Artificial neural nets for prediction of silicon content of blast furnace hot metal

1996 ◽  
Vol 67 (12) ◽  
pp. 521-527 ◽  
Author(s):  
Himanshu Singh ◽  
Nallamali Venkata Sridhar ◽  
Brahma Deo
Keyword(s):  
Blast Furnace ◽  
Silicon Content ◽  
Neural Nets ◽  
Hot Metal ◽  
Artificial Neural Nets ◽  
Artificial Neural

Prediction Model of Silicon Content in Blast Furnace Hot Metal Based on Artificial Neural Network and Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 3505-3508
Author(s):  
Yong Hua Cheng ◽  
Cai Wen Niu ◽  
Yu Xin Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Blast Furnace ◽  
Silicon Content ◽  
Absolute Error ◽  
Hot Metal ◽  
Predicting Model ◽  
Online Application ◽  
Artificial Neural

In this paper, a predicting model of silicon content in blast furnace hot metal is built based on artificial neural network and genetic algorithm, which optimizes the initialization weight values of neural network with genetic algorithm. This model can effectively improve the prediction accuracy and reduce the calculating time. Online application shows that the predicting model can effectively predict the silicon content in blast furnace hot metal and play an important role in production. when required absolute error was within ±0.03, the accuracy of model can reach 81.4%, and when absolute error was within ±0.04, the accuracy can reach 91.4%.

Artificial Neural Network Model for Predict of Silicon Content in Hot Metal Blast Furnace

2016 ◽  
Vol 869 ◽  
pp. 572-577 ◽  
Author(s):  
Sayd Farage David ◽  
Felipe Farage David ◽  
M.L.P. Machado
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Blast Furnace ◽  
Silicon Content ◽  
Flame Temperature ◽  
Reduction Process ◽  
Added Value ◽  
Hot Metal ◽  
Operational Parameter ◽  
Artificial Neural

The growing focus on the efficiency of the reduction process in blast furnace generates an alteration in the way they operate. This modifies the conditions of transfer of silicon for the hot metal and can cause problems in the added value of your product. To evaluate the changes of the operational parameters of the reduction on the conditions of transfer of silicon process a mathematical model based on artificial neural networks has been implemented. Through this model it was possible to predict the silicon content to determine the influence of each operational parameter. Artificial neural networks were able to predict the silicon content through parameters of the reduction in blast furnace process, and this was verified by the precision of this model. The ANN showed that Theoretical flame temperature, Pressure blow and Coke rate have a positive influence on the silicon content in hot metal, and the Hot metal rate is inversely proportional to the silicon content of the hot metal.

Recognition of Emotions in Gait Patterns by Means of Artificial Neural Nets

2008 ◽  
Vol 32 (2) ◽  
pp. 79-92 ◽  
Author(s):  
Daniel Janssen ◽  
Wolfgang I. Schöllhorn ◽  
Jessica Lubienetzki ◽  
Karina Fölling ◽  
Henrike Kokenge ◽  
...  
Keyword(s):  
Neural Nets ◽  
Gait Patterns ◽  
Artificial Neural Nets ◽  
Artificial Neural ◽  
Recognition Of Emotions

Two-dimensional defocusing correction using artificial neural nets

1998 ◽  
Author(s):  
Carlos Ortiz de Solorzano ◽  
Vicente Gonzalez ◽  
Andres Santos ◽  
Francisco del Pozo
Keyword(s):  
Neural Nets ◽  
Two Dimensional ◽  
Artificial Neural Nets ◽  
Artificial Neural

scholarly journals Arrangement of risk group with congenital fetus pathology using artificial neural nets

2003 ◽  
Vol 2 (2) ◽  
pp. 103-109
Author(s):  
O. S. Philippov ◽  
A. A. Kazantseva
Keyword(s):  
Pregnant Women ◽  
Risk Group ◽  
Pregnancy Termination ◽  
Maternal Serum ◽  
High Risk Group ◽  
Neural Nets ◽  
Congenital Defects ◽  
Artificial Neural Nets ◽  
Artificial Neural ◽  
Numeric Scale

The aim of the study is the evaluation of the significance of various risk factors for congenital fetus pathology (congenital defect – CD) and the development of risk numeric scale. 424 pregnant women with fetus CD and 520 pregnant women with fetus without congenital defects have been examined. Artificial neural nets have been used for investigation how various factors effect on pregnancy termination. It has been found that the important factors for congenital fetus defect risk are: age younger 18 years of an pregnant women, age older 35 years, noncarring of pregnancy in anamnesis, complicated clinical course of the first pregnancy trimester, CD cases in a family, ultrasonic markers of chromosome pathology in the first pregnancy trimester. Changes in maternal serum AFP, hCG and uE3 levels and blood flow disorders are important to forming high risk group. A numeric scale for CD risk has been developed on the basis of neuronic net analysis.

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