Experimental investigation of novel molten borate fuel cell supported by an artificial neural network for electrolyte composition selection

Abstract

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.