文献类型：期刊文献

英文题名：Uncertainty analysis of NOx and CO emissions in industrial ethylene cracking furnace using high-precision sparse polynomial chaos expansion

作者：Hu, Guihua[1]; Xu, Linghong[1]; Zhao, Liang[1]; Du, Wenli[1]; Qian, Feng[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, School of Information Science and Engineering, East China University of Science and Technology, Xuhui, Shanghai, China

年份：2022

卷号：196

期号：2

起止页码：195

外文期刊名：Combustion Science and Technology

收录：EI(收录号：20222012124229)

语种：英文

外文关键词：Computational fluid dynamics - Ethylene - Expansion - Industrial emissions - Nitrogen oxides - Pollution - Probability distributions - Sensitivity analysis

摘要：Traditional designing of ethylene-cracking furnaces by computational fluid dynamics (CFD) does not consider the uncertainties in actual engineering. This paper introduces one uncertainty analysis framework based on non-intrusive polynomial chaos expansion (NIPCE) and multi-order Sobol indices to perform uncertainty quantification (UQ) and sensitivity analysis for NOx and CO emissions in the combustion process. In this framework, several operating parameters of the bottom burners that easily cause uncontrollable fluctuations in the flame and emission characteristics are selected as uncertainty variables and characterized as a probability distribution. Computationally expensive CFD simulation of cracking furnace is used to generate a small number of samples, which are carried out to develop high-precision sparse PCE models by the degree-adaptive scheme and least angle regression (LAR) algorithm. And multi-order Sobol sensitivity analysis based on PCE models is performed efficiently to research the influence of uncertain parameters on pollution generation. Under 3% of burner’s uncertainty operating parameters, the results find that the excess air coefficient of 1.20 can obviously reduce the generation of NOx and CO emissions and control the fluctuation caused by uncertainties. Moreover, sensitivity analysis determines the critical variables that affect pollutant emissions to help the actual process avoid the unknown effects of uncertainties. ? 2022 Taylor & Francis Group, LLC.