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The objective of this paper was to study the sCO2 cycle as a waste heat recovery system for a 180 kW biogas engine. The research methodology adopted was numerical simulations through two models built in different programs: Aspen HYSYS and GT Suite. The models were used to optimize the design and thermodynamic parameters of a CO2 cycle in terms of system power, system efficiency, expander, and compressor efficiency. Depending on the objective function, the sCO2 cycle could provide additional power ranging from 27.9 to 11.3 kW. Based on the calculation performed, "Recuperated cycle at maximum power" was selected for further investigation. The off-design analysis of the system revealed the optimum operating point. The authors designed the preliminary dimensions of the turbomachinery, i.e., the rotor dimension is 16 mm, which will rotate at 100,000 rpm. [ABSTRACT FROM AUTHOR]

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]

Summary: This article shows the teaching processes of artificial neural networks that are used to model the molten carbonate fuel cell (MCFC). Researchers model MCFCs to address a variety of issues across a range of complexities, from simply gauging the effect of temperature through to a complete model with 14 input parameters. The architecture of the model is a triple layer network with one hidden layer containing three neurons. The activation function used for the hidden layer was a hyperbolic tangent, with the last layer being based on linear function. We produced various network configurations, mostly networks containing one hidden layer. Models map the work of a real fuel cell with an average error in the range of 2.4% to 4.6%. The model we created guided the optimization of the thermal‐flow and construction parameters of the MCFC. Commercially available software was used to build the model and optimize the operating parameters. The selected objective functions were the efficiency of electricity production and the power density obtained from the cell's surface. The results obtained serve as pointers for possible changes in fuel cell operation and could lead to some structural changes being made. [ABSTRACT FROM AUTHOR]