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In the present study, an optimal Proton-Exchange Membrane Fuel Cell-based includes Cooling, Heating, and Power system has been proposed to cover the required energy of the households for cooling, heating and electricity. The system is optimized by a modified version of the Mayfly Optimization (MMFO) algorithm to recover and develop the efficiency of the CCHP system. The innovation is the application of the proposed MMFO algorithm to provide the optimal configuration of the CCHP system in terms of environmentally and economically. the CCHP system is then simulated and its results are compared with NSGA-II from the literature, and also the original MFO method to indicate the method efficiency. The results indicate that the proposed optimal CCHP system offers good results and prominence toward the compared algorithms in both terms of environmental and efficiency.

In this paper, a new effective technique is presented for model identification of the Proton-exchange membrane (PEM) fuel cells by a new hybrid model of Rotor Hopfield Neural Network (RHNN). The concept is to lessen the Error between the empirical output voltage and the modeled produced voltage of the PEM fuel cells based on the proposed hybrid RHNN. To improve the structure of the RHNN model, a new metaheuristic, called collective guidance factor-based Pathfinder algorithm (GFPF) has been proposed. To validate the performance of the proposed model, it is performed to a case study and its results are compared with the original RHNN as two Blackbox models. Simulation results indicate 0.5665% and 0.4141% maximum relative error for testing of the original RHNN-based model and the GFPF-RHNN-based model, respectively which is a good accuracy for PEM fuel cells modeling.

Renewable energy sources play a crucial role in energy production due to the rapidly declining and depletion of conventional energy sources. Solar energy is one of the main sources that have the po...

In this paper, a new optimization algorithm called Adaptive Sparrow Search Algorithm (ASSA) is proposed for optimal model parameters identification of the proton exchange membrane fuel cell (PEMFC) stacks. The proposed ASSA is utilized for minimizing the sum of squared error (SSE) between the empirical stack voltage and the calculated stack voltage by optimal selection of the mentioned parameters in the PEMFC stack. The method is then performed to three case studies including Ballard Mark V, Horizon H-12, and NedStack PS6 under different operating conditions and give 0.82, 5.14, and 0.097 of SEE which is the least value for all three case studies. The results of the algorithm are compared with some reported works in the literature including CGOA, GRA, and basic SSA to show the method prominence. The final results indicated that the proposed ASSA has the best efficiency toward the others.

In this paper, the information gap decision theory is proposed as a solution model for reliable planning in residential smart building by considering the nondeterministic prices of the market and s...

In recent years, the application of renewable energy has been increasingly paid attention to keep environmental clear. One of the key components of this project is the fuel cell industry, which is ...

This paper presents an exergy assessment for a proposed power generation system that is used the organic Rankine cycle to recycle the waste heat from a high-temperature proton exchange membrane fuel cell (HT-PEMFC). To do so, mathematical model for the studied PEMFC along with the water management system have been introduced. Parametric analysis has been directed to study the impact of different economic and thermodynamic parameters, like the fuel cell irreversibility, exergy efficiency, and its work. For optimal designing the PEMFC, its parameters have been optimized by considering three objective functions, i.e. irreversibility, exergy efficiency, and its work. The optimization process has been performed based on a new model of fibrous root optimization algorithm improved. Simulation results of the presented algorithm have been compared with empirical results, genetic algorithm, and the basic of fibrous root optimization algorithm. The optimized values of irreversibility, exergy efficiency, work for the proposed algorithm are achieved 0.012, -0.439, and -0.4993, respectively which has the best values compared with the other analyzed algorithms.

This paper analyzes the efficiency of a high-temperature proton exchange membrane fuel cell (HT-PEMFC) by calculating the output voltage of the cell in different working conditions, using the semi-experimental relationships. The irreversibility and the exergy efficiency of the fuel cell is calculated under different working conditions and the effect of temperature and pressure has been studied. To achieve optimal design for the PEMFC, its parameters are optimized based on irreversibility, exergy efficiency, and its work. The system optimization is applied by a modified version of the Manta Ray Foraging Optimization Algorithm. The suggested algorithm is then compared with other algorithms from the literature and also simulation results and showed a high agreement between the suggested algorithm and the simulation results.

This study presents a techno-economic analysis of an off-grid hybrid photovoltaic (PV) and fuel cell (FC) system. The research is to supply the power of off-grid regions in urban areas in China. Th...

An effective way of increasing the effectiveness, energy use decline, and reducing emissions are the Combined Cooling, Heating, and Power (CCHP) system. In this paper, an optimization procedure bas...

The present study proposes a new efficient methodology for optimal model identification of the Proton-exchange membrane fuel cell (PEMFC) stacks based on an improved version of a Deep Belief Network (DBN). The proposed DBN has been updated by a new metaheuristic to provide the minimum relative error between the experimental output voltage and the network output data during simulation of the nonlinear transient behavior of the Proton-exchange Membrane Fuel Cells (PEMFC). To develop the effectiveness of the DBN, an improved version of the Archimedes optimization algorithm (IAOA) has been developed. The results of training and testing of the proposed method are compared with the original DBN model to indicate the method's effectiveness. Simulations showed 34.0879 and 28.5016 V for the DBN and the suggested DBN-IAOA methods, respectively. This indicates the higher performance of the suggested method toward the original DBN model and its well-organization for modeling the PEMFC stacks.

This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and power forecast techniques where, different prediction horizons are considered from day-ahead to one week forecasting. Obtained results confirm the validity of the developed approach in prediction model for different forecast horizons.

In this paper, a new prediction model is introduced based on hybrid forecast engine and new feature selection. In this model, the load signal is filtered by feature selection to filter out the best candidates. Then, the proposed forecast engine is predicted the output of feature selection. In this model, the weights of proposed forecast engine are optimised by an intelligent algorithm to increase its accuracy. Effectiveness of the proposed method is applied over real-world engineering test case and compared with other different well-known methods. Obtained results proof the validity of the proposed method.

A novel methodology for optimum sizing of a hybrid energy (HE) system is presented in this paper to supply the driving force of a locomotive. The HE system includes a lithium-ion battery along with a polymer electrolyte membrane (PEM) fuel-cell. The idea behind this paper is to minimize the HE system’s total cost under the PEM fuel-cell state of charge (SoC) constraint and capacity constraint of the battery. The minimization in this study is performed by an improved version of the Locust Swarm (LS) optimization algorithm (ILS). The algorithm uses oppositional learning and chaos mechanism to resolve the premature convergence and speed of the algorithm along with escaping from the local optimum point. The results of the final case study have been done for analyzing the locomotive speed demand, the average power demand, and the locomotive slant. A comparison of the outcomes of the suggested ILS algorithm with the standard LS algorithm and Particle swarm optimizer (PSO) from the literature and the results showed that in a maximum slope (2%), the total cost of the HE system for the suggested ILS algorithm, the basic LS algorithm, and the PSO algorithm are 3.8 × 10 6 $, 4.43 × 10 6 $, and 4.86 × 10 6 $, respectively which indicated that the achieved overall expense for the suggested ILS gives the best amount and the results are carried out to verify the superiority of the proposed method in solving a challenging real-world problem.

This paper presents a micro Combined Cooling, Heating, and Power system coupled with a 5-kW Proton-Exchange Membrane Fuel Cell stack as primary mover to supply the households’ requirements for cooling, heating and electricity. A new optimal methodology based on a modified bio-inspired optimization algorithm, called Developed Marine Predators Optimization Algorithm is introduced to improve the performance of the Combined Cooling, Heating, and Power system in terms of environment, thermodynamics, and economics. The reason for designing the developed version is to improve the algorithm efficiency in terms of accuracy, consistency, and convergence. The main idea is to maximize the economic performance and to minimize the annual cost of the Combined Cooling, Heating, and Power system during 20 years of life-span. For solving optimization problem, the proposed Developed Marine Predators Optimization Algorithm is used. After simulation, the results are compared with some other bio-inspired-based methods including NSGA-II and the basic Marine Predators Optimization Algorithm to indicate its higher efficiency. The results show that the maximum annual GHG reduction with 5.61e6 g belongs to the proposed DMPA with 52.6% exergy efficiency and 23.79e3 $ annual cost. The results also indicate that the final value of the exergy efficiency base on optimization is 32.57% that largely give better results comparing with the normal mode without optimization.

In this paper, the exergy analysis of a proposed power generation system based on high-temperature proton exchange membrane fuel cell (HT-PEMFC) has been proposed. The analyzed system includes the organic Rankine cycle to recover the wasted heat. To analyze the system, a mathematical model of the considered PEMFC has been presented and the water management system is investigated. Parametric analysis is provided to investigate the effect of the various thermodynamic and economic parameters such as exergy, irreversibility, and the work. Therefore, the three mentioned parameters have been optimized for improving the PEMFC design. To achieve an efficient optimized system, a new design of Farmland Fertility Optimization (FFO) is proposed. Final results of the algorithm are compared with experimental results, the genetic algorithm, and the basic FFO and the optimized values of irreversibility, exergy efficiency, work for the proposed algorithm are achieved 0.011, −0.446, and −0.462, respectively that are the best results toward the other compared algorithms.

This paper proposes a new optimization methodology for presenting an efficient result of the hydrogen consumption and speed trajectory in a PEM fuel cell (PEMFC)-based locomotive energy system for transportation applications. After modeling the system, it has been linearized for simplification. The efficiency of the hydrogen consumption is maximized based on a new version of the Pathfinder (IPF) algorithm and the speed trajectory. The reason for designing IPF is to resolve the local optimum and fast convergence shortcomings during the maximization. Two scenarios including normal and extreme characteristics are performed to the system. The simulation results specified that the total traction energy for the normal mode and the extreme mode is 4.27 MJ and 4.80 MJ, respectively, and the optimal hydrogen consumption for these two modes are 0.0764 kg and 0.0628 kg, respectively. The final results showed that the locomotive improves significantly the efficiency of the system for hydrogen saving.

In this work, optimal expansion planning is modeled mathematically as an optimization problem that considers the optimal combination of different generators, energy devices, and transmission lines. A wide range of research was conducted for several years to identify a solution for this problem. In this work, a single hub with various energy sources, i.e., gas, heaters, and power, is considered for resolving the optimum extension planning problem. A hub is generally composed of a varying numbers of combined heat and power units, kilns, and power transformers, which are in charge of satisfying heat and electricity demands throughout a specified time. Natural gas, power, and wood chips are the inputs to the hub. The target of the optimization problem is to reduce the release of polluting gases and improve the overall energy expense. Scentless transformation is used for simulating the indeterminacy and correlated electrical and heat demands. Initially, the problem is simulated as a limitation optimization problem; then, it is modified to a limit-less problem based on the Lagrange technique. The particle swarm optimization algorithm is used to determine a solution for the optimization problem, which resulted in the identification of the optimum quantities of the input powers along with the optimal number of devices. Obtained results of PWin, PGin, and PEin in two case studies, demonstrate the validity of proposed model while, the interrelationship among demands increases, and the standard deviation of Z increases and the expected value of y reduces. The proposed approach is applied on two engineering test cases; the solution for the optimal expansion problem is determined for time durations of two and five years. The obtained results demonstrate the validity of the proposed model.

The aim of this paper is simultaneous minimization of hydrothermal units to reach the best solution by employing an improved artificial bee colony (ABC) algorithm in a multi-objective function consisting of economic dispatch (ED) considering the valve-point effect and pollution function in power systems in view of the hot water of the hydro system. In this type of optimization problem, all practical constraints of units were taken into account as much as possible in order to comply with the reality. These constraints include the maximum and minimum output power of units, the constraints caused by the balance between supply and demand, the impact of pollution, water balance, uneven production curve considering the valve-point effect and system losses. The proposed algorithm is applied on the studied system, and the obtained results indifferent operating conditions are analyzed. To investigate in various operating conditions, different load profiles in 12 h are taken into account. The obtained results are compared with those of the other methods including the genetic algorithm (GA), the Basu technique, and the improved genetic algorithm. Fast convergence is one of this improved algorithm features.

In this paper a passive Neuro-wavelet on the basis of islanding detection procedure for grid-connected inverter-based distributed generation has been developed. Moreover, the weight parameters of neural network are optimized by Interactive Honey Bee Matting optimization (IHBMO) to increase the efficiency of the capability of suggested procedure in tendered problem. Islanding is the situation where the distribution system including both distributed generator and loads is disconnected from the major grid as a consequence of lots of reasons such as electrical faults and their subsequent switching incidents, equipment failure, or pre-planned switching events like maintenance. The suggested method uses and combines wavelet analysis and artificial neural network together to detect islanding. It can be used in removing discriminative characteristics from the acquired voltage signals. In passive schemes have a large Non Detection Zone (NDZ), concern has been raised on active method because of its lowering power quality impact. The main focus of the proposed scheme is to decrease the NDZ to as close as possible and to retain the output power quality fixed. The simulations results, performed by MATLAB/Simulink, demonstrate that the mentioned procedure has a small non-detection zone. What is more, this method is capable of detecting islanding precisely within the least possible amount of standard time.

This paper presents a new strategy based on Multi-stage Fuzzy (MSF) PID controller for damping Power System Stabilizer (PSS) in multi-machine environment using Modified Shuffled Frog Leaping (MSFL) algorithm. The proposed technique is a new meta-heuristic algorithm which is inspired by mating procedure of the honey bee. Actually, the mentioned algorithm is used recently in power systems which demonstrate the good reflex of this algorithm. Also, finding the parameters of PID controller in power system has direct effect for damping oscillation. Hence, to reduce the design effort and find a better fuzzy system control, the parameters of proposed controller is obtained by MSFL that leads to design controller with simple structure that is easy to implement. The effectiveness of the proposed technique is applied to Single machine connected to Infinite Bus (SMIB) and IEEE 3-9 bus power system. The proposed technique is compared with other techniques through ITAE and FD.

Introduction: The present research aims to study and compare intelligence in children of consanguineous and non-consanguineous marriages and its relation with Holland’s personality types. Methodology: This is a descriptive study and uses an ex-post facto design. The statistical population is comprised of all high school students of Tehran province in the school year 2010-2011. The sample size includes 1,221 students, 628 of whom are children of consanguineous marriages and 593 are children of non-consanguineous marriages. The researchers employed Raven’s Progressive Matrices Test and Holland Code (RIASEC) Test for data collection. Findings: T-test results indicate that there is no signifiant difference in intelligence between children of consanguineous marriages and those of non-consanguineous marriages. However, descriptive data indicate that the mean IQ in children of consanguineous marriages is one unit lower than those of non-consanguineous marriages. Also, the results of variance analysis reveal that the difference in intelligence between different personality types is signifiant. Tukey’s post hoc test results indicate that students with an investigative personality type have a higher intelligence compared with those with other personality types.

Objective: This study aims to compare the effectiveness of Family Therapy Based on metacognitive therapy and Mindfulness on Marital Intimacy among divorce applicant clients. Method: The design of the study is an experimental research with pretest and posttest with a control group. The study population includes all divorce applicant clients referring to well-being and counseling agencies of Sanandaj city, located in Kurdistan province, in 2011. Sixty-six clients were selected from among the population using random sampling and then, they were divided into three groups of 22 using random assignment method. The independent variable of marital intimacy was measured among divorce applicant clients (pretest) using the marital intimacy questionnaire (Oliya et al., 2006) and after intervening two experimental groups for 8 sessions, the dependent variable was again calculated for three groups (posttest). Multivariate analysis of covariance was used to examine the effect of pretest and treatment. Findings: The results of multivariate covariance analysis showed that there is a signifiant difference between divorce applicant clients in experimental and control groups in terms of the dependent variable of intimacy (P< 0/0001). Conclusion: Therefore, all hypotheses concerning the effectiveness of family therapy based on metacognitive therapy and mindfulness on marital intimacy among divorce applicant clients were confimed. The results also showed that the two methods of mindfulness and metacognitive therapy have almost the same effect.

In this paper, we have studied the abilities of shark in the sea environment. Sharks are mysterious and misunderstood creatures that have fascinated the people for generations. This trunk will introduce students to the biology and behaviors of sharks. The main purpose of this paper is to discover this animal’s abilities, especially its olfactory bulbs, and the reasons which makes the shark a successful hunter. After studying the characteristics of this animal, we will analyze each of them to answer: “how we can use these capabilities in our life?” Accordingly, we will focus on shark’s swimming and olfactory bulbs to achieve mathematical model. This model can be used in several engineering and other problems in human life.