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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]

The aim of the paper is to simulate multi-tank storage with the thermocline moving from tank to tank and compare the results against single tank storage. No analysis of this nature has previously been performed. The results lend added impetus to developing this new type of thermal energy storage, especially as heat losses through insulation have already been reduced a minimum. Dividing a seasonal thermal energy storage tank into smaller tanks reduces the negative effect of heat transfer through the thermocline. The work is a continuation of the concept already proposed in available literature of using multiple solar energy stores, but we focus mainly on developing a dynamic model of a system of this type and presenting the results of a time and thermocline dependent simulation. The study also encompassed examination of the thickness of thermal insulation of the storage tanks, with definitions of its critical size. Our study revealed that the use of multi-tank thermal energy storage reduces heat loss through the thermocline. This is illustrated mainly by changes in water temperature in the upper part of the tank, which fluctuates across a much smaller range than for a single tank. In the single tank system, the temperature drops below 60 °C, whereas for the multi-tank system it always remains above 64 °C. In addition, in the multi-tank solution the loss caused by the thermocline occurs in only one tank, leaving 3/4 of the storage system unaffected by thermocline loss. • The concept of multi tank seasonal thermal energy storage is examined. • The examination is based on the dynamic modeling. • Results are compared with the single tank thermal energy storage systems. • The examined system is more efficient due thermocline losses limitation. [ABSTRACT FROM AUTHOR]