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

共 50 条

[41] The Effect of Soil Physical and Chemical Properties on the Performance Indices of Artichoke's Leaf using Artificial Neural Network (ANN)
Alizadeh, Azadeh
Ghasemnezhad, Azim
Hezarjaribi, Aboutaleb
Aladdin, Mohammad Zaman
[J]. JOURNAL OF MEDICINAL PLANTS AND BY-PRODUCTS-JMPB, 2024, 13 (01): : 123 - 135
[42] An effect analysis model for corporate marketing mix based on artificial neural network
Wang, Haiping
[J]. Ingenierie des Systemes d'Information, 2020, 25 (05): : 579 - 587
[43] Predicting Solar Performance Ratio Based on Encoder-Decoder Neural Network Model
Yen, Chih-Feng
Hsieh, He-Yen
Su, Kuan-Wu
Leu, Jenq-Shiou
[J]. 2019 11TH INTERNATIONAL CONGRESS ON ULTRA MODERN TELECOMMUNICATIONS AND CONTROL SYSTEMS AND WORKSHOPS (ICUMT), 2019,
[44] Discrimination and identification of morphotypes of Banksia integrifolia (Proteaceae) by an Artificial Neural Network (ANN), based on morphological and fractal parameters of leaves and flowers
Pandolfi, Camilla
Messina, Giuseppe
Mugnai, Sergio
Azzarello, Elisa
Masi, Elisa
Dixon, Kingsley
Mancuso, Stefano
[J]. TAXON, 2009, 58 (03) : 925 - 933
[45] Artificial neural network prediction models for Montney shale gas production profile based on reservoir and fracture network parameters
Viet Nguyen-Le
Shin, Hyundon
[J]. ENERGY, 2022, 244
[46] Performance prediction of a production line with variability based on grey model artificial neural network
Li, Changjun
Wang, Hong
Li, Bo
[J]. PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 9582 - 9587
[47] Performance prediction of PV modules based on artificial neural network and explicit analytical model
Zhang, Chen
Zhang, Yunpeng
Su, Jialei
Gu, Tingkun
Yang, Ming
[J]. JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2020, 12 (01)
[48] Evaluating the Performance of Artificial Neural Network Model in Downscaling Daily Temperature, Precipitation and Wind Speed Parameters
Shiehbeigi, A.
Abbaspour, M.
Soltaniyeh, M.
Hosseinzadeh, F.
Abedi, Z.
[J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH, 2014, 8 (04) : 1223 - 1230
[49] Use of Artificial Neural Network Model for Rice Quality Prediction Based on Grain Physical Parameters
Sampaio, Pedro Sousa
Almeida, Ana Sofia
Brites, Carla Moita
[J]. FOODS, 2021, 10 (12)
[50] Analytical Modeling and Artificial Neural Network (ANN) Simulation of Current-Voltage Characteristics in Graphene Nanoscroll Based Gas Sensors
M. Khaledian
Razali Ismail
Elnaz Akbari
[J]. Plasmonics, 2015, 10 : 1713 - 1722

← 1 2 3 4 5 →