Uncertainty quantification of fuel variability effects on high hydrogen content syngas combustion

被引:21
作者
Zhang, Kai [1 ]
Jiang, Xi [1 ]
机构
[1] Queen Mary Univ London, Sch Engn & Mat Sci, Mile End Rd, London E1 4NS, England
基金
英国工程与自然科学研究理事会;
关键词
Uncertainty quantification; Syngas combustion; Fuel variability; Polynomial chaos expansion; Targeted uncertainty reduction; COMPRESSION RATIO; STABILITY LIMITS; EMISSIONS; FLAMES; CO2; TEMPERATURE; PERFORMANCE; TECHNOLOGY; DILUTION; ISSUES;
D O I
10.1016/j.fuel.2019.116111
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Fuel variability effects on high hydrogen content syngas combustion physicochemical properties and NOx emission characteristics are investigated invoking polynomial chaos expansion based uncertainty quantification. The focus is put on providing an in-depth understanding of fuel variability effect under very lean conditions, i.e., near lean blowout limit. It is found that leaner combustion of syngas leads to higher flame speed fluctuation. Under 1.5% small fluctuations in species concentration of syngas fuels, a maximum of 5% fluctuation of flame speed is observed at equivalence ratio of 0.45 for H60CO30 (60% H-2 + 30% CO), while the lowest fluctuation is observed near equivalence ratio of 0.8 for the cases considered. Meanwhile, maximum NO concentration fluctuation of 8.3% and NO2 concentration fluctuation of 6.5% are observed at very lean conditions. Uncertainty analyses show that on one hand, hydrogen always has the highest contribution to flame speed variation followed by carbon monoxide, carbon dioxide, and methane. On the other hand, carbon monoxide is found to have the highest contribution to variation of flame temperature, followed by hydrogen, carbon dioxide, and methane. The quantified sensitivity information reported in present study can be used to guide targeted uncertainty reduction from syngas upstream gasification process.
引用
收藏
页数:12
相关论文
共 46 条
[1]  
[Anonymous], 2007, Uncertainty Propagation and Sensitivity Analysis in Mechanical ModelsContributions to Structural Reliability and Stochastic Spectral Methods
[2]   Effects of compression ratio and hydrogen addition on lean combustion characteristics and emission formation in a Compressed Natural Gas fuelled spark ignition engine [J].
Bhasker, J. Pradeep ;
Porpatham, E. .
FUEL, 2017, 208 :260-270
[3]   An assessment of regulated emissions and CO2 emissions from a European light-duty CNG-fueled vehicle in the context of Euro 6 emissions regulations [J].
Bielaczyc, Piotr ;
Woodburn, Joseph ;
Szczotka, Andrzej .
APPLIED ENERGY, 2014, 117 :134-141
[4]   System issues and tradeoffs associated with syngas production and combustion [J].
Casleton, Kent H. ;
Breault, Ronald W. ;
Richards, George A. .
COMBUSTION SCIENCE AND TECHNOLOGY, 2008, 180 (06) :1013-1052
[5]   Co-production of hydrogen, electricity and CO2 from coal with commercially ready technology.: PartA:: Performance and emissions [J].
Chiesa, P ;
Consonni, S ;
Kreutz, T ;
Williams, R .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2005, 30 (07) :747-767
[6]   Co-gasification of biomass and coal for methanol synthesis [J].
Chmielniak, T ;
Sciazko, M .
APPLIED ENERGY, 2003, 74 (3-4) :393-403
[7]   Flashback propensity of syngas fuels [J].
Dam, Bidhan ;
Love, Norman ;
Choudhuri, Ahsan .
FUEL, 2011, 90 (02) :618-625
[8]  
Davis P. J., 2007, METHODS NUMERICAL IN
[9]  
de Finetti B., 2017, THEORY PROBABILITY C, DOI [10.1023/a:1007617005950, DOI 10.1023/A:1007617005950]
[10]  
Debusschere B., UNCERTAINTY QUANTIFI