Prediction of extrusion pressure using an artificial neural network

被引：48
|
作者：
Li, YY
Bridgwater, J
机构：
[1] Univ Bath, Dept Chem Engn, Bath BA2 7AY, Avon, England
[2] Univ Cambridge, Dept Chem Engn, Cambridge CB2 3RA, England
来源：
POWDER TECHNOLOGY | 2000年 / 108卷 / 01期
关键词：
artificial neural network; paste extrusion; extrusion pressure;
D O I：
10.1016/S0032-5910(99)00254-5
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
A three layer feed-forward artificial neutral network (ANN) model was used for the description of extrusion pressure. The studies employ experimental data obtained from capillary flow experiments using a paste containing 5A zeolite, bentonite and water. On comparing the experimental data, the predictions using the Benbow-Bridgwater equation and the ANN model predictions, it is found that the ANN model is capable of predicting the extrusion pressure well. The neural network model shows how the significant parameters influencing extrusion pressure can be found. (C) 2000 Elsevier Science S.A. All rights reserved.
引用
收藏
页码：65 / 73
页数：9
相关论文
共 50 条
  • [21] Prediction of hepatitis C using artificial neural network
    Jajoo, R
    Mital, D
    Haque, S
    Srinivasan, S
    [J]. METMBS'01: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICS AND ENGINEERING TECHNIQUES IN MEDICINE AND BIOLOGICAL SCIENCES, 2001, : 25 - 31
  • [22] Prediction of air pollutants by using an artificial neural network
    Sang Hyun Sohn
    Sea Cheon Oh
    Yeong-Koo Yeo
    [J]. Korean Journal of Chemical Engineering, 1999, 16 : 382 - 387
  • [23] Thermal cracking prediction using artificial neural network
    Zeghal, M.
    [J]. PAVEMENT CRACKING: MECHANISMS, MODELING, DETECTION, TESTING AND CASE HISTORIES, 2008, : 379 - 386
  • [24] Prediction of the plasma distribution using an artificial neural network
    Li Wei
    Chen Jun-Fang
    Wang Teng
    [J]. CHINESE PHYSICS B, 2009, 18 (06) : 2441 - 2444
  • [25] Flood Modelling and Prediction Using Artificial Neural Network
    Sanubari, Awal Rais
    Kusuma, Purba Daru
    Setianingsih, Casi
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND INTELLIGENCE SYSTEM (IOTAIS), 2018, : 227 - 233
  • [26] PREDICTION OF PIPE WRINKLING USING ARTIFICIAL NEURAL NETWORK
    Chou, Z. L.
    Cheng, J. J. R.
    Zhou, Joe
    [J]. PROCEEDINGS OF THE ASME INTERNATIONAL PIPELINE CONFERENCE 2010, VOL 4, 2010, : 49 - +
  • [27] The prediction of meteorological variables using artificial neural network
    Ahmet Erdil
    Erol Arcaklioglu
    [J]. Neural Computing and Applications, 2013, 22 : 1677 - 1683
  • [28] Prediction of perioperative transfusions using an artificial neural network
    Walczak, Steven
    Velanovich, Vic
    [J]. PLOS ONE, 2020, 15 (02):
  • [29] Prediction of semen quality using artificial neural network
    Badura, Anna
    Marzec-Wroblewska, Urszula
    Kaminski, Piotr
    Lakota, Pawel
    Ludwikowski, Grzegorz
    Szymanski, Marek
    Wasilow, Karolina
    Lorenc, Andzelika
    Bucinski, Adam
    [J]. JOURNAL OF APPLIED BIOMEDICINE, 2019, 17 (03) : 167 - 174
  • [30] Prediction of pavement performance using artificial neural network
    Wang, Y.L.
    Wang, B.G.
    [J]. Xi'an Gonglu Jiaotong Daxue Xuebao/Journal of Xi'an Highway University, 2001, 21 (01):
12345