Optimizing PEMFC model parameters using dragonfly algorithm: a performance study

Hamal, N.S.; Isa, Zainuddin Mat; Nayan, Norkharziana Mohd; Arshad, Mohd Hafiz; Kajaan, Nor Ashbahani Mohamad

doi:10.1049/cp.2018.1352

Cited by 9 publications

(6 citation statements)

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“…Electrons released were then passed through the external circuit and collected by the cathode, while hydrogen atom penetrates the membranes [30] to the cathode [31] to form water with oxygen from the surroundings:…”

Section: Electron Extraction From An Oxidizing Substratementioning

confidence: 99%

Electrical energy production from plant biomass: an analysis model development for Pandanus Amaryllifolius plant microbial fuel cell

Cheng

Ching

Uttraphan

et al. 2020

IJEECS

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Plant microbial fuel cell (P-MFC) is an electrochemical reactor that converts organic compounds to electrical energy through the catalytic reaction from electrochemically active bacteria (EAB). However, there is no sign of an attempt in developing the functional model in predicting the energy conversion and utilization of P-MFC. In this study, an analytic model is proposed to show the whole production process of the organic compound to electrical energy generation. <em>Pandanus Amaryllifolius</em> plant was used as sources of photosynthate, where biomass product from rhizodeposition, acetate was produced, and soil bacteria as the microbial culture, and air as the input to the cathode chamber. The proposed analytical model is able to predict the output of the P-MFC using the parameters from the experiment. The generated data from the model was then compared with the monitored data from the <em>Pandanus Amaryllifolius </em>P-MFC. The results show the electrical power output has a high similarity pattern with the bacterial growth curve model and able to achieve the coulombic efficiency of 95.32%.

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Section: Electron Extraction From An Oxidizing Substratementioning

confidence: 99%

Electrical energy production from plant biomass: an analysis model development for Pandanus Amaryllifolius plant microbial fuel cell

Cheng

Ching

Uttraphan

et al. 2020

IJEECS

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“…The use of meta-heuristic algorithms, due to their ability for faster and simpler solving of nonlinear models, are exponentially increasing [13][14][15][16]. Some examples of these methods are the dragonfly algorithm [17], deer hunting optimizer [18_ENREF_20], multi-verse optimizer [19_ENREF_18], improved invasive weed optimization algorithm [20_ENREF_19], and chaotic grasshopper optimizer [21].…”

Section: Introductionmentioning

confidence: 99%

Optimization of PEMFC Model Parameters Using Meta-Heuristics

et al. 2021

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The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed for minimizing the construction cost of a 50 kW fuel cell stack, along with the costs of accessories regarding the current density, stoichiometric coefficient of the hydrogen and air, and pressure of the system as well as the temperature of the system as optimization parameters. The functional–economic model is developed for the studied system in which all components of the system are modeled economically as well as electrochemically–mechanically. The objective function is solved by a newly improved metaheuristic technique, called converged collective animal behavior (CCAB) optimizer. The final results of the method are compared with the standard CAB optimizer and genetic algorithm as a popular technique. The results show that the best optimal cost with 0.1061 $/kWh is achieved by the CCAB. Finally, a sensitivity analysis is provided for analyzing the consistency of the method.

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“…The applied methods may be divided into two groups; the first is based on only one algorithm, while the second is based on combining two techniques or more [8, 17, 26, 27]. The first group of single algorithms such as adaptive differential evolution algorithm (ADE) [25, 26], particle swarm optimisation, seeker optimisation algorithm (SOA) and genetic algorithm (GA) [18, 21, 27], Grey Wolf Optimisation (GWO) [8], Antlion Optimiser (ALO) [33] and Dragonfly Algorithm (DA) [34], Grasshopper Optimisation Algorithm (GOH) [16], Slap Swarm Optimiser (SSO) [17], Shark Smell Optimiser (SHSO) [35], JAYA algorithm [24], and Cuckoo Search (CS) [13] have been proposed to resolve the issue of parameter estimation of PEMFCs. The main objective of applying the reported methods is to obtain an accurate model of the PEMFC.…”

Section: Introductionmentioning

confidence: 99%

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Diab

Abdul-Ghaffar²,

Ahmed

et al. 2021

IET Renewable Power Gen

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This paper introduces the application of the variants of the Grey Wolf Optimisation algorithm for the sake of assessing unknown parameters of Proton Exchange Membrane Fuel Cells models. Three versions of Grey Wolf Optimisation algorithm are applied: Conventional Grey Wolf Optimisation, Improved Grey Wolf Optimisation based on dimension learning-based hunting, and Selective Opposition-based Grey Wolf Optimisation. Moreover, Optimisation algorithms of Ant Lion Optimiser, Atom search optimisation, Dragonfly algorithm, and Multi-Verse Optimiser have been applied to estimate the parameters of Proton Exchange Membrane Fuel Cells models for the validation purpose. These algorithms are applied to three commercial Proton Exchange Membrane Fuel Cells stacks: BCS 500W-PEM, 500W-SR-12PEM and 250W-stack. The analyses are executed considering several operational circumstances. Sum of square errors value of the results based on parameters estimation and those experimentally tested are calculated. The objective function is chosen as sum of square errors value. The results are compared with those obtained using well-known methods in the literature to validate the effectiveness of the proposed methods. It is noticeable that the simulated I-V curves momentously match the datasheet curves for all the studied cases. In addition, considering the accuracy of the solution and the convergence speed, the Proton Exchange Membrane Fuel Cells model based on the Improved Grey Wolf Optimisation algorithm excels all other algorithms. Based on the simulation results, the Improved Grey Wolf Optimisation algorithm can improve the optimisation efficiency to 99.97 for the 250 W stack while the efficiency with Grey Wolf Optimisation and Selective Opposition-based Grey Wolf Optimisation are 98.83 and 98.84, respectively, for the 250 W stack case study.

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Optimizing PEMFC model parameters using dragonfly algorithm: a performance study

Cited by 9 publications

References 0 publications

Electrical energy production from plant biomass: an analysis model development for Pandanus Amaryllifolius plant microbial fuel cell

Electrical energy production from plant biomass: an analysis model development for Pandanus Amaryllifolius plant microbial fuel cell

Optimization of PEMFC Model Parameters Using Meta-Heuristics

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

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