Reforming characteristic data

Table 7.1 Characteristic data of three electrically heated reactors for methanol steam reforming [183].

Table 6.9.2 Characteristic data of a reforming catalyst (Kern, 2003 Kern and Jess, 2005).

Over 30 man-years of effort were involved in developing the model, which is named KINPTR, an acronym for kinetic platinum reforming model. Since its development, KINPTR has had a major impact in Mobil s worldwide operations. It can be accessed by personnel at each of Mobil s locations throughout the world. Input requirements are simple and convenient making it very user friendly. Only feed characteristics, product quality targets, process configuration information, and process conditions are required for input. Output is informative and detailed. Overall and detailed yields, feed and product properties, and reactor performance data are given in the output. 

Parallelly a simulation has been started to formulate a flow sheet for the cycle operation. The IS process and its characteristics have been verified. Agreement has been achieved between experimental data and the simulation model. A further improvement of the process is needed to acquire a higher thermal efficiency and to select better structural materials which are compatible on an industrial scale. Further demonstration steps are the employment of a metallic reactor for hydrogen production at a rate of 1 Nm /h and a scale-up process to a 100 Nm /h rate [44]. The IS process is one of the candidates to be connected to the HTTR following the steam reforming system. 

In Fig. 10.6, the polarisation and power density curves obtained from the model are compared with the data from [1] in which the fuel considered was CH4 and the fuel composition for obtaining the static characteristic curves was fully reformed steam and methane mixture. It can be seen that the difference between the results is small because the principal gaseous species in the anode channel are still H2 and H2O, which is a valid assumption. A part of the small difference between the results can also be attributed to the difference in the calculations of the activation over-voltage between this model and [1]. 

While the reformer and burner can be considered as Gibbs reactors (delivering thermodynamic equilibrium values), the flow sheet simulation of the overall process requires the implementation of a confirmed stack characteristic. Key figures for the stack are power output, fuel utilization and electrochemical efficiency at the desired operation point. Thus, a Staxera Mk200/ESC4 stack was evaluated in a stack-test-bench with different fuel gas compositions and throughputs. Figure 4 shows the measured U/I-curves, Table 1 summarizes the stack performance data for the different operation points. 

Mathematical models that predict performance can aid in understanding and development of solid oxide fuel cells (SOFCs). A mathematical simulation of a SOFC is helpful in examining issues such as temperatures, materials, geometries, dimensions, fuels, and fuel reformation and in determining their associated performance characteristics. When physical properties or reaction kinetics are not known reliably, they can be estimated by fitting performance data on small-size, laboratory-scale cells to a mathematical model. The performance of a... 

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