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
Variability in Metallurgical Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR): An Experimental Study
