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

ACS Omega ◽  
2022 ◽  
Author(s):  
Deepak Kumar ◽  
Vinod Kumar Saxena ◽  
Hari Prakash Tiwari ◽  
Barun Kumar Nandi ◽  
Abhilash Verma ◽  
...  
Keyword(s):  
Experimental Study ◽  
Reactivity Index ◽  
Metallurgical Coke ◽  
Coke Strength ◽  
Coke Reactivity ◽  
Coke Strength After Reaction

Effect of Hydro-Thermal Treatment with CaO Addition on the Coking Properties of Shengfu Coal

2011 ◽  
Vol 236-238 ◽  
pp. 835-838 ◽  
Author(s):  
Hua Zhou ◽  
Hong Tao Chang ◽  
Heng Fu Shui ◽  
Zhi Cai Wang ◽  
Chang Hui Lin ◽  
...  
Keyword(s):  
Thermal Treatment ◽  
Gas Permeability ◽  
Reactivity Index ◽  
Metallurgical Coke ◽  
Strength Index ◽  
Coke Strength ◽  
Coke Reactivity ◽  
Coking Properties ◽  
Different Temperatures ◽  
Coke Strength After Reaction

Shengfu (SF) coal was hydro-thermally treated with and without CaO addition at different temperatures and the crucible coking determinations were carried out to evaluate the effects. The results show that hydro-thermal treatment can obviously increase the micro-strength index (MSI) of the coke and particle coke strength after reaction (PSR), and decrease particle coke reactivity index (PRI), which are beneficial for metallurgical coke to increase the gas permeability in blast furnace. Addition of CaO during hydro-thermal treatment at 150-250°C greatly promotes the effects of hydro-thermal treatment. However, hydro-thermal treatment at 300°C with CaO results in dramatic decrease in MSI and PSR due to the enhanced hydrolysis reactions at this temperature by CaO to form strong cross-linking in the treated coal.

scholarly journals Simulation of Coke Quality Indicators Using Artificial Neural Network

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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