Investigation into the effect of molybdenum-site substitution on the performance of Sr2Fe1.5Mo0.5O6-δ for intermediate temperature solid oxide fuel cells

In this paper, niobium doping is evaluated as a means of enhancing the electrochemical performance of a Sr₂Fe_1.5Mo_0.5O_6-δ (SFM) perovskite structure cathode material for intermediate temperature solid oxide fuel cells (IT-SOFCs) applications. As the radius of Nb approximates that of Mo and exhibits +4/+5 mixed valences, its substitution is expected to improve material performance. A series of Sr₂Fe_1.5Mo_0.5-xNb_xO_6-δ (x = 0.05, 0.10, 0.15, 0.20) cathode materials are prepared and the phase structure, chemical compatibility, microstructure, electrical conductivity, polarization resistance and power generation are systematically characterized. Among the series of samples, Sr₂Fe_1.5Mo_0.4Nb_0.10O_6-δ (SFMNb_0.10) exhibits the highest conductivity value of 30 S cm^-1 at 550°C, and the lowest area specific resistance of 0.068 Ω cm² at 800°C. Furthermore, an anode-supported single cell incorporating a SFMNb_0.10 cathode presents a maximum power density of 1102 mW cm^-2 at 800°C. Furthermore no obvious performance degradation is observed over 15 h at 750°C with wet H₂(3% H₂O) as fuel and ambient air as the oxidant. These results demonstrate that SFMNb shows great promise as a novel cathode material for IT-SOFCs.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.