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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]

Molten Carbonate Fuel Cells (MCFC) offer several advantages that are accelerating the research and development effort. Recent advances include improved materials, new fabrication techniques and new designs, flow configurations and applications. Nevertheless, several factors are holding back large scale implementation of fuel cells, especially in distributed energy generation, a major one being their long response time to changing parameters. Whereas alternative mathematical models of the molten carbonate fuel cell stack have been developed over the last decade, there are no reported analyses of regarding the application of models for emergency scenarios such as fuel cell malfunctions. This paper presents the 0D model of MCFC, calibrated to the appropriate available experimental data. This study investigates a generic molten fuel cell stack with nominal power output of 1 kWel. As daily, weekly and monthly variations in the electrical power load are expected, there is a need to develop numerical tools to predict the unit's performance with high accuracy. Hence, a fully physical dynamic model of an MCFC stack was developed and implemented in Aspen HYSYS 10 modeling software to enable predictive analysis of the dynamic response. The model is used to simulate a number of few malfunctions with 5. 66% broken cells in the stack. Losses of performance are obtained, manifested by rising operating temperature and falling power output. Moreover, a number of emergency operation scenarios were investigated, such as sudden loss of electric load, fuel and oxidiant supply. The presented model exhibits high accuracy and accounts for thermal and electrochemical processes and parameters. The paper presents the methodology used in the study to analyze the sensitivity of key scenarios such as load changes and emergencies. Further functionality of the model, which was validated using real operating data, is discussed. • Dynamic model of a Molten Carbonate Fuel Cell 1 kW stackis proposed. • The model was calibrated to the appropriate available experimental data. • The load changes and emergency situations were examined. [ABSTRACT FROM AUTHOR]