Effect of equivalence ratio on gas distribution and performance parameters in air-gasification of asphaltene: A model based on Artificial Neural Network (ANN)

被引：9

作者：

Gao, Wei ^{[1
]}

Aslam, Adnan ^{[2
]}

Li, Fei ^{[3
]}

机构：

[1] Yunnan Normal Univ, Sch Informat Sci & Technol, Kunming, Yunnan, Peoples R China

[2] Univ Engn & Technol, Dept Nat Sci & Humanities, Lahore, Rcet, Pakistan

[3] Yunnan Normal Univ, Dept Res, Kunming 650500, Yunnan, Peoples R China

来源：

PETROLEUM SCIENCE AND TECHNOLOGY | 2019年 / 37卷 / 02期

关键词：

equivalence ratio; artificial neural network; gasification; lower caloric value; asphaltene; STEAM GASIFICATION; WASTE;

D O I：

10.1080/10916466.2018.1533864

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：

Air-gasification of asphaltene was carried out in a bench scale fluidized bed. A neural network model was also developed to study the influence of equivalence ratio (ER) on gas distribution, lower caloric value (LHV) of producer gas, and performance indicators (char conversion and cold gas efficiencies). The contents of CO and H-2 were initially increased from 53.2 to 55.4 vol% and 35.4 to 37.6 vol% as ER increased to 0.35, and then decreased to 43.3 vol% and 27.7 vol% at ER of 0.5, respectively. The results also indicated that the LHV of the produced syngas was significantly decreased with ER increasing because of more oxidation reactions at higher ERs.

引用

页码：202 / 207

页数：6

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