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

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

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

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]

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]

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

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

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

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

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]

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]

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]

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

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