Mathematical Model of Forecasting the Coke Quality Indicators

2010 ◽  
Vol 297-301 ◽  
pp. 1290-1294 ◽  
Author(s):  
A.N. Dmitriev ◽  
Yu.A. Chesnokov ◽  
G.Yu. Arzhadeeva

A mathematical model for the forecasting of the basic metallurgical properties of coke depending on the quality indicators of coals and carbonization parameters is developed. The base of the model are the theoretical and statistical dependences of the physico-chemical and petrographic properties of initial coals on the reactivity and mechanical strength of the coke. These indicators depend on diffusion processes during carbonization. On the basis of initial data, the material and heat balance of the coking process are made and the product price is calculated. The full-function program includes: the initial database interactively formed on the basis of the coals quality certificates and concentrates; the calculating unit of forecasting of the coke properties, allowing to fulfil the mathematical model adapting for the concrete industrial conditions of carbonization process; the unit to calculate results. The calculated values, i.e. the Coke Reactivity Index (CRI) and the Coke Strength after Reaction (CSR) can be used further for technical and economical calculations in the balance logic-statistic model of the blast furnace smelting operation. Or using the feedback, it is possible to calculate necessary relations of the burden components for the carbonization.

2020 ◽  
Author(s):  
O.Yu. Sidorov ◽  
N.A. Aristova

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


Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shaohong Yan ◽  
Hailong Zhao ◽  
Liangxu Liu ◽  
Qiaozhi Sang ◽  
Peng Chen ◽  
...  

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.


2011 ◽  
Vol 312-315 ◽  
pp. 1198-1203 ◽  
Author(s):  
A.N. Dmitriev ◽  
Yu.A. Chesnokov ◽  
G.Yu. Arzhadeeva ◽  
Yu.P. Lazebnaya

The iron ore raw materials and coke quality is the basic reserve of improvement of blast furnace technology. Some of the quality indicators of iron ore raw materials and coke and their influence on the main parameters of the blast furnace smelting are considered in this paper.


ACS Omega ◽  
2022 ◽  
Author(s):  
Deepak Kumar ◽  
Vinod Kumar Saxena ◽  
Hari Prakash Tiwari ◽  
Barun Kumar Nandi ◽  
Abhilash Verma ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document