Boosting the power density of two‐chamber microbial fuel cell: Modeling and optimization

Rezk, Hegazy; Olabi, Abdul–Ghani; Abdelkareem, Mohammad Ali; Sayed, Enas Taha

doi:10.1002/er.8589

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…The fuel cell's voltage-current (VeI) curve is often used in these evaluations [6,7]. The VeI curve may be derived by experimentation [8,9], physical modelling [10,11], or data analysis [12,13]. By altering the operational conditions-for example, by switching between loads [7,28,]-the experimental methods can acquire the voltage at various current points.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of operational conditions of fuel cells by using machine learning

Shrote,

Kumar,

Kaur

et al. 2024

EAI Endorsed Trans IoT

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The reliability of fuel cells during testing is crucial for their development on test benches. For the development of fuel cells on test benches, it is essential to maintain their dependability during testing. It is only possible for the alarm module of the control software to identify the most serious failures because of the large operating parameter range of a fuel cell. This study presents a novel approach to monitoring fuel cell stacks during testing that relies on machine learning to ensure precise outcomes. The use of machine learning to track fuel cell operating variables can achieve improvements in performance, economy, and reliability. ML enables intelligent decision-making for efficient fuel cell operation in varied and dynamic environments through the power of data analytics and pattern recognition. Evaluating the performance of fuel cells is the first and most important step in establishing their reliability and durability. This introduces methods that track the fuel cell's performance using digital twins and clustering-based approaches to monitor the test bench's operating circumstances. The only way to detect the rate of accelerated degradation in the test scenarios is by using the digital twin LSTM-NN model that is used to evaluate fuel cell performance. The proposed methods demonstrate their ability to detect discrepancies that the state-of-the-art test bench monitoring system overlooked, using real-world test data. An automated monitoring method can be used at a testing facility to accurately track the operation of fuel cells.

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Section: Introductionmentioning

confidence: 99%

Monitoring of operational conditions of fuel cells by using machine learning

Shrote,

Kumar,

Kaur

et al. 2024

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

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Optimizing proton exchange membrane fuel cell parameter identification using enhanced hummingbird algorithm

Singla,

Safaraliev,

Gupta

et al. 2024

International Journal of Hydrogen Energy

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Advancements in Model Parameter Estimation for Proton Exchange Membrane Fuel Cells via Enhanced Artificial Hummingbird Algorithm

Shaheen,

Alassaf,

Alsaleh

et al. 2024

International Journal of Energy Research

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Fuel cells (FCs) have garnered significant attention due to their versatile applications, but their nonlinear characteristics pose challenges in the modeling process. This research presents a unique enhanced artificial hummingbird algorithm (EAHA) aimed at identifying the seven unknown parameters of the proton exchange membrane fuel cells (PEMFCs) stack by utilizing their experimental datasets. To accomplish this, the objective is to achieve accurate current/voltage (I/V) curves where a cost function is defined using the aggregation of quadratic deviations (AQD) between the measured dataset points and the appropriate model‐based estimations. The presented EAHA combines several territorial foraging techniques with a linear regulating mechanism. The performance of the conventional AHA is compared with the suggested EAHA using three commonly employed PEMFC modules. Furthermore, a comparative analysis is conducted against previously published methodologies and newly developed optimizers such as the equilibrium optimizer (EO), social networking search (SNS) technique, slim mold algorithm (SMA), heap‐based optimizer (HBO), and African vultures optimization (AVO) technique. The findings are compared to existing methodologies and other state‐of‐the‐art optimizers, providing valuable insights into the efficacy of the proposed approach. For the 250 W PEMFC stack, the proposed EAHA shows improvements of 2.966%, 6.493%, 1.491%, 7.080%, 1.131%, and 2.875% over AHA, AVO, EO, HBO, SMA, and SNS, respectively, depending on the mean AQD values. Similar findings are attained for the other two stacks. For example, for the test case of the BCS 500 W PEMFCs stack, the proposed EAHA demonstrates improvements of 64.228%, 82.859%, 66.140%, 81.156%, 46.302%, and 71.635% over AHA, AVO, EO, HBO, SMA, and SNS, respectively.

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Boosting the power density of two‐chamber microbial fuel cell: Modeling and optimization

Cited by 4 publications

References 44 publications

Monitoring of operational conditions of fuel cells by using machine learning

Monitoring of operational conditions of fuel cells by using machine learning

Optimizing proton exchange membrane fuel cell parameter identification using enhanced hummingbird algorithm

Advancements in Model Parameter Estimation for Proton Exchange Membrane Fuel Cells via Enhanced Artificial Hummingbird Algorithm

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