Simulation of Coke Quality Indicators Using Artificial Neural Network

KnE Engineering ◽

10.18502/keg.v5i3.6753 ◽

2020 ◽

Author(s):

O.Yu. Sidorov ◽

N.A. Aristova

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Quality Indicators ◽

Coke Quality ◽

Calculated Data ◽

Reactivity Index ◽

Coke Strength ◽

Coke Reactivity ◽

Coke Strength After Reaction ◽

Artificial Neural

The article shows the application of a neural network for modeling coke quality indicators Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR). Two optimization methods were used to train the neural network. The influence of the number of neurons on the simulation results was studied. The difference between experimental and calculated data on average does not exceed 2 %. The conclusion is made about the prospects of using a neural network to predict the values of CRI and CSR of coke. Keywords: artificial neural network, coke, coke reactivity index, coke strength after reaction

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Coke. Determination of coke reactivity index (CRI) and coke strength after reaction (CSR)

10.3403/30063631u ◽

2015 ◽

Keyword(s):

Reactivity Index ◽

Coke Strength ◽

Coke Reactivity ◽

Coke Strength After Reaction

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Application Study of Sigmoid Regularization Method in Coke Quality Prediction

Complexity ◽

10.1155/2020/8785047 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Shaohong Yan ◽

Hailong Zhao ◽

Liangxu Liu ◽

Qiaozhi Sang ◽

Peng Chen ◽

...

Keyword(s):

Neural Network ◽

Network Structure ◽

Coke Quality ◽

Coke Production ◽

Quality Parameters ◽

Reactivity Index ◽

Quality Prediction ◽

Sigmoid Function ◽

Blast Furnace Smelting ◽

Coke Strength

Coke is an indispensable and vital flue for blast furnace smelting, during which it plays a key role as a reducing agent, heat source, and support skeleton. Models of prediction of coke quality based on ANN are established to map the functional relationship between quality parameters Mt, Ad, Vdaf, St,d, and caking property (X, Y, and G) of mixed coal and quality parameters Ad, St,d, coke reactivity index (CRI), and coke strength after reaction (CSR) of coke. A regularized network training method based on Sigmoid function is designed considering that redundancy of network structure may lead to the learning of undesired noise, in which weights having little impact on performance and leading to overfitting are removed in terms of computational complexity and training errors. The cascade forward neural network with validation is found to be the most suitable one for coke quality prediction, with errors around 5%, followed by feedforward neural network structure and radial basis neural networks. The cascade forward neural network may play a guiding role during the coke production.

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