Optimizing a New Configuration of a Proton Exchange Membrane Fuel Cell Cycle With Burner and Reformer Through a Particle Swarm Optimization Algorithm for Residential Applications

2019 ◽  
Vol 16 (4) ◽  
Author(s):  
Mehdi Yousefi ◽  
M. A. Ehyaei ◽  
Marc A. Rosen
Keyword(s):  
Particle Swarm Optimization ◽  
Fuel Cell ◽  
Electricity Generation ◽  
Water Flow Rate ◽  
Particle Swarm ◽  
Exergy Efficiency ◽  
Proton Exchange ◽  
Swarm Optimization ◽  
Cost Of Electricity ◽  
The Cost

Abstract The energy, exergy, and economic aspects are analyzed of a cycle consisting of a polymer fuel cell, a burner, a reformer, and a heat exchanger. Water is used for cooling the fuel cell, and the heated water is used for domestic consumption. The exergy and energy efficiencies of the cycle are calculated, and the effects of various cycle parameters on the exergy and energy efficiencies are investigated. To maximize the exergy efficiency while minimizing the cost of electricity generation by the fuel cell, the particle swarm optimization (PSO) algorithm is utilized. The results show that increasing the cooling water flow rate has the greatest effect on increasing the energy efficiency of the cycle, while increasing the burner temperature has the greatest effect on increasing the exergy efficiency of the cycle. Moreover, it is shown via multi-objective optimization of the proposed cycle that the exergy efficiency of the cycle increases by 31% and the cost of electricity generation decreases by 18% by applying optimized parameters.

Particle Swarm Optimization for Identification of PEMFC Generation System Fuzzy Model

2012 ◽  
Vol 588-589 ◽  
pp. 260-263
Author(s):  
Yuan Ren ◽  
Zhi Dan Zhong ◽  
Hong Xiao Liu ◽  
Xiao Hui Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Pem Fuel Cell ◽  
Proton Exchange ◽  
Cell Generation ◽  
Generation System ◽  
Swarm Optimization

This paper proposes particle swarm optimization (PSO) for identification of the Proton Exchange Membrane Fuel Cells (PEMFC) generation systems fuzzy model. The PEM fuel cell generation system efficiency decreases as its output power increases. Thus, an optimum efficiency should exist and should result in a cost-effective PEM fuel cell generation system. The PEMFC generation system cost and efficiency fuzzy model were build, we use the PSO as an optimization engine to indentify the fuzzy model. The simulation results were presented and the results show that we may minimize the total cost of the generation system by using the PSO.

scholarly journals Performance Analysis and Optimization of a High-Temperature PEMFC Vehicle Based on Particle Swarm Optimization Algorithm

Membranes ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 691
Author(s):  
Yanju Li ◽  
Zheshu Ma ◽  
Meng Zheng ◽  
Dongxu Li ◽  
Zhanghao Lu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
High Temperature ◽  
Fuel Cell ◽  
Power Density ◽  
Proton Exchange Membrane ◽  
Particle Swarm ◽  
Proton Exchange ◽  
Swarm Optimization ◽  
Output Performance ◽  
Exchange Membrane

In this paper, a high-temperature proton exchange membrane fuel cell (HT-PEMFC) model using the polybenzimidazole membrane doped with phosphoric acid molecules is developed based on finite time thermodynamics, considering various polarization losses and losses caused by leakage current. The mathematical expressions of the output power density and efficiency of the HT-PEMFC are deduced. The reliability of the model is verified by the experimental data. The effects of operating parameters and design parameters on the output performance of the HT-PEMFC are further analyzed. The particle swarm optimization (PSO) algorithm is used for the multi-objective optimization of the power density and efficiency of the HT-PEMFC. The results show that the output performance of the optimized HT-PEMFC is improved. Then, according to the different output performance of the low-temperature proton exchange membrane fuel cell (LT-PEMFC), HT-PEMFC, and optimized HT-PEMFC, different design schemes are provided for a fuel cell vehicle (FCV) powertrain. Simulation tests are conducted under different driving cycles, and the results show that the FCV with the optimized HT-PEMFC is more efficient and consumes less hydrogen.

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

Comparison of SVC and TCSC Installation in Transmission Line with Loss Minimization and Cost of Installation via Particle Swarm Optimization

2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Objective Function ◽  
Transmission Loss ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
The Cost ◽  
Thyristor Controlled Series Compensator

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

scholarly journals Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2000
Author(s):  
Jin-Hwan Lee ◽  
Woo-Jung Kim ◽  
Sang-Yong Jung
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Cost Function ◽  
Particle Swarm ◽  
Torque Ripple ◽  
Rate Of Change ◽  
Swarm Optimization ◽  
Traction Motor ◽  
Target Performance ◽  
The Cost

This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optimal solutions. To ensure the robustness of the determined optimal solution, RePSO employs the rate of change of the cost function. When this rate is high, the cost function appears as a steep curve, indicating low robustness; in contrast, when the rate is low, the cost function takes the form of a gradual curve, indicating high robustness. For verification, the performance of the proposed algorithm was compared with those of the conventional methods of robust particle swarm optimization and explorative particle swarm optimization with a Gaussian basis test function. The target performance of the traction motor for the optimal design was derived using a simulation of vehicle driving performance. Based on the simulation results, the target performance of the traction motor requires a maximum torque and power of 294 Nm and 88 kW, respectively. The base model, an 8-pole 72-slot permanent magnet synchronous machine, was designed considering the target performance. Accordingly, an optimal design was realized using the proposed algorithm. The cost function for this optimal design was selected such that the torque ripple, total harmonic distortion of back-electromotive force, and cogging torque were minimized. Finally, experiments were performed on the manufactured optimal model. The robustness and effectiveness of the proposed algorithm were validated by comparing the analytical and experimental results.

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