Influence of gray particle assumption on the predictive accuracy of gas property approximations

2018-11-01
In this study, influence of gray particle assumption on the predictive accuracy of gas property models is investigated for conditions typically encountered in industrial coal-fired furnaces. The aim is (i) to identify how the share of gas radiation is influenced by the presence of particles and particle properties and (ii) to determine the effect of gray particle assumption on the predictive accuracy of gas property approximations. For that purpose, predictive accuracy of a simple gas property model is benchmarked against that of Spectral Line-Based Weighted Sum of Grey Gases Model (SLW) in the presence of gray/non-gray particles with different ash compositions, particle loads and boundary conditions. Input data required for the radiation code and its validation are provided from two combustion tests previously carried out in a 300 kWt Atmospheric Bubbling Fluidized Bed Combustor (ABFBC) test rig burning low calorific value Turkish lignite with high volatile matter/fixed carbon (VM/FC) ratio in its own ash. Comparisons reveal that gray particle assumption leads to over-estimation of particle radiation, which leads to under-estimation of gas radiation share in total radiative heat exchange. This under-estimation is found to be reflected on the predictive accuracy of gas property models, that is, a simple gas property model can be found to be "accurate" if particles are assumed to be gray although that is not the case. Furthermore, share of particle radiation in total radiative heat exchange is demonstrated to be strongly dependent on the spectral nature of particle properties. The results show that accurate gas property models such as SLW are needed to represent the spectral behavior of combustion gases even at high particle loads.
Citation Formats
C. Ates, N. Selçuk, and G. Külah, “Influence of gray particle assumption on the predictive accuracy of gas property approximations,” pp. 67–83, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30608.