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Toward a Digital Twin of a Solid Oxide Fuel Cell Microcogenerator: Data-Driven Modelling

Author

Listed:
  • Tancredi Testasecca

    (Department of Engineering, Università degli Studi di Palermo, 90128 Palermo, Italy)

  • Manfredi Picciotto Maniscalco

    (CNR-ITAE: Istituto di Tecnologie Avanzate per l’Energia “Nicola Giordano”, 90128 Palermo, Italy)

  • Giovanni Brunaccini

    (CNR-ITAE: Istituto di Tecnologie Avanzate per l’Energia “Nicola Giordano”, 90128 Palermo, Italy)

  • Girolama Airò Farulla

    (CNR-INM: Consiglio Nazionale delle Ricerche—Istituto di Ingegneria del Mare, 90146 Palermo, Italy)

  • Giuseppina Ciulla

    (Department of Engineering, Università degli Studi di Palermo, 90128 Palermo, Italy)

  • Marco Beccali

    (Department of Engineering, Università degli Studi di Palermo, 90128 Palermo, Italy)

  • Marco Ferraro

    (CNR-ITAE: Istituto di Tecnologie Avanzate per l’Energia “Nicola Giordano”, 90128 Palermo, Italy)

Abstract

Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R 2 score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions.

Suggested Citation

  • Tancredi Testasecca & Manfredi Picciotto Maniscalco & Giovanni Brunaccini & Girolama Airò Farulla & Giuseppina Ciulla & Marco Beccali & Marco Ferraro, 2024. "Toward a Digital Twin of a Solid Oxide Fuel Cell Microcogenerator: Data-Driven Modelling," Energies, MDPI, vol. 17(16), pages 1-15, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4140-:d:1460023
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    References listed on IDEAS

    as
    1. Zhimin Guo & Zhiyuan Ye & Pengcheng Ni & Can Cao & Xiaozhao Wei & Jian Zhao & Xing He, 2023. "Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System," Energies, MDPI, vol. 16(6), pages 1-21, March.
    2. Yaping Wu & Xiaolong Wu & Yuanwu Xu & Yongjun Cheng & Xi Li, 2023. "A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    3. Mohamad Fairus Rabuni & Tao Li & Mohd Hafiz Dzarfan Othman & Faidzul Hakim Adnan & Kang Li, 2023. "Progress in Solid Oxide Fuel Cells with Hydrocarbon Fuels," Energies, MDPI, vol. 16(17), pages 1-36, September.
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