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This paper presents research on the impact of the material microstructure on the performance of Molten Carbonate Fuel Cells (MCFC). The study is motivated by the need to increase the operation time of MCFC stacks. As the MCFC stacks are mainly composed of porous materials, the paper shows the impact of the porosity on both fuel cell performance and degradation rate. The study is based on the mathematical model, validated through experiments with varying microstructure parameters, achieving an overall relative error of 8%. The study evaluated the performance of MCFC using anodes with different porosity and found that increasing anode porosity from 40% to 70% led to a nearly two-fold increase in maximum power density. However, from both the degradation test and polarization curves, the optimal level of porosity for the anode is around 55%. The porosity level at 55% ensures the highest catalytic surface of the electrodes and is a compromise between the mechanical properties of the electrode and its electrochemical properties. • New approach of 0D model of MCFC including materials microstructure influence is proposed. • The new model is prepared in MS Excel and Aspen HYSYS environments. • Experimental investigation for materials with predefined microstructire parameters was performed for model validation. • Variant analysis is presented for different porosity, tortuosity and constrictivity of MCFC anode. • An optimal scenario is suggested based on modleing results. [ABSTRACT FROM AUTHOR]

This research proposes idea of new kind of high temperature fuel cell – Molten Borate Fuel Cell (MBFC). The idea of new fuel cell is based on technical modification of well-known Molten Carbonate Fuel Cell by changing electrolyte composition to a borate-based mixture. The cell was tested in laboratory conditions as proof-of-concept investigation and reached OCV at around 1 V and 0.05 A/cm2 of current density. The optimization of borate-based electrolyte composition was conducted with a help of ANN and experimentally verified. The deep feedforward artificial neural network (DFF ANN) was proposed in this study to model the behavior of the new Molten Borate Fuel Cell. This modeling approach is well-known as a potent tool for dealing with complicated modeling and prediction tasks. This study presented many network designs for a range of operating circumstances. The ANN has been used to model and optimize the SOFC, MCFC, and PEMFC, but the design and performance of the MBFC were not previously investigated. The innovative type of fuel cells at issue - Molten Borate Fuel Cells - were designed and optimized using a deep learning algorithm. The most advanced model provides a dynamic forecast of fuel cell operation, taking thermal-flow and electrolyte material factors into account. With an average inaccuracy of 0.3%, all the models performed fair enough. An ANN-based technique may also be used to improve cell operating parameters. Moreover, the operating composition of a novel molten borate electrolyte might be improved in terms of electrochemical performance of the fuel cell. • New type of fuel cells is proposed. • Various electrolyte compositions are proposed. • The selected electrolyte composition were tested experimentally. • The Artificial Neural Network is built for tailoring the electrolyte compositions. • New composition is proposed by the ANN. [ABSTRACT FROM AUTHOR]