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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]

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]

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]