Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems

Asensio, F.J.; Martín, Jesús Izquierdo; Zamora, I.; García-Villalobos, J.

doi:10.1016/j.energy.2017.02.043

Cited by 28 publications

(3 citation statements)

References 23 publications

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“…White box physical models can be difficult to implement on-line due to their high level of complexity (Petrone et al, 2013). Hybrid approaches (grey-box) have received the most attention in recent years initially focusing on the use of neural networks (Asensio et al, 2017) with extension to other AI based methods (e.g. Zhao et al (2019)).…”

Section: Introductionmentioning

confidence: 99%

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Tsalapati

Johnson

Jackson

et al. 2021

Expert Systems with Applications

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Polymer electrolyte membrane fuel cells (PEMFC) are a promising technology for economic and environmentally friendly energy production. However, they haven't reached their full potential in the market yet as only few reliable PEMFC systems have successfully passed the prototyping face. A drawback of the current diagnostic tools is that only a select few are of high genericity, reliability and can perform efficiently on-line at the same time. Furthermore, there is only limited research identifying both PEMFC stack faults and ancillary system faults simultaneously. While none of the existing tools can be interrogated by the end-user. In this research, we develop novel artificial intelligence-based technologies to overcome these existing barriers, i.e., i) a semantically enriched integrating schema (ontology) of the overall operation and structure of the PEMFC that allows automatic inference engines to automatically deduce fault detection; ii) a knowledgebased, light-weight, on-line fuel cell system diagnosis (FuCSyDi) platform. FuCSyDi detects and provides the location of failures by considering only the data from the reliable sensors. Additionally, it provides the reasons underpinning any forthcoming failures and enables the end-user to interrogate the platform for further information regarding its operation and structure. Our platform is validated by performing tests against common automotive stress conditions. This innovative approach enhances the reliability of the fuel cell system diagnosis and, hence, its lifetime performance.

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Section: Introductionmentioning

confidence: 99%

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Tsalapati

Johnson

Jackson

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…Over the last decades, commercially available CFD and mathematical models have evolved. [11][12][13][14][15][16][17] However, they are often in a complex form, and the estimation of the PEMFC performance requires a significant amount of computational turnaround time and resources. One way of overcoming this burden is to construct computationally cheaper models, known as surrogates or metamodels, that can very closely represent the simulation models.…”

mentioning

confidence: 99%

“…Data-driven surrogates have been developed, tested, and used in PEMFC applications. [15][16][17][18] Well-known surrogates include response surface approximation (RSA), radial basis neural network (RBNN), kriging (KRG), support vector machines (SVM), and artificial neural networks. Kanani et al 19 employed a quadratic RSA surrogate model to determine the maximum power in a single-cell serpentine PEMFC.…”

mentioning

confidence: 99%

Multi-Variate Optimization of Polymer Electrolyte Membrane Fuel Cells in Consideration of Effects of GDL Compression and Intrusion

Choi

Cha

kong

et al. 2022

J. Electrochem. Soc.

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This study applies a comprehensive surrogate-based optimization techniques to optimize the performance of polymer electrolyte membrane fuel cells (PEMFCs). Parametric cases considering four variables are defined using latin hypercube sampling. Training and test data are generated using a multidimensional, two-phase PEMFC simulation model. Response surface approximation, radial basis neural network, and kriging surrogates are employed to construct objective functions for the PEMFC performance. There accuracies are tested and compared using root mean square error and adjusted R-square. Surrogates linked with optimization algorithms, i.e., genetic algorithm and particle swarm optimization are used to determine the optimal design points. Comparative study of these surrogates reveals that the kriging model outperforms the other models in terms of prediction capability. Furthermore, the PEMFC model simulations at the optimal design points demonstrate that performance improvements of around 56–69 mV at 2.0 A/cm2 are achieved with the optimal design compared to typical PEMFC design conditions.

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Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

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For the sake of precise simulation, and proper controlling of the performance of the proton exchange membrane fuel cells (PEMFCs) generating systems, robust and neat mathematical modelling is crucially needed. Principally, the robustness and precision of modelling strategy depend on the accurate identification of PEMFC’s uncertain parameters. Hence, in the last decade, with the noteworthy computational development, plenty of meta-heuristic algorithms (MHAs) are applied to tackle such problem, which have attained very positive results. Thus, this review paper aims at announcing novel inclusive survey of the most up-to-date MHAs that are utilized for PEMFCs stack’s parameter identifications. More specifically, these MHAs are categorized into swarm-based, nature-based, physics-based and evolutionary-based. In which, more than 350 articles are allocated to attain the same goal and among them only 167 papers are addressed in this effort. Definitely, 15 swarm-based, 7 nature-based, 6 physics-based, 2 evolutionary-based and 4 others-based approaches are touched with comprehensive illustrations. Wherein, an overall summary is undertaken to methodically guide the reader to comprehend the main features of these algorithms. Therefore, the reader can systematically utilize these techniques to investigate PEMFCs’ parameter estimation. In addition, various categories of PEMFC’s models, several assessment criteria and many PEMFC commercial types are also thoroughly covered. In addition to that, 27 models are gathered and summarized in an attractive manner. Eventually, some insights and suggestions are presented in the conclusion for future research and for further room of improvements and investigations.

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Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems

Cited by 28 publications

References 23 publications

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Multi-Variate Optimization of Polymer Electrolyte Membrane Fuel Cells in Consideration of Effects of GDL Compression and Intrusion

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

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