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Rotating dissolved air flotation (RDAF) has been utilized for several decades in paper‐recycling wastewater treatment; however, it has rarely been addressed in the literature, which makes research into this system challenging and complicated. However, in this work, a full‐scale industrial wastewater treatment system for a paper‐recycling mill in Mazandaran province, Iran, was evaluated. Experiments indicated that under the same wastewater and chemical conditions, there are differences in the removal efficiencies. This finding was investigated by conducting simulation in ANSYS CFX R18.0 and experimentation simultaneously. Thus, the main purpose of this research was to optimize the operation to achieve the highest removal efficiencies in a cost‐effective manner. For simulation, the flow was considered as a two‐phase homogeneous flow with a continuous solid–fluid phase, which discharges into the separation zone at different scenarios. Particles with diameters of 250, 500 and 1000 μm were introduced at four frame rotating speeds of 0.125, 0.25, 0.5 and 0.75 rpm. Measured qualitative parameters of the effluent included COD, BOD, TSS, TDS, conductivity and turbidity, whose removal efficiency ranges were (32.2%–54.8%), (32.7%–54.47%), (94.5%–97.1%), (0.47%–0.55%), (0.48%–0.59%) and (more than 55%), respectively. Moreover, the optimum rotating speeds of the frame for particles with diameters of 250, 500 and 1000 μm were obtained as 0.25, 0.5 and 0.125 rpm, respectively; these values were compared and found to agree with the experimental results. [ABSTRACT FROM AUTHOR]

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]

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]

This paper presents a multipurpose optimization algorithm (MOA) to optimize crop patterns under climate change, minimizing water use and maximizing crop revenue while enforcing food security and regional water security constraints. An application of the MOA yields a total of 12 Pareto fronts for 20-year horizons centered on 2030, 2050, 2070, and 2090 under representative concentration pathways (RCPs) 2.6, 4.5, and 8.5, each of which is associated with specific land use conditions. The results show that crop production must increase due to population growth. However, climate projections for the study region in eastern Iran indicate unsuitable conditions to support the incremental production. This paper's optimization results show that 89%, 73%, and 48% of optimal crop production are achievable considering food-safety constraints in 20-year periods centered on 2050, 2070, and 2090, respectively. This paper's results indicate that revenue would increase, water use would decline, and environmental sustainability would be reached in the study area under the optimized cropping patterns. [ABSTRACT FROM AUTHOR]

In this study, a new water and power cogeneration plant, employing photovoltaic (PV) and photovoltaic–thermal (PVT) panels simultaneously, is designed and optimized for a village struggling to provide energy and potable water in Iran. The system includes a solar energy unit to generate clean electricity and heat, and a reverse osmosis unit to produce drinking water. Techno-economic optimization is performed by implementing a genetic algorithm, and a comprehensive water and energy management strategy is designed and presented in detail, expandable for future works. A new method, the logarithmic model, is used to calculate the depth of discharge (DOD) of lithium-ion batteries, which was previously a fixed and predetermined value in previous papers. Various indices, the constraints of the optimization process, are also introduced to measure the reliabilities of different units. The effects of the system components on total cost are investigated and a comprehensive sensitivity analysis is performed to find the best solution to increase the penetration of renewable-energy systems. The results reveal that considering the depth of discharge of batteries and water storage tank capacity as decision variables reduces the system's life-cycle cost (by 5.1% for changeable DODs). Furthermore, the simultaneous use of PV and PVT panels decreases the life-cycle cost considerably by ≤19% compared with the use of only PVT panels. Additionally, the cost of the battery causes a decrease in the cost of electricity storage and the cost of producing and storing fresh water. [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]

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]

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]

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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]

Purpose: In emergency services, maximizing population coverage with the lowest cost at the peak of the demand is important. In addition, due to the nature of services in emergency centers, including hospitals, the number of servers and beds is actually considered as the capacity of the system. Hence, the purpose of this paper is to propose a multi-objective maximal covering facility location model for emergency service centers within an M (t)/M/m/m queuing system considering different levels of service and periodic demand rate. Design/methodology/approach: The process of serving patients is modeled according to queuing theory and mathematical programming. To cope with multi-objectiveness of the proposed model, an augmented ε-constraint method has been used within GAMS software. Since the computational time ascends exponentially as the problem size increases, the GAMS software is not able to solve large-scale problems. Thus, a NSGA-II algorithm has been proposed to solve this category of problems and results have been compared with GAMS through random generated sample problems. In addition, the applicability of the proposed model in real situations has been examined within a case study in Iran. Findings: Results obtained from the random generated sample problems illustrated while both the GAMS software and NSGA-II almost share the same quality of solution, the CPU execution time of the proposed NSGA-II algorithm is lower than GAMS significantly. Furthermore, the results of solving the model for case study approve that the model is able to determine the location of the required facilities and allocate demand areas to them appropriately. Originality/value: In the most of previous works on emergency services, maximal coverage with the minimum cost were the main objectives. Hereby, it seems that minimizing the number of waiting patients for receiving services have been neglected. To the best of the authors' knowledge, it is the first time that a maximal covering problem is formulated within an M (t)/M/m/m queuing system. This novel formulation will lead to more satisfaction for injured people by minimizing the average number of injured people who are waiting in the queue for receiving services. [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 objective of this paper is to provide an efficient framework for effluent trading in river systems. The proposed framework consists of two pessimistic and optimistic decision-making models to increase the executability of river water quality trading programs. The models used for this purpose are (1) stochastic fallback bargaining (SFB) to reach an agreement among wastewater dischargers and (2) stochastic multi-criteria decision-making (SMCDM) to determine the optimal treatment strategy. The Monte-Carlo simulation method is used to incorporate the uncertainty into analysis. This uncertainty arises from stochastic nature and the errors in the calculation of wastewater treatment costs. The results of river water quality simulation model are used as the inputs of models. The proposed models are used in a case study on the Zarjoub River in northern Iran to determine the best solution for the pollution load allocation. The best treatment alternatives selected by each model are imported, as the initial pollution discharge permits, into an optimization model developed for trading of pollution discharge permits among pollutant sources. The results show that the SFB-based water pollution trading approach reduces the costs by US$ 14,834 while providing a relative consensus among pollutant sources. Meanwhile, the SMCDM-based water pollution trading approach reduces the costs by US$ 218,852, but it is less acceptable by pollutant sources. Therefore, it appears that giving due attention to stability, or in other words acceptability of pollution trading programs for all pollutant sources, is an essential element of their success. [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]

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]

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]

In the conventional zone-based hedging rule, as a common approach, the rationing factors are changed from one zone to another at once, which is not proper for actual reservoir operation. Hence a fuzzy-rule-based approach is employed to create a transition zone to assign rationing factors. This remedy causes gradual variation in the rationing factors and mitigates severe water shortages during drought periods. In this paper, a monthly simulation model linked to an evolutionary algorithm is developed. The proposed model was applied to the Zohre multireservoir system in southern Iran. There are two objective functions to supply minimum flow and agriculture demands over a long-term simulation period. Therefore, a multiobjective particle swarm optimization (MOPSO) algorithm was applied. The results showed that annual and long-term modified shortage index (MSI) values have been improved compared to the current conventional hedging rule. Implementation of the proposed hedging rule for the case study results in improving at least 34 and 21% in the maximum MSI, respectively, for the annual minimum flow and agriculture shortages. [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]

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]

In this paper, a new game theoretic methodology is developed for interbasin water transfer management with regard to economic, equity, and environmental criteria. The main objective is to supply the competing users in a fair way, while the efficiency and environmental sustainability criteria are satisfied and the utilities of water users are incorporated. Firstly, an optimization model is developed to proportionally allocate water to the competing users in water donor and receiving basins based on their water demands. Secondly, for different coalitions of water users, the water shares of the coalitions are determined using an optimization model with economic objectives regarding the physical and environmental constraints of the system. In order to satisfy water-quality requirements, the impacts of decreasing the instream flow in donor basin are estimated using a water-quality simulation model, and the required treatment levels for effluents discharged into the river, downstream of the water transfer point are determined. Finally, to achieve equity and to provide sufficient incentives for water users to participate in the cooperation, some cooperative game theoretic approaches are utilized for reallocation of net benefits to water users. This model is applied to a large-scale interbasin water allocation problem including two different basins struggling with water scarcity in Iran. The results show that this model can be utilized as an effective tool for optimal interbasin water allocation management involving stakeholders with conflicting objectives subject to physical and environmental constraints. [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]

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]

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]

Purpose: This study aims to design a proper supply chain network for the vaccine industry in Iran, which considers several features such as uncertainties in demands and cost, perishability of vaccines, wastages in storage, limited capacity and different priorities for demands. Design/methodology/approach: This study presents a mixed-integer linear programming (MILP) model and using a robust counterpart approach for coping with uncertainties of model. Findings: The presented robust model in comparison with the deterministic model has a better performance and is more reliable for network design of vaccine supply chain. Originality/value: This study considers uncertainty in the network design of vaccine supply chain for the first time in the vaccine context It presents an MILP model where strategic decisions for each echelon and tactical decisions among different echelons of supply chain are determined. Further, it models the difference between high- and low-priority demands for vaccine. [ABSTRACT FROM AUTHOR]

Point predictions of hydroclimatic processes through nonlinear modeling tools are associated with uncertainty. The main goal of this research was to construct prediction intervals (PIs) for nonlinear artificial neural network (ANN)-based models of evaporation and the standardized precipitation index (SPI). These are two critical indicators for climate for four stations in Iran (i.e., Tabriz, Urmia, Ardabil, and Ahvaz) to qualify their predicted uncertainty values (UVs). We used classical techniques of bootstrap (BS), mean variance estimation (MVE), and Delta, as well as an optimization-based method of lower upper bound estimation (LUBE), to construct and compare the PIs. The wavelet-based denoising method was also adopted to denoise input data, enhancing the modeling performance. The obtained results indicate the ability of the BS and LUBE methods to estimate the uncertainty bound. The Delta method mostly failed to find the desired coverage due to its narrow PIs. On the other hand, the MVE method, due to its wide bound, did not convey valuable information about uncertainty. According to the obtained results, denoising the input vector could enhance the PI quality in the modeling of the SPI by up to 76%. It was more prominent than reducing the UV for evaporation models, which was observed the most at the Ardabil station, up to 30%. The inherently more random nature of drought than the evaporation process was interpreted as the cause of this reaction. From the results, Urmia station seems the riskiest regarding drought ventures. Significance Statement: The point predictions of evaporation and precipitation (in the form of SPI) are subject to uncertainty. The best way is to provide an area with the highest contingency as a prediction interval. The reduction in the width of such an interval leads to increased confidence in explaining and predicting these processes. We investigated different methods and found that by utilizing the optimization-based method for denoised inputs, uncertainty values of the output were conveyed better. Additionally, we concluded that the more random the process, the greater its uncertainty. A primary sense of the drought risk was made from the uncertainty perspective. [ABSTRACT FROM AUTHOR]

Unsupervised clustering methods are used as data-driven models for mapping of mineral prospectivity (MPM). One of the challenging problems for modeling high potential mineral prospects using these algorithms is to select proper cluster centroids in order to locate the same geospatial objects into reasonable clusters. To overcome this kind of problems arise from traditional clustering algorithms (e.g. K-means), the combination of harmony search (HS) and artificial bee colony (ABC) metaheuristic optimization algorithms with K-means have been firstly developed in this study which can be used for selection of optimum cluster centroids for highlighting favorable targets in prospecting stage of mineral explorations. This stage is critically important for exploration of undiscovered mineral deposits, because the time and cost are two substantial factors in regional-scale exploration programs. This study stated that the applying hybrid data-driven clustering models (e.g., HS-KM and ABC-KM) are operational tools for identifying high-favorable areas linked to porphyry Cu systems in prospecting stage. Moreover, the results demonstrated that the properly selection of geospatial exploration features has strong impact on the modeling of mineral prospectivity in Kerman porphyry belt, Iran and cause significant decrease in systemic uncertainty linked to MPM. [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 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]

Combined simulation-optimization models have been widely used to address the management of water resources issues. This paper presents a simulation-optimization model for conjunctive use of surface water and groundwater at a basin-wide scale, the Zayandehrood river basin in west central Iran. In the Zayandehrood basin, in the past 10 years, a historical low rainfall in the head of the basin, combined with growing demand for water, has triggered great changes in water management at basin and irrigation system level. The conjunctive use model that coupled numerical simulation with nonlinear optimization is used to minimize shortages of water in meeting irrigation demands for four irrigation systems. Constraints guarantee the maximum/minimum cumulative groundwater drawdown and maximum capacity of irrigation systems. A support vector machines (SVMs) model is developed as a simulator of surface water and groundwater interaction model while a genetic algorithm (GA) is used as the optimization model. Conjunctive use model runs for three scenarios. Results show that the accuracy of SVMs as a simulator for surface water and groundwater interaction model is good and that it is possible to decrease the water shortage for irrigation systems with application of proposed SVMs-GA model. [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]

• 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]

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]

In this paper, two cooperative and non-cooperative methodologies are developed for a large-scale water allocation problem in Southern Iran. The water shares of the water users and their net benefits are determined using optimization models having economic objectives with respect to the physical and environmental constraints of the system. The results of the two methodologies are compared based on the total obtained economic benefit, and the role of cooperation in utilizing a shared water resource is demonstrated. In both cases, the water quality in rivers satisfies the standards. Comparing the results of the two mentioned approaches shows the importance of acting cooperatively to achieve maximum revenue in utilizing a surface water resource while the river water quantity and quality issues are addressed. [ABSTRACT FROM AUTHOR]

Water resources management in semiarid regions with low precipitation and high potential of evapotranspiration is a great challenge for managers and decision makers. In those regions, both sources of water should be managed conjunctively so as to minimize shortages of water in dry seasons. In conjunctive use, the difficulty increases as one must represent the response of both systems interactions, and develop management strategies that simultaneously address surface water and aquifer regulation. This paper focuses on the simulation-optimization for conjunctive use of surface water and groundwater on a basin-wide scale, the Najafabad plain in west-central Iran. A trained artificial neural network model is developed as a simulator of surface water and groundwater interaction while a genetic algorithm is developed as the optimization model. The main goal of the simulation-optimization model is to minimize shortages in meeting irrigation demands for three irrigation systems subject to constraints on the control of cumulative drawdown of the underlying water table and maximum capacity of surface irrigation systems. To achieve the main goal, three scenarios are presented. Results of the proposed model demonstrate the importance of the conjunctive use approach for planning the management of water resources in semiarid regions. [ABSTRACT FROM AUTHOR]

In recent years, uneven distribution of available water resources as well as increasing water demands and overexploiting the water resources have brought severe need for transferring water from basins having sufficient water to basins facing water shortages. Therefore, optimal allocation of shared water resources in water transfer projects, considering the utilities of different stakeholders, physical limitations of the system and socioeconomic criteria is an important task. In this paper, a new methodology based on crisp and fuzzy Shapley games is developed for optimal allocation of inter-basin water resources. In the proposed methodology, initial water allocations are obtained using an optimization model considering an equity criterion. In the second step, the stakeholders form crisp coalitions to increase the total net benefit of the system as well as their own benefits and a crisp Shapley Value game is used to reallocate the benefits produced in the crisp coalitions. Lastly, to provide maximum total net benefit, fuzzy coalitions are constituted and the participation rates of water users to fuzzy coalitions are optimized. Then, the total net benefit is reallocated to water users in a rational and equitable way using Fuzzy Shapley Value game. The effectiveness of this method is examined by applying it to a large scale case study of water transfer from the Karoon river basin in southern Iran to the Rafsanjan plain in central Iran. [ABSTRACT FROM AUTHOR]

Water resources planning and management at basin scale is such a large and complex problem that makes it essential to use effective modeling tools in order to obtain an optimum plan for river basins development. In this paper, a methodology is presented for optimized design and operation of the upstream Sirvan basin in Iran. The model proposed integrates MODSIM generalized river basin network flow model, with the capability of simulating various characteristics and features of water resources in a river basin, and Particle Swarm Optimization (PSO) algorithm. In the developed PSO-MODSIM model, the size of planned dams and water transfer systems, as design variables, and the relative priorities for meeting reservoir target storages, as operational variables, are varied and evolved using PSO algorithm. MODSIM is called to simulate the system performance and to evaluate the fitness of each set of those design and operational variables with respect to the model's objective function. The PSO objective function is to maximize the total net benefit consisting of benefits due to supplying water to different types of water uses and construction costs of dams and water transfer and/or pumping systems. Varying the design and operational variables in MODSIM 8 is done using the MODSIM's custom coding feature in VB.NET routine. The PSO-MODSIM model is used to size the planed water storage and transfer components of a river basin system and to allocate water resources optimally over time and space among competing demands, considering coordinated operation of the system components. The model results has been analyzed for different scenarios of water transfer from Sirvan to neighboring basins. [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]

Waste electrical and electronic equipment is one of the essential pollutants with significant negative impacts on the environment and human health. In this study, a multi-period mixed-integer linear programming model is developed to design a closed loop supply network for the management of electrical and electronic equipment by explicitly considering the economic and environmental sustainability with a budget constraint. Different recycling options like refurbishing, disassembling, remanufacturing, and disposal centers are considered in the design of the network. The model minimizes the total costs of the network and the total carbon emission tax. The literature review shows that the introduced model is more comprehensive than the other existing models because it simultaneously determines the location of facilities, the capacity of facilities, type of manufacturing technologies, variety of vehicles, and the allocation and transportation of materials and products. The model was applied to a real-life case study in Iran and could provide a profit of IRR 245,509,165 M during the planning periods. The carbon tax policy with different echelons is applied to control the environmental impacts in which the carbon tax increases as carbon emissions increase. The results show a nearly linear relationship between the total costs of the network and the carbon tax. The carbon tax of equal to or more than 10,800 IRR/t CO2 can be a deterrent factor for the electrical and electronic equipment manufacturer in Iran to diminish emissions through investment in green technologies. [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]

Water is a component that makes up about 60%-70% of the adult human body weight and is the basic ingredient for life after oxygen. Satisfactory and high quality water is essential for human life. Nowadays in water treatment, disinfection is considered as one of the main methods for chemical water treatment. Currently the use of chlorine for disinfection is the most common method in the world including Iran because of low cost and high germicidal power in water distribution networks and good residual effect. Dosage of chlorine is different depending on the circumstances of inlet raw water including pH, temperature, and amount of suspended solids and the injection of chemicals such as preliminary chlorine, coagulant (aluminum sulfate), and coagulant assistant (polyelectrolyte). Free residual chlorine is used to disinfect water sources and water transfer lines to the houses of citizens. In this paper, adaptive neuro-fuzzy inference system (ANFIS) was used to optimize the amount of injected secondary chlorine in clear water. For modeling, exploitation data are divided into two categories (training and testing). Eventually, the results of modeling were compared with experimental data that demonstrates fine data compliance, as correlation coefficient in neuro-fuzzy inference network was 0.95 for training data, and 0.93 for testing data. [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]