Abstract
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
Highlights
It is known that metallurgical coke can be characterized by two parameters: Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR)
There is no reliable model for calculating CRI and CSR based on the characteristics of charge materials and coking mode
The solution of insufficiently formalized problems is possible using artificial neural networks. This approach was used in [2] to describe the quality of coke and in [4] to analyze the yield of chemical coking products
Summary
There is no reliable model for calculating CRI and CSR based on the characteristics of charge materials and coking mode In this regard, studies in the direction of establishing the dependence of coke quality on the characteristics of the charge are relevant. How to cite this article: O.Yu. Sidorov and N.A. Aristova, (2020), “Simulation of Coke Quality Indicators Using Artificial Neural Network” in III Annual International Conference ”System Engineering”, KnE Engineering, pages 21–28. The solution of insufficiently formalized problems is possible using artificial neural networks (see, for example, [3]) This approach was used in [2] to describe the quality of coke and in [4] to analyze the yield of chemical coking products. The data for the study and the results of the determination of CRI and CSR are provided by industrial enterprise and are shown in tables 1, 2
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