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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]

Using hybrid renewable energy systems is a smart choice for reducing the carbon emitted by power plants. It also helps climate change mitigation and global warming, leading to universal health for humans and the environment. The efficiency of these systems depends on choosing the right combination of renewable sources, their sizes, and proper scheduling of the generating units. This paper suggests an optimisation method for sizing a geothermal/PV/wind/diesel system, both in off-grid and grid-connected configurations. In the grid-connected mode, the possibility of selling surplus energy generated by renewable resources to the network is considered. Investigations reveal that without accurate control of geothermal reservoirs, they may become depleted. In this study, a strategy is suggested for unit commitment; moreover, the harmony search algorithm is used to find the optimal size of the hybrid system in both configurations. The effectiveness of the proposed approach is represented by simulating an HRES for Ferdows/Iran. [ABSTRACT FROM AUTHOR]

The optimization of hydropower multi-reservoir systems is one of the most challenging reservoir management problems due to the complex dynamic nonlinear influencing variables such as reservoir active storage volume, released water discharge rate, and water pressure head available for hydropower generation. To optimize such a complex problem in an efficient way, the development of a high-precision meta-heuristic algorithm are of essential significance. This paper develops an adaptive differential evolution with particle swarm optimization (A-DEPSO) algorithm to derive optimal operating rules for multi-reservoir systems with the purpose of hydropower production. A-DEPSO algorithm uses novel mutation and crossover mechanisms to improve global and local search capabilities, respectively. The application of the proposed method is verified to solve a complex four-reservoir hydropower generation system, located in the southwest of Iran. The results achieved indicate that the A-DEPSO reduced the value of the objective function by an average of 57% compared with other well-known optimizers in the literature. Furthermore, the total power generated from the four-reservoir system using the proposed method increased by an average of 11% compared to the other methods. Finally, the results obtained in this study confirm that the A-DEPSO can be used efficiently to solve complex multi-reservoir hydropower generation systems. • An adaptive DE with PSO (A-DEPSO) is proposed. • A novel mutation and crossover operator are introduced. • The performance of the A-DEPSO is evaluated by a hydropower 10-reservoir problem. • A-DEPSO is used to optimize the operating rules for a hydropower four-reservoir system. [ABSTRACT FROM AUTHOR]

During the design and construction of a project, determination of geomechanical parameters is a key factor for its success. Back analysis is an appropriate method to reduce in situ and field measurements during site investigation and design phase of the projects. Over the past decades, displacement measurements were used as input data for back analysis of geomechanical parameters. In back analysis, an important factor is to choose an appropriate algorithm to minimize the magnitude of the error function in order to reduce the difference between measured and calculated displacements. In this paper, two methods, i.e. continuous ant colony algorithm (CACA) and multivariable regression (MR), were applied to calculate optimized values for four geomechanical parameters in "Lavarak" underground hydroelectric power plant cavern in Iran. These four parameters are maximum principal stress, minimum principal stress, the angle between maximum principal stress and the horizontal axis, and the deformation modulus of the rock mass. Initially, back analysis was conducted with CACA. For this purpose, CACA was programmed in Phase2 software used for numerical modelling to calculate the displacement of the measuring points. Then, back analysis of four parameters was done with both linear and nonlinear MR using Phase2, and the optimized values were calculated. According to the Lar Consulting Engineers' data for Lavarak HEPP, the results show lower general error function values of CACA than MR. In other words, CACA algorithm shows better performance for obtaining optimal values and for having more accurate calculations. CACA shows better performance for back-calculation of the maximum principal stress, the angel of maximum principal stress, and the deformation modulus of the rock mass. [ABSTRACT FROM AUTHOR]

In this paper, the efficient amounts of radiation received by a flat‐plate collector and top heat loss coefficient along with affecting parameters are investigated using measured data in different climates of Iran. Coded program in MATLAB software is then distributed to calculate the optimum tilt angle for extracting the highest solar radiation in different months of the year for five cities of Iran with different geographic latitudes. An optimal annual slope is obtained for maximum utilization and for conditions where the collector can only be adjusted at an angle during the year. Using an interpolation, an equation has been derived for obtaining the optimal tilt angle of collector for areas with latitude between 27° and 37°. Inasmuch as the top heat loss coefficient is required for evaluating the thermal performance of solar collectors, the value for different temperatures is obtained at ambient temperature up to 200°C for the absorber plate. Effects of key parameters such as collector slope, number of covers, wind speed, emittance coefficient of adsorption plate, and cover spacing on top heat loss coefficient are investigated. In the end, the particle swarm optimization (PSO) method is employed to minimize the top heat loss coefficient. The minimum amount of top heat loss coefficient is obtained with the amount of 0.99 W/m2K. [ABSTRACT FROM AUTHOR]

In this paper, the main and interaction effects of chemical reagent (collector and frother) dosages on the copper flotation performance using response surface methodology in design expert software were examined and the optimized dosage of reagents were specified. Then, in order to increase copper grade and recovery at industrial rougher flotation cells of the Miduk copper concentrator, Iran, distribution patterns of chemical reagents in suggested optimum conditions were investigated. A central composite design with three levels and five variables was employed to model and optimize the laboratory batch scale flotation process. The effect of dosages of Z11 (0–10 g/t), Flomin C4132 (14–34 g/t), A3477 (5–15 g/t) as collectors, AF65 (5–15 g/t) and MIBC (4–12 g/t) as frothers was studied. Forty-eight batch rougher flotation experiments (pH 11.5) for Miduk porphyry copper ore consisting of 0.9% Cu were conducted at different conditions using a bench scale Denver flotation cell with 3 L capacity. Second order response functions were generated for both the Cu grade and recovery models, as metallurgical indicators. These model equations were optimized using quadratic programming so as to maximize Cu grade and recovery within the experimental range studied. The main effect of all reagents dosages and the interaction of the Z11-Flomin C4132, Z11-A3477, and A3477-MIBC dosages had a significant impact on both recovery and grade. The optimum reagents dosages were found to be 5 g/t Z11, 34 g/t C4132, 5 g/t A3477, 15 g/t AF65, and 8 g/t MIBC. Maximum recovery of 91.4% and grade 8.13 were achieved under optimal conditions. Additionally, verification experiments at optimum conditions proposed by the models were carried out to specify the validity and adequacy of the predicted models. Based on the industrial rougher circuit tests results, distribution patterns of 75%-0%-25% and 50%-25%-25% were selected as the best distributions of collectors and frothers in mentioned conditions, respectively. The copper recovery and grade improved about 2% and 1.8%, respectively, via the use of optimized stage addition of reagents. [ABSTRACT FROM AUTHOR]

This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computa-tional phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (Î,) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India. [ABSTRACT FROM AUTHOR]

• A bi-level mathematical model is firstly formulated for the rice supply chain. • Two-hybrid and a modified metaheuristic algorithms are developed. • A real-life applied example is provided. • Results of numerical experiments demonstrate the efficiency of the modified algorithm. According to the recent gigantic development in the agricultural section, Agricultural Supply Chain (ASC) management has attracted both researchers and agronomic practitioners. In this regard, rice as one of the important agricultural products is generally cultivated by rural farmers in small farmlands. Due to the high demand, high price, type of products, and also wide geographic range of production and consumption, the rice supply chain has special characteristics in ASC. In this regard, this paper not only firstly considers rice supply chain and but also proposes a bi-level optimization model for rice supply chain. The proposed model aims to minimize total cost with respect to the two decision makers' opinions. Since, the bi-level programming is NP- hard , to solve the proposed model, two well-known meta-heuristic algorithms including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) along with two hybrid algorithms (PSO-GA and GA-PSO) and a modified algorithm (GPA) are utilized. In order to fill the literature gaps and to get closer to real-world applications, an applicable example in Iran is studied. Based on the results and managerial insights, the GPA is chosen as the best method and its allocated value of the variable are reported. The results show that the proposed model and solution methods are valid, practical, and effective. Also, in order to provide an insight to the functionality of the model and the results of the case, some sensitivity analyses on the major model parameters such as the demand are provided. [ABSTRACT FROM AUTHOR]

Increases in greenhouse gases emissions have encouraged the replacement of fossil fuels with renewable energy sources. This paper investigates the potential of wind energy as a renewable resource for producing agricultural water in Eghlid city, Iran. The purpose of the optimization model herein considered is to maximize the net benefit from crop production by selecting an optimal cropping pattern. This paper’s results demonstrate that wind energy can be efficiently applied to provide irrigation water and optimize cropping patterns. Specifically, the application of wind energy to withdraw irrigation water increases agricultural production benefits in the amount of 1,254 million Rials (US$45,000). [ABSTRACT FROM AUTHOR]

Abstract In this paper, the full repowering of a steam Power Plant with a capacity of 320 MW in Iran was evaluated technically and economically. Implementation of this plan for the target power plant was selected because of its over 30 years of age. Due to the presence of steam flash feed water preheaters and the possibility of preheating feed water entirely or partially prior to entering heat recovery steam generator, the effect of using or not using these low and high pressure equipment was evaluated in different cases. To do this, five different cases were proposed and reviewed. Considering the available gas turbines in Iran, the gas turbines models Siemens V94.2 and V94.3A were selected. In each case, the optimum point is obtained in terms of the lowest cost of electricity generation. Finally, after reviewing the results of all cases, full repowering plan with parallel feed water heating was selected as the best alternative in terms of energy and exergy efficiencies and the price of generating electricity. If two heat recovery steam generators and two V94.3A gas turbines are used, the net exergy and energy efficiencies for the cycle can reach 54.49% and 52.98%, respectively. Moreover, the power capacity of a repowered unit is equal to 700.44 MW with electricity generation price of 0.05986 $/kWh. Graphical abstract Image 1057 Highlights • In the present study, a real power plant has been studied (A Case Study). • The five different arrangements of feed water heaters for proposed scenarios have been considered. • The merging of water-gas heat exchangers with existing preheaters has been investigated. • Optimum techno-economic case for repowering has been presented. [ABSTRACT FROM AUTHOR]

Highlights • Developing a possibilistic MILP model to design a second-generation biodiesel supply chain network under epistemic uncertainty. • Employing a credibility-based possibilistic programming approach to convert the original possibilistic programming model into a crisp counterpart. • Proposing an accelerated benders decomposition algorithm using efficient acceleration mechanisms to deal with the computational complexity of solving the proposed model. • Verification and validation of the proposed approach through investigating a real case study in Iran. Abstract This paper proposes a possibilistic programming model in order to design a second-generation biodiesel supply chain network under epistemic uncertainty of input data. The developed model minimizes the total cost of the supply chain from supply centers to the biodiesel and glycerin consumer centers. Waste cooking oil and Jatropha plants, as non-edible feedstocks, are considered for biodiesel production. To cope with the epistemic uncertainty of the parameters, a credibility-based possibilistic programming approach is employed to convert the original possibilistic programming model into a crisp counterpart. An accelerated benders decomposition algorithm using efficient acceleration mechanisms is devised to deal with the computational complexity of solving the proposed model in an efficient manner. The performance of the proposed possibilistic programming model and the efficiency of the developed accelerated benders decomposition algorithm are validated by performing a computational analysis using a real case study in Iran. [ABSTRACT FROM AUTHOR]

In this paper, the latest approaches for automated history matching (AHM) were applied to a real brown field having 14 active wells with multiple responses (production rate, bottom hole pressure and well block pressure) located in the south of Iran. A modified support vector machine was employed to create a proxy model incorporated based on design of experimental. Thereafter, all model parameters were adjusted to reproduce the observed history within the created proxy model. Accordingly, the proposed proxy model was successfully constructed using 1086 samples based on an R2 coefficient of about 0.9 for the trained and test dataset. Finally, the process was optimized by two main algorithms to reach the best solutions, which are genetic and particle swarm optimization. [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]

Stochastic dual dynamic programming (SDDP) is a widely used technique for operation optimization of large-scale hydropower systems in which reservoir inflow uncertainty is modeled with discrete scenarios produced by statistical time series models, such as the family of periodic auto-regressive (PAR) models. It is a common practice in statistical modeling of hydrologic time series to fit a well-known probability distribution (usually normal distribution) to the data by applying proper transformation. Box-Cox transformation is a commonly used transformation in the case of normal distribution fitting. The convexity requirement of SDDP means that nonlinearly transformed time series cannot be used for statistical inflow model calibration. In this paper, a linear approximation is proposed to estimate the expected value of the next stage inflow. In the proposed approach, next-stage inflows are estimated by a model that uses transformed time series. Furthermore, using the proposed linear approximation, it is shown that it is possible to utilize the time series transformed by Box-Cox transformation for scenario generation in SDDP. The Karoon multireservoir system in Iran has been used as a case study in order to show the effectiveness of the proposed method. Some concluding remarks have also been provided by comparing the results of the two SDDP models, with and without the proposed linear approximation. [ABSTRACT FROM AUTHOR]

Osmotic power generation is one of the cleaner and sustainable methods for energy generation. This paper presents a study on the optimal size and design of pressure retarded osmosis (PRO) power plant as a viable source of renewable energy in the selected site in Bahmanshir river of Iran. The optimal sizing and design problem is formulated as a Non-Linear Programming (NLP) optimization problem and solved using the standard optimization software titled generalized algebraic modeling system (GAMS). The objective function is minimizing the total cost while considering the technical and economic constraints. Optimization results show that the optimal capacity of the PRO osmotic power plant is 77.8 MW. Economic analysis is performed for the obtained results and it was observed that construction of this type of power plants will not be attractive for private sector investors with the market energy prices (i.e. 8 cents/kW h).The effect of the future development in power density of membrane and membrane unit price in economic feasibility of the osmotic power plant is studied. [ABSTRACT FROM AUTHOR]

This paper addresses a possible decrease in flood damages by using structural methods and determines proper locations (layout) to construct protective levees and height of levees (design of levees) in high-risk areas. Because of discontinuity of the objective function and a large number of decision variables, the optimization uses the genetic algorithm (GA). The simulation of the region is carried out with the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model. The GA is employed to maximize the benefit of flood control and also to minimize the cost of protective levees' construction. Thus, the fitness function of the algorithm maximizes the net benefit of the project. After a sensitivity analysis, mutation and crossover probability are assumed to be 0.03 and 0.7, respectively. Thus, 1,000 generations and 98 chromosomes for each run are considered. The optimization model is run 5 times for floods with return periods of 50 and 100 years. Furthermore, the algorithm is used to evaluate flood damages in the Sarm and Khoor Abad River, located in the Qom province of Iran. Project net benefits resulting from the optimization model for rivers using 100-year flood exceed those for a 50-year flood. Results indicate that construction of protective levees reduces the rate of damages up to 99% in comparison with a nonconstruction of levees scenario. By taking into account the construction cost of levees, net benefits decrease by 0.97% and 1.1% for floods with 50-year and 100-year return periods, respectively. [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]

Research has revealed the impacts of climate change on the hydrologic cycle. These impacts are intensified in the urban water system due to special characteristics of urban watersheds, such as a high percentage of impervious areas and the presence of a microclimate. A slight change in rainfall intensity and duration can cause severe floods, with a significant incremental increase in life and property losses. Therefore, it is a key issue to modify the urban watershed characteristics in a systematic way to deal with these probable changes. In this paper, a multicriteria optimization model is developed to select best management practices (BMPs) for flood management in urban watershed systems. The addressed criteria include maximizing reliability of the drainage system, as well as minimizing flood damages and BMP costs. A new reliability index for dealing with floods is suggested in this study that considers both severity and duration of flooding. The optimization model is solved using a two-stage multicriteria decision making method which employs the nondominated sorting genetic algorithm II (NSGA II) and data envelopment analysis (DEA) for the selection of best alternatives. The performance of the suggested model is tested in an urban watershed located in the northeastern part of Tehran, the capital of Iran. The drainage system of this watershed is simulated using the storm water management model (SWMM) developed by United States Environmental Protection Agency (USEPA). The climate characteristics of this region are simulated under a climate change scenario based on Hadley Center Coupled Model, version 3 General Circulation Model (HadCM3 GCM) outputs. The rainfall events, with flooding potential, are determined and the optimization model is used to select the best BMPs for flooding conditions. The results show the important value of using the proposed algorithm for reducing flooding volume and increasing the drainage system's reliability. The results show further investigations are needed for real case applications to incorporate the uncertainties in climate change impacts. [ABSTRACT FROM AUTHOR]

The railroad blocking problem (RBP) is an important decision for freight railroad companies. The objective of this problem is to minimize the costs of delivering all commodities by deciding which interyard blocks to build and specifying the assignment of commodities to these blocks. In this paper, a mathematical model is presented for the RBP on Iran Railways. Its decision variables identify blocking scheme and demand assignment to these blocks. The RBP in medium and large sizes is not solvable with any commercial software available in the market. Therefore, a solution method based on tabu search algorithm is proposed for the suggested model. For evaluating the proposed algorithm, several simulated test problems are randomly generated and solved. The obtained results on the test problems are compared with those of solutions generated by CPLEX software. The comparison shows high efficiency and effectiveness of the proposed algorithm. The proposed model and solution method are applied to build a blocking plan for the Iranian railway. [ABSTRACT FROM AUTHOR]

This paper combines optimization (LINGO) and simulation (HEC-ResSim) models to determine monthly operating rules for the Zayandeh Rud Reservoir system in Iran. Based on the optimized flow determined in the single-objective framework, system behaviour was simulated over 47 years. The results show that optimizing the operation of Zayandeh Rud Reservoir could increase its storage by 88.9% and increase the reliability index of regulated water for all downstream demands by over 10%. [ABSTRACT FROM AUTHOR]

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing. [ABSTRACT FROM PUBLISHER]

Abstract: Solar collectors need to be inclined at the optimum angle to maximize the receiving energy. In spite of many theoretical and experimental investigations on optimization of solar collector inclination, there is an inconsistency in presented results. In this paper, solar global radiation on a horizontal surface was estimated using a mathematical model and the results were compared with the recorded data from the Zahedan city meteorological station. Total received solar energy by a flat inclined collector was determined in a certain day, and searching for the angle which has the maximum incident energy was the general procedure in determination of daily optimum tilt angle. In case of operational limitation for daily tilt adjustment, this procedure is repeated for other specific period of time and monthly, seasonal, semi-annual and annual optimum tilt angles were determined. A MATLAB-based code is used to calculate the daily optimum tilt angle. The results were in good agreement with the obtained data of a new constructed device. Finally, in the case of stationary devices, some recommendations were presented with respect to their typical application. [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]

A common method for assessing the energy potential of hydropower systems in Iran is the sequential streamflow routing with control on the energy-yield reliability. The method results in developing a reliability-based simulation (RBS) model that is used to analyze single-reservoir hydropower systems. In most of the cascade hydropower systems in the country, a single-reservoir RBS model is usually employed in the design and operation of each of the systems reservoirs. This paper presents a multireservoir RBS model considering the integrated operation of the systems. The model employs the general algorithm of the single-reservoir RBS model; however, with a single-period optimization submodel in determining releases from reservoirs during the system simulation. The objective function of the submodels minimizes the sum of reservoir releases and/or maximizes the sum of reservoir storages in each of the time periods, subject to a reliability constraint on the system’s energy yield. The single and multireservoir RBS models are applied in the Khersan hydropower system located in Iran as a case study. The multireservoir RBS, compared to the single-reservoir model, results in 7.9% excess firm-energy yield in reliability of 90%. [ABSTRACT FROM AUTHOR]

Increasing Renewable energies efficiency and harnessing the available potentials are among the goals that have been addressed in sustainable development. Helping to promote the utilization of these valuable and reliable sources can provide a basis for their worldwide expansion. Pump as turbine (PAT) is a suitable device to generate clean energy from hydraulic potentials. One of the main challenges of using a PAT is the lack of control tools on the flow, which affects mainly the off-design performance of the machine. In this paper, the criteria of pump selection for running in turbine mode were modified under variable head and discharge conditions by improving existing performance indicators and introducing availability. The effect of operating schemes was studied on optimal pump specifications with the effectiveness criterion consisting of capability, availability and adaptability. The performance of power plant components was investigated in regulating the flow and reducing the potential. Using a case study in Iran, dual regulation is the most effective scheme with an effectiveness of 0.4037, but mechanical scheme had the most capability with 0.4230. The results showed that compared to using average values of head and discharge for pump selection, optimization could increase the effectiveness up to 20%. • Substituting pressure reducing stations with hydropower plant was investigated. • Optimal PAT selection was performed under variable hydraulic conditions. • Design criteria were improved to consider PAT characteristics. • The performance of plant components was analyzed from energy perspective. • Regulating schemes were compared in terms of effectiveness and economy. [ABSTRACT FROM AUTHOR]

This paper brings together the benefits of a membrane-thermal desalination plant with an integrated system containing parabolic trough collectors and wind turbines in collaboration with the electrical network to produce power and freshwater. This proposed cogeneration system was employed for providing power and freshwater in Chabahar in Iran by three types of desalination system consist of the Reverse Osmosis (RO), Multi-effect distillation (MED), and Thermal Vapor Compression (TVC). Through this novel system, the heat generated in the parabolic trough collector is transferred into the organic Rankine cycle (ORC) to produce power. So, the generated power of the organic Rankine cycle and an auxiliary wind turbine can employ by the Reverse Osmosis unit, and the surplus amount of the electricity can sell to the electrical network. This configuration is enabled to increase the stability of the system, and it ultimately reduces the cost of water production by 23%. Besides, the integration of Multi-effect distillation (MED) and Thermal Vapor Compression (TVC) systems enjoys the rejected waste heat from the condenser to increase the produced freshwater. The obtained results from exergy analysis demonstrated that the exergy destruction of the solar collectors and wind turbines contributed by 39.5% and 22.2%, respectively. The results of multi-objective particle swarm optimization reveal that the exergy efficiency and the cost of freshwater production reach to 26.2% and 3.08 US$/m3, each. The environmental assessments of this system demonstrate that this hybrid system prevents the amount of 52164 tons of CO 2 emission per year. [ABSTRACT FROM AUTHOR]

Geothermal heat sources are promising renewable resources of energy. In this paper, a novel ammonia-water power cycle was designed considering the geothermal resources in Iran. This cycle was analyzed and the effects of ammonia fraction and boiler pressure on its performance were investigated. At the first, Sabalan geothermal field with temperature of 456K was studied. For this geothermal source, the effects of various ammonia fractions were perused in the boiler pressure of 25 bar. The results showed that the ammonia concentration of 0.855 led to the best performance. Also, investigating the boiler pressure at the constant concentration (0.855) indicated that the maximum efficiency happened at 35 bar. Based on genetic algorithm, the simultaneous effects of various concentrations and pressures on the performance of the ammonia-water cycle were surveyed and the optimum values were found for Sabalan source. So that, at the source temperature of 456K, the optimum concentration and pressure were obtained 0.85 and 36 bar, respectively. Finally, the optimum conditions for several geothermal source temperatures were presented within a table. Moreover, at the same sources, the presented optimized cycle was compared with other cycles in the literatures. The results indicated that the presented cycle at optimum conditions had higher efficiency. • Introducing an improved ammonia-water cycle in order to power generation by geothermal resource. • Energy and exergy analyses of the proposed cycle in details. • Optimizing ammonia concentration and boiler pressure in various temperatures of geothermal sources by genetic algorithm (GA). [ABSTRACT FROM AUTHOR]

The optimal allocation and sizing of distributed generation (DG) resources are important in installing these resources, to improve the technical parameters of the network, including the power losses, voltage profile, and short-circuit level, as well as to increase economic factors. In this paper, a new multi-criteria algorithm and objective function are proposed for the optimal sizing and allocation of renewable and non-renewable DG resources simultaneously. The proposed algorithm is implemented on 63/20 kV substations at 20 kV levels. In the proposed objective function, all important technical and economic factors as well as important constraints, such as penetration level of DGs and budget constraint, are considered in a way that all factors are assigned to monetary values. Moreover, a new mathematical formulation is introduced for the allocation of renewable DG resources to reduce run-time optimization. The genetic algorithm (GA) is employed in the proposed algorithm to minimize the objective function. For renewable DG resources, photovoltaic panels and wind turbines, and for non-renewable DG resources, gas turbines are considered. The 115 buses network of Bakhtar Regional Electric Company (BREC) in Iran is used to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm improves technical factors efficiently and maximizes the profitability of the investment. [ABSTRACT FROM AUTHOR]

Design of water distribution networks (WDNs) is classified as a large combinatorial discrete nonlinear nondeterministic polynomial-hard (NP-hard) optimization problem. The main concerns associated with the optimization of WDNs are related to the nonlinearity of the discharge–head loss relationships for pipes and the discrete nature of pipe sizes. This paper proposes a new evolutionary algorithm, called fittest individual referenced differential evolution (FDE), which is significantly more efficient and reliable than other algorithms for optimization of WDN designs. The fundamental structure of FDE is similar to conventional differential evolution (DE), although the functions of the mutation and crossover operators are to exploit good solutions, and explore very different solutions, respectively, and counting and restarting mechanisms are introduced to identify and avoid local optima. These features are beneficial in WDN design because such problems may have an array of local optima that are far from one another, making finding the global optima difficult. The mutation and crossover operators are developed to accelerate convergence and, although the optimal mutation and crossover parameters may vary among networks, the efficiency of the algorithm reduces the need for adaptive parameters or population size. A sensitivity analysis is conducted for algorithm parameters, as well as counting and restarting limits, based on eight evolutionary algorithm (EA) benchmark test problems, and the efficiency of the FDE for solving 2- and 10-dimensional versions of these problems is demonstrated using the most effective values of the parameters and limits. FDE-based optimal designs of four benchmark networks—the Two-loop, Hanoi, and New York WDNs and the Balerma Irrigation Network—show that on average, a minimal number of function evaluations (or hydraulic simulations) is required to reach the best known optimum, which is less than or equal to 600 in all cases for the first three networks. Furthermore, a detailed application of FDE to a large-sized network, that for the City of Farhadgerd, Iran, shows that FDE is significantly more effective than other EAs in terms of its speed of convergence and reliability. [ABSTRACT FROM AUTHOR]