Journal of Computational & Applied Research in Mechanical Engineering (JCARME)

Articles in Press

Document Type : Research Paper

Authors

Department of Mechanical Engineering, Khaje Nasir Toosi University of Technology, Tehran, Tehran, 1999143344, Iran

10.22061/jcarme.2025.11195.2470

Abstract

Researchers encounter difficulties in producing clean energy and addressing environmental issues. Solid oxide fuel cells (SOFCs) present a promising prospect to the growing demand for clean and efficient electricity due to their capacity to convert chemically stored energy into electrical energy directly. In enhancing this technology, ammonia is employed as a cost-effective and carbon-free fuel with convenient transport capabilities. Efficiently predicting the performance of a system in relation to its operating environment has the potential to expedite the identification of the optimal operating conditions across a broad spectrum of parameters. For this purpose, the performance of intermediate temperature solid oxide fuel cell (IT-SOFC) with inlet ammonia fuel is predicted utilizing machine learning, which is efficient in time and cost. Initially, the system is simulated with computational fluid dynamics finite element code to generate data for training machine learning algorithms (DNN, RFM and LASSO regression), followed by an evaluation of the predictive accuracy of these algorithms. The analysis demonstrates that the three examined algorithms exhibit sufficient accuracy in predicting the performance of the introduced solid oxide fuel cell (SOFC) system, all surpassing a 95 percent threshold. The RFM and DNN exhibit the most accurate predictions for the maximum temperature and power density of fuel cells, respectively.

Graphical Abstract

Keywords

Main Subjects

Fuel Cells

References

[1] Y. Wang, Y. Gu, H. Zhang, J. Yang, J. Wang, W. Guan, J. Chen, B. Chi, L. Jia, H. muroyama, T. Matsui, K. Eguchi and Z. Zhong, “Efficient and durable ammonia power generation by symmetric flat-tube solid oxide fuel cells”, Appl. Energy, Vol. 270, p. 115185, (2020).

[2] R. Braun and P. Kazempoor, “Application of SOFCs in combined heat, cooling and power systems”, Solid Oxide Fuel Cells: From Materials to System Modeling, Eds. M. Ni and T. S. Zhao, pp.327-381, (2013).

[3] M. Liu, A. Lanzini, W. Halliop, V. Cobas, A. Verkooijen and P. Aravind, “Anode recirculation behavior of a solid oxide fuel cell system: A safety analysis and a performance optimization”, Int. J. Hydrogen Energy, Vol. 38, No. 6, pp. 2868-2883, (2013).

[4] J. Yang, T. Akagi, T. Okanishi, H. Muroyama, T. Matsui and K. Eguchi, “Catalytic Influence of Oxide Component in Ni‐Based Cermet Anodes for Ammonia‐Fueled Solid Oxide Fuel Cells”, Fuel Cells, Vol. 15, No. 2, pp. 390-397, (2015).

[5] G. Cinti, U. Desideri, D. Penchini and G. Discepoli, “Experimental analysis of SOFC fuelled by ammonia”, Fuel Cells, Vol. 14, No. 2, pp. 221-230, (2014).

[6] N. Jantakananuruk, Performance, Temperature and Concentration Profiles in a Non-Isothermal Ammonia-Fueled Tubular SOFC, Master thesis, Worcester Polytechnic Institute, Worcester, Massachusetts, (2019).

[7] M. Asmare and M. İlbaş, “Direct ammonia fueled solid oxide fuel cells: A comprehensive review on challenges, opportunities and future outlooks”, Int. J. Energy T., Vol. 2, No. 1, (2020).

[8] A. Wojcik, H. Middleton and I. Damopoulos, “Ammonia as a fuel in solid oxide fuel cells”, J. Power Sources, Vol. 118, No. 1-2, pp. 342-348, (2003).

[9] E. S. M. Seo, W. K. Yoshito, V. Ussui, D. R. R. Lazar, S. R. H. d. M. Castanho and J. O. A. Paschoal, “Influence of the starting materials on performance of high temperature oxide fuel cells devices”, Mater. Res., Vol. 7, pp. 215-220, (2004).

[10] F. Ishak, I. Dincer and C. Zamfirescu, “Thermodynamic analysis of ammonia-fed solid oxide fuel cells”, J. Power Sources, Vol. 202, pp. 157-165, (2012).

[11] S. A. Hajimolana, M. A. Hussain, W. W. Daud and M. Chakrabarti, “Dynamic modelling and sensitivity analysis of a tubular SOFC fuelled with NH3 as a possible replacement for H2”, Chem. Eng. Res. Des., Vol. 90, No. 11, pp. 1871-1882, (2012).

[12] O. Siddiqui and I. Dincer, “A review and comparative assessment of direct ammonia fuel cells”, Therm. Sci. Eng. Prog., Vol. 5, pp. 568-578, (2018).

[13] K. Machaj, M. Skrzypkiewicz, A. Niemczyk, J. Kupecki, Z. Malecha and M. Chorowski, “Impact of Thickness on Direct Ammonia Decomposition in Solid Oxide Fuel Cells, Numerical and Experimental Research”, ECS Trans., Vol. 111, No. 6, p. 2203, (2023).

[14] M. Ilbas, B. Kumuk, M. A. Alemu and B. Arslan, “Numerical investigation of a direct ammonia tubular solid oxide fuel cell in comparison with hydrogen”, Int. J. Hydrogen Energy, Vol. 45, No. 60, pp. 35108-35117, (2020).

[15] A. F. S. Molouk, J. Yang, T. Okanishi, H. Muroyama, T. Matsui and K. Eguchi, “Comparative study on ammonia oxidation over Ni-based cermet anodes for solid oxide fuel cells”, J. Power Sources, Vol. 305, pp. 72-79, (2016).

[16] L. Barelli, G. Bidini and G. Cinti, “Operation of a solid oxide fuel cell based power system with ammonia as a fuel: Experimental test and system design”, Energies, Vol. 13, No. 23, p. 6173, (2020).

[17] M. Ilbas, M. A. Alemu and F. M. Cimen, “Comparative performance analysis of a direct ammonia-fuelled anode supported flat tubular solid oxide fuel cell: a 3D numerical study”, Int. J. Hydrogen Energy, Vol. 47, No. 5, pp. 3416-3428, (2022).

[18] M. Asmare, M. Ilbas, F. M. Cimen, C. Timurkutluk and S. Onbilgin, “Three-dimensional numerical simulation and experimentalvalidation on ammonia and hydrogen fueled micro tubular solid oxide fuel cell performance”, Int. J. Hydrogen Energy, Vol. 47, No. 35, pp. 15865-15874, (2022).

[19] M. Keyhanpour and M. Ghasemi, “3D Investigation of Tubular PEM Fuel Cell Performance Assuming Fluid-Solid-Heat Interaction”, J. Comp. Methods Eng., Vol. 41, No. 1, pp. 79-99, (2022).

[20] M. Keyhanpour and M. Ghasemi, “3D Simulation of Effect of Geometry and Temperature Distribution on SOFC Performance”, J. Fluid Mech. Aerodyn., Vol. 10, No. 2, pp. 169-184, (2022).

[21] S. A. Vilekar, I. Fishtik and R. Datta, “The peculiar catalytic sequence of the ammonia decomposition reaction and its steady-state kinetics”, Chem. Eng. Sci., Vol. 71, pp. 333-344, (2012).

[22] J. Badur, M. Lemański, T. Kowalczyk, P. Ziółkowski and S. Kornet, “Zero-dimensional robust model of an SOFC with internal reforming for hybrid energy cycles”, Energy, Vol. 158, pp. 128-138, (2018).

[23] J. Kupecki, D. Papurello, A. Lanzini, Y. Naumovich, K. Motylinski, M. Blesznowski and M. Santarelli, “Numerical model of planar anode supported solid oxide fuel cell fed with fuel containing H2S operated in direct internal reforming mode (DIR-SOFC)”, Appl. Energy, Vol. 230, pp. 1573-1584, (2018).

[24] L. Barelli, G. Bidini, G. Cinti and A. Ottaviano, “Studyof SOFC-SOE transition on a RSOFC stack”, Int. J. Hydrogen Energy, Vol. 42, No. 41, pp. 26037-26047, (2017).

[25] G. Botta, M. Romeo, A. Fernandes, S. Trabucchi and P. Aravind, “Dynamic modeling of reversible solid oxide cell stack and control strategy development”, Energy Convers. Manage., Vol. 185, pp. 636-653, (2019).

[26] V. Menon, V. M. Janardhanan and O.Deutschmann, “A mathematical model to analyze solid oxide electrolyzer cells (SOECs) for hydrogen production”, Chem. Eng. Sci., Vol. 110, pp. 83-93, (2014).

[27] N. Chowdhury and G. Chandra Deka, Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications: IGI Global, Ministry of Skill Development and Entrepreneurship, New Delhi, pp. 1-255, (2020).

[28] B. Yang, Z. Guo, Y. Yang, Y. Chen, R. Zhang, K. Su, H. Shu, T. Yu and X. Zhang, “Extreme learning machine based meta-heuristicalgorithms for parameter extraction of solid oxide fuel cells”, Appl. Energy, Vol. 303, p. 117630, (2021).

[29] R. Ding, Y. Ding, H. Zhang, W. Yin, R. Wang, Z. Xu, Y. Liu, J. Wang, J. Li and J. Liu, “Applying machine learning to boost thedevelopment of high-performance membrane electrode assembly for proton exchange membrane fuel cells”, J. Mater. Chem. A, Vol. 9, No. 11, pp. 6841-6850, (2021).

[30] Q. Zhao and X. Tang, “Prediction of battery performance degradation based on machine learning”, Second Int. Conf. Sustainable Technology and Management (ICSTM), Vol. 12804: SPIE, pp. 221-229, (2023).

[31] J. Arriagada, P. Olausson and A. Selimovic, “Artificial neural network simulator for SOFC performance prediction”, J. Power Sources, V. 112, No. 1, pp. 54-60, (2002).

[32] J. Milewski and K. Świrski, “Modelling the SOFC behaviours by artificial neural network”, Int. J. Hydrogen Energy, Vol. 34, No. 13, pp. 5546-5553, (2009).

[33] K. Chaichana, Y. Patcharavorachot, B. Chutichai, D. Saebea, S. Assabumrungrat and A. Arpornwichanop, “Neural network hybrid model of a direct internal reforming solid oxide fuel cell”, Int. J. Hydrogen Energy, Vol. 37, No. 3, pp. 2498-2508, (2012).

[34] H. Xu, J. Ma, P. Tan, B. Chen, Z. Wu, Y. Zhang, H. Wang, J. Xuan and M. Ni, “Towards online optimisation of solidoxide fuel cell performance: Combining deep learning with multi-physics simulation”, Energy AI, Vol. 1, p. 100003, (2020).

[35] F. C. İskenderoğlu, M. K. Baltacioğlu, M. H. Demir, A. Baldinelli, L. Barelli, and G. Bidini, “Comparison of support vector regressionand random forest algorithms for estimating the SOFC output voltage by considering hydrogen flow rates”, Int. J. Hydrogen Energy, Vol. 45, No. 60, pp. 35023-35038, (2020).

[36] S. Ba, D. Xia and E. M. Gibbons, “Model identification and strategy application for Solid Oxide Fuel Cell using Rotor HopfieldNeural Network based on a novel optimization method”, Int. J. Hydrogen Energy, Vol. 45, No. 51, pp. 27694-27704, (2020).

[37] J. Li and T. Yu, “A novel data-driven controller for solid oxide fuel cell via deep reinforcementlearning”, J. Cleaner Prod. , Vol. 321, p. 128929, (2021).

[38] H. Jia and B. Taheri, “Model identification of solid oxide fuel cell using hybrid Elman neuralnetwork/quantum pathfinder algorithm”, Energy Rep., Vol. 7, pp. 3328-3337, (2021).

[39] V. Subotić, M. Eibl and C. Hochenauer, “Artificial intelligence for time-efficient predictionand optimization of solid oxide fuel cell performances”, Energy Convers. Manage., Vol. 230, p. 113764, (2021).

[40] F. Mütter, C. Berger, B. Königshofer, M. Höber, C. Hochenauer and V. Subotić,“Artificial intelligence for solid oxide fuel cells: Combining automated high accuracy artificial neural network model generation and genetic algorithm for time-efficient performance prediction and optimization”, Energy Convers. Manage., Vol. 291, p. 117263, (2023).

[41] M. Tofigh, Z. Salehi, A. Kharazmi, D. J. Smith, A. R. Hanifi, C. R. Cokh and M. Shahbakhti, “Transient modeling of a solid oxide fuel cell using an efficient deep learning HY-CNN-NARX paradigm”, J. Power Sources, Vol. 606, p. 234555, (2024).

[42] H. Kim, J. Lee, A. Gokbayrak, Y. Seo, S. Oh, M. Oh, Y. Jun, J. Son and S. Yang, “Characterization of direct-ammonia solid oxide fuel cells (DA-SOFCs) at 650–750° C in a single-repeating unit stack: Effects of metallic components and residual ammonia”, Int. J. Hydrogen Energy, Vol. 68, pp. 1312-1321, (2024).

[43] Z. Gong, H. Wang, C. Li, Q. Sang, Y. Xie, X. Zhang and Y. Liu, “Progress in the design and performance evaluation of catalystsfor low-temperature direct ammonia fuel cells”, Green Chem. Eng., pp. 1-14, (2024).

[44] S. N. Ranasinghe and P. H. Middleton, “Modelling of single cell solid oxide fuel cellsusing COMSOL multiphysics”, 2017 IEEE Int. Conf. Environ. Elec. Eng. 2017 IEEE Indus. Com. Power Sys. Europe (EEEIC/I&CPS Europe), pp. 1-6, (2017).

[45] D. F. Cheddie, “Temkin-Pyzhev kinetics in intermediate temperature ammonia-fed solid oxide fuel cells”, Int. J. PowerEnergy Res., Vol. 2, No. 3, pp. 43-51, (2018).

[46] M. Keyhanpour, M. Ghassemi and M. Pourbagian, “Parametric Study of Ammonia-Fueled Tubular AP-SOFC with Temkin-Pyzhev Kinetic Model”, J. Iranian Chem. Eng., Vol. 22, No. 129, pp. 110-123, (2023).

[47] D. Su, J. Zheng, J. Ma, Z. Dong, Z. Chen and Y. Qin, “Application of Machine Learningin Fuel Cell Research”, Energies, Vol. 16, No. 11, p. 4390, (2023).

[48] M. H. Golbabaei, M. R. Saeidi Varnoosfaderani, A. Zare, H. Salari, F. Hemmati, H. Abdoli and B. Hamawandi, “Performance Analysis of Anode-Supported Solid Oxide Fuel Cells: A Machine Learning Approach”, Materials, Vol. 15, No. 21, p. 7760, (2022).

[49] S. Wang, Y. Chen, Z. Cui, L. Lin and Y. Zong, “Diabetes RiskAnalysis Based on Machine Learning LASSO Regression Model”, J. Theory and Practice Eng. Sci., Vol. 4, No. 01, pp. 58-64, (2024).

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Journal of Computational & Applied Research in Mechanical Engineering (JCARME)

Articles in Press, Accepted Manuscript
Available Online from 18 February 2025

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