalination, is used to combine the Allam cycle and desalination in the Kish district, located in the south of Iran. The proposed cycle is analyzed from the perspective of energy and exergy. The results show, the highest amount of exergy used to generate power in turbine and the amount of produced freshwater increase with increasing the capacity of Turbine. For this purpose genetic algorithm is used in two different scenarios to minimize and maximize the exergy destruction and produced fresh water respectively. The optimization of the system with genetic algorithm led to 18% decrease in total exergy destruction of the cycle in the first scenario and 7% decrease in the second optimization scenario. Furthermore, the efficiency of the cycle in first scenario and second scenario increased by 30% and 13% respectively. In the scenario optimization scheme, the amount of fresh water increased by 22%. [ABSTRACT FROM AUTHOR]

Rain gauge networks are usually redesigned to improve the accuracy of spatial and temporal estimates and reduce the monitoring costs. In addition to the rain-gauge data, satellite-based data can be used for the optimal redesign of rainfall monitoring networks. Despite high spatial and temporal resolutions of products of meteorological satellites, they must be calibrated using ground-based data. In this paper, a new methodology is presented for an optimal redesign of rain-gauge networks. In this methodology, after the bias correction of some satellite-based rainfall products (i.e., PERSIANN-CCS, TRMM3B42RTV7, and CMORPH), they are combined using a Bayesian information fusion technique. The spatiotemporal rainfall maps, which are developed using the fusion model, are used in an optimization model. The optimization model provides the optimal locations of the rain-gauge stations, considering the objectives of minimizing both the monitoring cost and the average standard deviation of estimation errors. To illustrate the applicability and efficiency of the proposed methodology, it is applied to the Khorasan Razavi Province in Iran. The results show that by using the satellite data and the Bayesian information fusion technique, the summation of standard deviations of estimation errors is significantly reduced from 18,787.9 to 778.28 mm. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide a solution to decrease water consumption in the slab-cooling unit of Mobarakeh Steel Complex in Iran. The plan should give an hourly decline in water consumption during a one-year operation period to calculate the annual reduction in water consumption of the proposed process. Recommended solutions for the conversion scheme of an existing wet cooling tower to a dry or hybrid cooling system require modeling of the slab cooling process. The curves of temperature drop in slabs are extracted in this paper by modeling the transient heat transfer of the slabs in the cooling process. This will reduce the computational volume. Then, the design and optimization of the hybrid cooling system done by the Genetic Algorithm (GA) is investigated. Accordingly, the objective function (reducing the costs of construction, operation, waste of water during the year) is chosen by single-objective method, and the modeling of the series hybrid system is done for the off-design mode during one year. The present design is for different scenarios and various savings of water consumption. The results demonstrate that if the control system is fitted with the sensitivities of the ambient temperature and also is based on the water consumption reduction and equipment design according to reliable standards, then the annual water consumption will be reduced by 88.3% (1.89 million m3). The payback period will be 3.63 years for the proposed system. • Numerical modeling of slab cooling process in transient cooling. • Extraction of the outlet water performance map to model the hybrid in off-design. • Modeling of off design air cooler control system and wet cooling tower. • An overview of hybrid system during the year based on an optimized design. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three water/power generation configurations were designed, modeled, and optimized. • Damage to human health and the ecosystem caused by diesel generator were estimated. • Division algorithm was more accurate and faster than the genetic algorithms. • Desalination with photovoltaic panel/wind turbine was environmentally efficient. • Desalination with the photovoltaic panels was economically the best configuration. Although freshwater is necessary for the well-being of humankind, increasing population growth and limited resources lead to a serious crisis to supply freshwater. Since the Earth is surrounded by seawater, desalination based on electrical power is introduced as a promising technology to provide freshwater. It is well documented that the connection of remote areas that usually do not have access to freshwater into the electricity grid is not affordable and eco-friendly. Hence, the efforts to design and construct high reliability, cost-effective, and eco-friendly stand-alone hybrid renewable energy system in remote areas. In line with this, this paper describes a novel energy management system for the optimized operation of a stand-alone hybrid energy system based on photovoltaic panels, wind turbines, batteries, and diesel generator. For this purpose, a multi-objective optimization problem is formulated by combining three objective functions, i.e., minimum the total life cycle cost as well as environmental impacts on human health and ecosystems and the maximum system reliability that can conflict with each. To solve the multi-objective optimization problem, a division algorithm is proposed that is more flexible and faster compared with conventional algorithms such as genetic algorithm. In order to show the proposed framework, a real case study in Larak Island, Iran, with appropriate solar and wind is considered. The effectiveness of the applied approach compared with optimization results of genetic algorithm and the artificial bee swarm optimization algorithm that was previously used successfully to solve optimization problems related to desalination integrated with the renewable energy system. The optimization is performed based on different diesel fuel price amounts (0.2, 0.5, and 1 $/liter). It is seen that at fuel price set to 0.2 and 0.5 $/liter, the seawater reverses osmosis desalination/photovoltaic/diesel generator/battery is the most cost-effective energy system, and when fuel price is 1 $/liter, the seawater reverses osmosis desalination/photovoltaic/wind turbine/diesel generator/battery is the most cost-effective hybrid system. While at fuel price set to 0.2, 0.5, and 1 $/liter, the seawater reverse osmosis desalination /photovoltaic/wind turbine/diesel generator/battery is the most eco-friendly. Finally, the results of this study show proposed algorithm is faster and more accurate (100 iterations, 98.36% accuracy) than the genetic algorithm (1000 iterations, 83.03% accuracy) and the artificial bee swarm optimization (300 iterations, 95.49% accuracy). [ABSTRACT FROM AUTHOR]

• A novel freshwater, cooling, and power trigeneration system based on solar energy and molten salt storage is designed. • The proposed system aims to satisfy the water and power shortage in a southern city of Iran. • The performance of the system is evaluated under worst weather conditions in summer time. • The system is optimized in four different scenarios to identify optimum operating conditions. In summer, the coastal areas of southern Iran suffer from the freshwater shortage and electricity instability. Meanwhile, this region benefits from the high intensity of solar radiation and the huge potential of brackish water resources. Hence, this paper designs a novel cooling, power and pure water trigeneration system for application in this area to mitigate its energy and water crisis. The proposed system is composed of a solar-based Goswami power and cooling production cycle and a multistage flash seawater desalination process. Moreover, a molten salt heating energy storage unit is used to prove its steady operation under uncertain sunlight condition. A comprehensive parametric study is provided to evaluate the system performance by changing the key variables. The numerical results demonstrate that increasing the pressure ratio from 6 to18 causes 3.3% leads to an increase in system efficiencies and minimum total products cost experiences at the pressure ratio of 12.6. Additionally, as the superheating degree increases from 0 to 10 K, the energy efficiency decreases but the exergy efficiency increases. When the ammonia concentration at the rectifier exit is increased from 0.965 to 0.995, the energy efficiency increases 5%, the exergy efficiency has a decreasing trend from 28% to 25.9%, and the minimum total products cost is 131.2 $/GJ. Furthermore, the exergy efficiency gains its maximum, the energy efficiency has a minimum value, and the total products cost grows with higher values of the minimum approach temperature. Finally, the system is optimized under four different scenarios to identify its best operating condition in each condition: three single-objective and one multi-objective optimization cases. The results of the multi-objective optimization are trade-offs between the ones for single-objective optimization cases. In this case, the net power of 12 MW, cooling capacity of 15.74 kW, and the freshwater of 4.72 kg/s are attainable. [ABSTRACT FROM AUTHOR]

Finding an optimal design for hydraulic structures and devices, which work together in irrigation networks, can be formulated as a multi-objective optimization problem. In this paper, a novel framework is proposed for simultaneous optimization of an open channel section and a labyrinth weir geometry. A recently proposed optimizer called Multi-Objective Multi-Verse Optimization (MOMVO) algorithm is employed and its results are compared with Pareto Envelope-based Selection Algorithm II (PESA-II) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) using five metrics, including spacing (SP), Maximum Spread (MS), Non-uniformity of Pareto Front (NPF), Mean Ideal Distance (MID), and Coverage Measure (CM). The first objective function is defined to minimize the construction costs per unit length of the open channel, and the second one is to minimize total concrete volume of the labyrinth weir. The results showed significant differences between MOMVO Pareto optimal solutions and the other two algorithms. The least values of SP, NPF, and MID metrics were provided by MOMVO, which meant its solutions had better conditions regarding uniformity and the closeness to the ideal point. To optimize irrigation network as a system, penalty functions were applied to satisfy hydraulic conditions (flow velocity, Froude number, and nappe interference). Results showed that if the proposed model had been employed in Isfahan Irrigation Networks (IINs) design, it would have reduced the construction costs of open channel and labyrinth weir approximately 11% and 74%, respectively. It can be reported that the most cost-effective design has the least channel wetted perimeter, channel cross-section top width, cross-sectional area, and labyrinth apex length; and the highest channel depth, and weir sidewall angles. • Proposing a novel hybrid framework to optimal design of irrigation networks. • Reducing construction cost of two hydraulic structures using artificial intelligence. • Comparing the performance of three AI algorithms to find the best tradeoff. • Considering a real design problem (Isfahan Irrigation Networks in Iran). • Approximating trade-offs between open channel and labyrinth weir design solutions. [ABSTRACT FROM AUTHOR]