Artificial Neural Network Prediction for Coke Strength after Reaction and Data Analysis
In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.
[1] G. R. Gainieva, L. D. Nikitin, M. M. Naimark, N. N. Nazarov, and G. P.
Tkachenko, Influence of Batch Composition and Clinkering Properties
on the Hot Strength of Coke and Blast-Furnace Operation, ISSN 1068-
364X, Coke and Chemistry, 2008, Vol. 51, No. 10, pp. 390-393. ┬®
Allerton Press, Inc., 2008.
[2] M.A. Díez, R. Álvarez and C. Barriocanal, Coal for metallurgical coke
production: Predictions of coke quality and future requirements for
cokemaking, Int J Coal Geol 50 (2002), pp. 389-412.
[3] Girish Kumar Jha,--Artificial Neural Networks--,Indian Agricultural
Research Institute.
[4] Mustafa Taskin, Halil Dikbas and Ugur Caligulu, --Artificial neural
network (ANN) approach to prediction of Diffusion bonding behavior
(shear strength) of Ni-Ti alloys manufactured by powder metallurgy
method--,Mathematical and Computational Applications, Vol. 13, No. 3,
pp. 183-191, 2008.
[5] http://www.mathworks.com
[6] D.E Rummelhart, G.E.Hinton, and R.J.Williams,--Learning internal
representations by error backpropagation--, Parallel Distributed
Processing,vol. 1 ,Cambridge,Mass:MIT Press,1986.
[7] H. Demuth, and M. Beale, Neural Network Toolbox For Use with
MATLAB. Natick, MA: The Mathworks Inc., 2001.
[1] G. R. Gainieva, L. D. Nikitin, M. M. Naimark, N. N. Nazarov, and G. P.
Tkachenko, Influence of Batch Composition and Clinkering Properties
on the Hot Strength of Coke and Blast-Furnace Operation, ISSN 1068-
364X, Coke and Chemistry, 2008, Vol. 51, No. 10, pp. 390-393. ┬®
Allerton Press, Inc., 2008.
[2] M.A. Díez, R. Álvarez and C. Barriocanal, Coal for metallurgical coke
production: Predictions of coke quality and future requirements for
cokemaking, Int J Coal Geol 50 (2002), pp. 389-412.
[3] Girish Kumar Jha,--Artificial Neural Networks--,Indian Agricultural
Research Institute.
[4] Mustafa Taskin, Halil Dikbas and Ugur Caligulu, --Artificial neural
network (ANN) approach to prediction of Diffusion bonding behavior
(shear strength) of Ni-Ti alloys manufactured by powder metallurgy
method--,Mathematical and Computational Applications, Vol. 13, No. 3,
pp. 183-191, 2008.
[5] http://www.mathworks.com
[6] D.E Rummelhart, G.E.Hinton, and R.J.Williams,--Learning internal
representations by error backpropagation--, Parallel Distributed
Processing,vol. 1 ,Cambridge,Mass:MIT Press,1986.
[7] H. Demuth, and M. Beale, Neural Network Toolbox For Use with
MATLAB. Natick, MA: The Mathworks Inc., 2001.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:52177", author = "Sulata Maharana and B Biswas and Adity Ganguly and Ashok Kumar", title = "Artificial Neural Network Prediction for Coke Strength after Reaction and Data Analysis", abstract = "In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.", keywords = "Artificial Neural Networks, backpropagation, CokeStrength after Reaction, Multilayer Perceptron.", volume = "4", number = "9", pages = "554-5", }