Commenced in January 2007
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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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058407Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2179
 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.
 M.A. D├¡ez, R. ├ülvarez 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.
 Girish Kumar Jha,--Artificial Neural Networks--,Indian Agricultural Research Institute.
 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.
 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.
 H. Demuth, and M. Beale, Neural Network Toolbox For Use with MATLAB. Natick, MA: The Mathworks Inc., 2001.