Artificial neural network investigation of the structural group contribution method for predicting pure components' autoignition temperature.

被引：0
|
作者：
Albahri, TA [1 ]
George, RS [1 ]
机构：
[1] Kuwait Univ, Dept Chem Engn, Kuwait, Kuwait
来源：
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY | 2003年 / 225卷
关键词：
D O I：
暂无
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
201-FUEL
引用
收藏
页码：U874 / U874
页数：1
相关论文
共 50 条
  • [21] Determination of Parachor of Various Compounds Using an Artificial Neural Network-Group Contribution Method
    Gharagheizi, Farhad
    Eslamimanesh, Ali
    Mohammadi, Amir H.
    Richon, Dominique
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2011, 50 (09) : 5815 - 5823
  • [22] Prediction of the Liquid Vapor Pressure Using the Artificial Neural Network-Group Contribution Method
    Tarjomannejad, Ali
    [J]. IRANIAN JOURNAL OF CHEMISTRY & CHEMICAL ENGINEERING-INTERNATIONAL ENGLISH EDITION, 2015, 34 (04): : 97 - 111
  • [23] An Artificial Neural Network Model for Predicting the Hydrate Formation Temperature
    A. N. El-hoshoudy
    Abdelrahman Ahmed
    Sayed Gomaa
    Atef Abdelhady
    [J]. Arabian Journal for Science and Engineering, 2022, 47 : 11599 - 11608
  • [24] An Artificial Neural Network Model for Predicting the Hydrate Formation Temperature
    El-hoshoudy, A. N.
    Ahmed, Abdelrahman
    Gomaa, Sayed
    Abdelhady, Atef
    [J]. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2022, 47 (09) : 11599 - 11608
  • [25] Unified artificial neural network-group contribution method for predictions of normal boiling point and critical temperature of refrigerants and related compounds
    Devotta, Sukumar
    Chelani, Asha
    [J]. INTERNATIONAL JOURNAL OF REFRIGERATION, 2022, 140 : 112 - 124
  • [26] MNLR and ANN structural group contribution methods for predicting the flash point temperature of pure compounds in the transportation fuels range
    Albahri, Tareq A.
    [J]. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2015, 93 : 182 - 191
  • [27] Prediction of the specific volume of polymeric systems using the artificial neural network-group contribution method
    Moosavi, Majid
    Soltani, Nima
    [J]. FLUID PHASE EQUILIBRIA, 2013, 356 : 176 - 184
  • [28] ARTIFICIAL NEURAL NETWORK APPROACH FOR PREDICTION OF TOXICITY OF ORGANIC COMPOUNDS BASED ON AN IMPROVED GROUP CONTRIBUTION METHOD
    Yu, Yan-Jun
    Liu, Rui
    Su, Rong-Xin
    Wang, Li-Bing
    Qi, Wei
    He, Zhi-Min
    [J]. FRESENIUS ENVIRONMENTAL BULLETIN, 2010, 19 (12): : 2777 - 2782
  • [29] Viscosity prediction of hydrocarbon binary mixture using an artificial neural network-group contribution method
    Nanvakenari, Sara
    Ghasemi, Mitra
    Movagharnejad, Kamyar
    [J]. CHEMICAL PRODUCT AND PROCESS MODELING, 2022, 17 (03): : 199 - 211
  • [30] Artificial neural network models for predicting degradation trends in system components and sensors
    Naghedolfeizi, M
    Arora, S
    [J]. AUTOTESTCON 2003, PROCEEDINGS: FUTURE SUSTAINMENT FOR MILITARY AND AEROSPACE, 2003, : 647 - 651
12345