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

Tancredi Testasecca, Manfredi Picciotto Maniscalco (), Giovanni Brunaccini, Girolama Airò Farulla, Giuseppina Ciulla, Marco Beccali and Marco Ferraro
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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

Energies, 2024, vol. 17, issue 16, 1-15

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.

Keywords: digital twin; energy; solid oxide fuel cell; machine learning; hydrogen (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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