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Green Infrastructures as Best Management Practice (GI‐BMP) play important role in preserving cities from urban flood and excessive runoff. In the process of using GI‐BMP in cities for stormwater management, a number of steps are taken that normally include selection of suitable sites, formulating proper combination of infrastructures, and optimization of the place and design of GI‐BMPs to maximize their cost‐effectiveness. This paper presents a site‐scale GI‐BMP implementation in Tehran West Bus Terminal (TWBT), Iran. To achieve this goal, this study applies a three steps framework namely GI‐BMP suitability analysis, GI‐BMP combination planning, and GI‐BMP optimization. In the first step, using the BMP Siting Tool, the suitable places for allocating GI practices were identified. In the next step, suitable GI‐BMP practices, including permeable pavements, bioretention basin, infiltration trench, and rain barrel were planned and arranged for each subwatersheds of the study area. In the third step, with the use of System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) model and NSGA‐II algorithm, the sizes of the planned GI‐BMP types were optimized for each subwatershed. The results indicate that runoff problem caused by surface runoff in the study area was serious and needed to be controlled. The results also revealed that there were 104 near‐optimal solutions that help reduce runoff volume by up to 70%. According to the results, applying GI‐BMPs in TWBT will reduce 60% of flow volume in the site with the price of 353,568$. This research is of practical importance for stormwater management using nature‐based solutions in bus terminals. Recommendation For Resource Managers: Considering suitable places, selecting proper combination, and optimizing GI‐BMP location and design will result in an efficient green infrastructure planning in the context of cities.BMP Siting Tool and SUSTAIN model applied in this research provided a framework for planning and optimization of GI‐BMP in TWBT.This study suggested 104 near‐optimal solutions for GI‐BMP planning for TWBT reducing the cost of GI construction and reduce runoff volume.The proposed solutions for GI‐BMP development in TWBT may reduce up to 60% of annual volume of runoff with the cost of 353,568$. [ABSTRACT FROM AUTHOR]

Holding a lasting balance between the water resources and water demands has become a challenging task for water resources managers, especially in recent years with the looming global warming crisis and its resulting climatic change effects. This paper focuses on modeling the optimized operation of the Zayandehrud Reservoir, located in west-central Iran, under two fifth-generation climate change scenarios called RCP4.5 and RCP8.5. A novel variant of the gravitational search algorithm (GSA), named the adaptive accelerated GSA (AAGSA) is proposed and adopted as the optimizer of the reservoir operation in this paper. The major advancement of the AAGSA against the original GSA is its high exploration capability, allowing the proposal to effectively tackle a variety of difficulties any complex optimization problem can face. The goal of the optimization process is the maximization of the sustainability of supplying the downstream water demands by the reservoir. The optimal results obtained by the original GSA and the proposed AAGSA algorithms suggest that the AAGSA can achieve much more accurate results with much less computational runtime, such that the proposed AAGSA is able to achieve the reservoir operation sustainability index of 98.53% and 99.46%, under RCP4.5 and RCP8.5 scenarios, respectively. These figures are higher than those obtained by the original GSA by 23.5% and 16% under RCP4.5 and RCP8.5, respectively, while the runtime of the proposal is reduced by over 80% in both scenarios, as compared to the GSA, suggesting the high competence of the proposed AAGSA to solve such a high-dimensional and complex real-world engineering problem. [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]

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]

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]

Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations. [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]

Stormwater management in an urban environment is beset by uncertainties about future development. Dynamic strategies must be devised to cope with such uncertain environment. This work proposes a simulation–optimization model that minimizes the costs of low-impact development (LID) measures for mitigating impacts of future urban development on runoff. This paper's methodology is tested in an urban watershed in Tehran, Iran, relying on the stormwater management model (SWMM) coupled with the genetic algorithm (GA) to function as a simulation–optimization method for urban–runoff control by means of LID stormwater control measures. A sensitivity analysis of the calculated optimal solution revealed the impacts the most sensitive LIDs would have on runoff considering a set of plausible future development scenarios in the urban catchment. A comparison of the results from two different scenarios of future development with the existing stormwater system's performance shows the cost increase in redesigning the existing system to make it LID sensitive would equal 20% of the existing system's cost. The additional cost of redesigning the existing system without LID features would be 45% of the existing system's cost. These results demonstrate the importance of assessing the sensitivity of designed units in a stormwater management system and studying the trade-offs between possible decisions and future uncertainties concerning development in the watershed. [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]

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]

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]

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 main purpose of the presented research is to raise the efficiency of pumping stations' operational work by developing a model for reducing energy costs in urban water supply systems. Pumping systems are responsible for a significant portion of the total electrical energy use. Significant opportunities exist to reduce the pumping energy through smart design, retrofitting, and operating practices. Today, considering the increase in pumping energy prices in water conveyance systems, the problem of optimal operation of pumping stations is very actual. The optimal operation of pumping stations was determined using a Genetic Algorithm Optimization (GAO) to achieve the minimum energy cost. The paper presents a novel management model for the optimal design and operation of water pumping systems on a real case study for the town of Gonabad in Iran. To achieve this goal, three days in a year were selected randomly. The results indicate that the proposed mode in conjunction with a GAO is a versatile management model for the design and operation of the real pumping station. Modeling results show that optimization with a GAO reduces power consumption by about 15–20%. [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]

Iran is one of the most energy-consuming countries, especially in the construction sector, and more than 40% of its energy consumption is in the construction sector. Therefore, considering the very high potential of Iran in the field of solar energy, the need to pay attention to providing part of the energy required by buildings by solar energy seems necessary. The study of the effect of climate on the performance of a BIPV has not been done in Iran so far. Also, the use of ranking methods using the weighting of parameters affecting the performance of BIPV has not been done so far. The purpose of this study is to investigate the power supply of BIPV connected to the grid in the eight climates of Iran. Technical–economic–environmental energy analyses were performed by HOMER 2.81 software. In order to study different types of BIPV, four angles of 0°, 30°, 60°, and 90° were considered for the installation of solar cells. The effective output parameters of HOMER software were weighted by the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on the opinion of experts, and it was observed that payback time (year) has the highest weight among the studied criteria. Then, different cities were ranked using the evaluation based on distance from the average solution (EDAS) method. The results showed that Jask is the most suitable and Ramsar is the most unsuitable city. Also, the results of the EDAS method were confirmed by Additive Ratio Assessment (ARAS), Weighted Aggregates Sum Product Assessment (WASPAS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. [ABSTRACT FROM AUTHOR]

For the first time, this work conducted a comparison of two indigenous plants in Iran, namely, Syzygium cumini and Psidium guajava, which were prepared as low-cost adsorbents to remove fluoride contamination from aqueous solution. The results revealed the nonlinearity of the interactive effects and showed that the pH and adsorbent dosage were the most influential factors during fluoride adsorption. The results of characterization exhibited a mesoporous structure of prepared biosorbents; therefore, the adsorption process may involve multiple functional groups, resulting in electrostatic attraction and hydrogen binding between fluoride ions and the biosorbents. In the case of Syzygium cumini, the maximum removal efficiency of 72.5% was obtained under optimum experimental conditions (Co = 6 mg/L, pH = 5, adsorbent dose = 8 g/L, and contact time = 75 min). For the Psidium guajava, the maximum removal efficiency of 88.3% was achieved at a Co of 6 mg/L, adsorbent dose of 6 g/L, initial pH of 5.1, and a contact time of 90 min. Moreover, four consecutive adsorption/desorption cycles with the chemical agent of NaOH solution (0.1 mol/L) showed excellent reusability of the biosorbents. The adsorption isotherm fitted better to the Langmuir model and the kinetic data best accorded with the pseudo-second-order kinetic model for both biosorbents, expressing a monolayer chemisorption process with recorded maximum adsorption capacities of 1.14 and 1.50 mg/g for Syzygium cumini and Psidium guajava, respectively. Therefore, given their removal capacity and potential utility, the prepared biomass could be effective reusable biosorbents to treat water contaminated with fluoride. [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]

Desalination is set to become a major source of drinking water in several Middle Eastern countries over the coming decades. Solar distillation is a simple power-independent method of water desalination, which can be carried out in active or passive modes. This study is among the first attempts to investigate the possibility of desalinating brackish groundwater resources under the threat of saltwater intrusion in the southern areas of Razavi Khorasan province in Iran. For this purpose, a pilot solar distillation unit was constructed to analyze the effects of the unit orientation, depth of the water pool, atmospheric conditions, input salinity, and flow continuity on the solar distillation performance. The results showed that the unit exhibited the highest efficiency when it had a 3 cm deep water pool. It was oriented facing southward while operating a continuous flow for at least 3 days under sunny weather conditions. It was found that among the studied parameters, the unit orientation and pool depth had the greatest impact on the water production performance for this type of water desalination system. Conversely, the water production efficiency was not very sensitive to the input salinity level. Overall, the solar distillation technology was able to reduce the salinity by 99.7% and the hardness by 94.7%. [ABSTRACT FROM AUTHOR]

Although various optimization algorithms have been developed for operational purposes, the development of new optimization algorithms is still an open problem due to the complex system of irrigation canals that must be addressed. Recently, a new algorithm named gray wolf optimization (GWO) has been introduced and applied in different contexts. It mimics the social hierarchy and hunting behavior of gray wolves in nature. In this research, GWO was formulated, developed, and linked to irrigation canal system simulation (ICSS) to schedule water delivery. A fitness (optimization) function was defined according to the standard water delivery performance indicators. Normal and water shortage operational scenarios in the E1R1 Dez canal in Iran were tested and evaluated. The results revealed that GWO is a powerful optimization method and avoids local optimal points when normal conditions exist. However, it has relatively poor performance in water shortage conditions in which there is not enough water. Water depth variations remain inside acceptable margins. Its results were comparable to fuzzy state, action, reward, state, action (SARSA) learning (FSL) in the same canal, showing a value of maximum absolute error (MAE) and integral absolute error (IAE) of 10.7% and 9.2%, respectively, and it can distribute water between turnouts adequately, efficiently, equitably, and dependably in normal conditions. [ABSTRACT FROM AUTHOR]

This study deals with a new approach to tackle the challenges encountered in the filters-based reactive power calculation techniques utilized in the control system of static VAr compensators. In this approach, step and time-varying responses are improved by a combination of differential and filter-based methods. Then, optimum values of the main parameters of the low-pass and band-stop filters are determined considering three criteria: accuracy, speed, and capability for following the time-varying loads. To assess the performance of the proposed method, a new objective function is introduced, calculated for two sets of signals, captured from a real field in Mobarakeh Steel Company in Isfahan/Iran. Then, the proposed and the other reactive power calculation methods are assessed in practice and simulation. Also, the algorithm is implemented using a TMDSCNCD28335 as the processor to assess the real-time feasibility of the method. To do so, the reactive power variation of an electrical arc furnace is emulated in a prototype test bench and the algorithm is run in real-time by the processor during the system operation. [ABSTRACT FROM AUTHOR]

Efficient Asset allocation and investment portfolio selection are among the most critical and challenging issues in investment management and a continuous concern for investors. When investors invest in the capital market, they expect their portfolio to perform well. Therefore, this study determines the optimal stock portfolio of agricultural companies in the Tehran Stock Exchange (TSE). Thirty-two most important agriculture companies in the (TSE), with monthly data from 2014-2020, were selected from Iran's two most essential agriculture industries, the food and beverage industries, and the sugar industry. Two portfolios for the food and beverage industry and sugar industry goals: minimizing portfolio variance and maximizing portfolio return using the Markowitz model with two different scenarios and applying two minimum investment constraints of 1% and optimized maximum investment of 20% without considering these two constraints. The efficiency, variance, and Sharp ratios are also calculated. The results showed that both food and beverage industry portfolios and the sugar industry portfolios became more efficient when optimized to maximize portfolio returns. The result also indicates the food and beverage industry was more efficient than the portfolio of the sugar industry. In this portfolio, the amount of investment for the shares of Salmin Company was 86.7% and for Mehram Company was 13.3%. [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]

The main purpose of the present study is to predict the optimal values of the ultrasound-extraction conditions by employing the response surface method based on Box–Behnken design from Moringa oleifera leaves. The impact of three independent variables, namely the extraction time (5,15, and 25 min), solid to the solvent ratio (60%, 70%, and 80%), and ultrasonic amplitude (20%, 60%, and 100%) on the extraction yield and total phenolic/flavonoid content were studied. Analysis of variance (ANOVA) was employed to estimate the significance of the independent parameters and the model. An extraction time of 15 min, an ultrasonic amplitude of 100%, and a solid to solvent ratio of 80% were found to be optimal values. The findings of this investigation revealed that at optimal independent factors, the values of the extraction yield, total flavonoids, and phenolics content were found to be 83.75 mgL−1,50 mgL−1, and 0.974 g, respectively. The obtained results indicated that BBD-RSM design could effectively be used in the optimization of the ultrasonic-assisted extraction process. To the best of our knowledge, this is the first study assessing the effect of US power, time of extraction, and solid to the solvent ratio on the extraction yield and phenolics content/flavonoids content of Moringa oleifera leaves cultivated in Iran. [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]

One of the most important factors that improve most indicators of poverty is increasing the income of the study population. In the agricultural sector, this can be achieved through proper resource management and appropriate cultivation pattern. The current study presents a mathematical programming model that aims to optimize the Foster, Greer and Turbek (FGT) poverty index by generating higher income by providing an optimal cultivation model. To evaluate the ability of the proposed model, rural areas of Sardasht, Langirat suburbs, Dodangeh and Tashan were selected from Behbahan city. To calculate the poverty line, the method of daily caloric requirement, which is estimated from the nutritional performance matrix, was used. The required data were collected through the Jihad Agricultural Service Centers of this city in the 2017-2018 crop year and the Statistics Center of Iran and analyzed by Excel and GAMS software. The results showed that in the proposed model, while profits increased by 10.47%, the area under wheat and alfalfa decreased and the area under watermelon and sugar beet increased. In this model, the amount of water consumed decreased by about 0.5%. Also, by averaging the FGT index in both current and optimal models, it was found that the numerical value of this index (the value of the objective function) in the whole city has decreased from 3.47 to 0.03. Therefore, the optimal model, while increasing farmers' incomes, has reduced poverty and also reduced the use of scarce water inputs. Therefore, promoting the optimal model obtained in this study is recommended by the Agricultural Jihad Organization. [ABSTRACT FROM AUTHOR]

Reservoirs provide rural and municipal water supply for various purposes such as drinking water, irrigation, hydropower, industrial purposes and recreational activities. Supplying these demands depends strongly on the dam reservoir capacity. Hence, reservoir storage capacity prediction is a determining factor in water resources planning and management, drought risk management, flood risk assessment and management. In the present study, imperialist competitive algorithm as a relatively new socio-political-based global search technique introduced for solving different optimization problems employed to predict reservoir storage capacity of Shaharchay dam located in the Urmia lake basin in northwest of Iran. The high convergence rate of imperialist competitive algorithm along with its capability in finding global optimal is striking aspect of the algorithm. The results obtained from this algorithm were compared with those of Artificial Neural Network. The comparison of the results with the measured ones by means of error measures indicates the superiority of imperialist competitive algorithm over Artificial Neural Network. [ABSTRACT FROM AUTHOR]

Summary: Sustainable power sources, that is, the energy produced as of hydro control, biomass, wind, geothermal, sunlight, and sea resources deliberated as innovative choice intended to create clean energy and crisp water. The majority of the nations have challenged issues proceeding freshwater and power creation, which tends to the utilization of sustainable power source‐controlled desalination frameworks. In this work expanding the clean water accessibility that satisfies the load need, Hybrid Renewable Energy Systems (HRES) on the basis of Reverse Osmosis Desalination (ROD) is structured and then displayed. HRES is getting to be well known for power applications because of advances in sustainable power sources. Here, an innovative calculation based on Social Spider Optimization (SSO) is aimed at explaining the required optimization tasks. The technique referenced is turned out to be powerful, utilizing sustainable power source framework. The proposed system is contrasted with a genuine contextual analysis in the eastern part of Iran, Canary Islands and outcomes demonstrate that it has been successfully utilized thinking about both power quality and cost. Also, the outcomes acquired by the suggested technique are very encouraging to outline the capability and strength of the introduced methodology. [ABSTRACT FROM AUTHOR]

Micro-CHP systems are designed to reduce energy consumption in the present era. In this study, micro-CHP systems with a solar-powered Stirling engine are designed for use in similar buildings in three different climates of Iran. These solar systems are highly dependent on weather conditions in different cities. Modeling and optimizing to reduce costs, the economic dimension of the system, increasing energy storage and reducing the amount of emissions, especially CO2, have been achieved and acceptable results have been obtained. [ABSTRACT FROM AUTHOR]

Successful implementation of construction projects worldwide calls for a set of effective risk management plans in which uncertainties associated with risks and effective response strategies are addressed meticulously. Thus, this study aims to provide an optimization approach with which risk response strategies that maximize the utility function are selected. This selection is by opting for the most appropriate strategies with the highest impact on the project regarding the weight of each risk and budget constraints. Moreover, the risk assessment and response strategy of a construction project in Iran as a case study, based on the global standard of the project management body of knowledge (PMBOK) and related literature, is evaluated. To handle the complexity of the proposed model, different state of the art metaheuristic algorithms including the ant lion optimizer (ALO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO), moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), sine cosine algorithm (SCA), salp swarm algorithm (SSA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO). These algorithms are validated by the exact solver from CPLEX software and compare with each other. One finding from this comparison is the high performance of MFO and HHO algorithms. Based on some sensitivity analyses, an extensive discussion is provided to suggest managerial insights for real-world construction projects. [ABSTRACT FROM AUTHOR]

In recent decades, the forest industry has been growingly expanded due to economic conditions, climate changes, environmental and energy policies, and intense demand changes. Thus, appropriate planning is required to improve this industry. To achieve economic, social and environmental goals, a supply chain network is designed based on a multi-period and multi-product Mixed-Integer Non-Linear Programming (MINLP) model in which the objective is to maximize the profit, minimize detrimental environmental effects, improve social effects, and minimize the number of lost demands. In addition, to improve forest industry planning, strategic and tactical decisions have been implemented throughout the supply chain for all facilities, suppliers and machinery. These decisions significantly help to improve processes and product flows and to meet customers' needs. In addition, because of the presence of uncertainty in some parameters, the proposed model was formulated and optimized under uncertainty using the hybrid robust possibilistic programming (HRPP-II) approach. The ε -constraint technique was used to solve the multi-objective model, and the Lagrangian relaxation (LR) method was utilized to solve the model of more complex dimensions. A case study in Northern Iran was conducted to assess the efficiency of the suggested approach. Finally, a sensitivity analysis was performed to determine the impact of important parameters on objective functions. The results of this study show that increasing the working hours of machines instead of increasing their number, increasing the capacity of some facilities instead of establishing new facilities and expanding the transport fleet has a significant impact on achieving predetermined goals. [ABSTRACT FROM AUTHOR]