Abstract
The work describes a data mining prediction model for control of carbon rate in a blast furnace iron making process. The proposed model not only enhances the control on fuel consumption, but also contributes to the thermal stability of the blast furnace. The carbon rate consists of coke rate plus coal rate and is measured per ton of hot metal. It is continuously monitored through measurement of the chemical composition of the gas exiting the furnace top known as “top gas”. This paper presents an advanced data mining model that uses the technique of artificial neural networks (ANN) which can accurately predict the carbon rate with the key factors impacting the process. The accuracy of the model largely depends on the volume and the quality of data. Further, the paper reconfirms that artificial neural networks (ANN) is a better prediction technique than the conventional regression methods.
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References
Wirth R, Hipp J (2000) CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining. Citeseer
Cao L, Zhang C (2006) Domain-driven data mining: a practical methodology. Int J Data Warehousing Mining 2(4):49–65. https://doi.org/10.4018/jdwm.2006100103
Lu H, Setiono R, Liu, H (1996) Effective data mining using neural networks. IEEE Trans Knowl Data Eng 8(6):957– 961. https://doi.org/10.1109/69.553163
Raju DV, Iqbal A, Kumar Trivedi A, Mukhopadhyay A (2007) Prediction of shape defects over length of cold rolled sheet using artificial neural networks. Ironmaking Steelmaking 34(2):166–176. https://doi.org/10.1179/174328107X155268
Dwarapudi S, Gupta PK, Gupta SS (2006) Application of artificial neural network model to predict reduction degradation index of iron oxide pellets. Ironmaking Steelmaking 33(6):500–506. https://doi.org/10.1179/174328106X94807
Vasudharini SV, Dash PS, Singh RK (2018) Prediction of carbonisation time in byproduct recovery coke ovens through gooseneck temperature measurement. Ironmaking Steelmaking. https://doi.org/10.1080/03019233.2017.1398501
Tosun E, Aydin K, Bilgili M (2016) Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alex Eng J 55(4):3081–3089
Tabatabaei S (2017) ` A probabilistic neural network-based approach for predicting the output power of wind turbines. J Exp Theor Artif Intell 29(2):273–285. https://doi.org/10.1080/0952813X.2015.1132272
Bauer P, Anzer G (2021) Data-driven detection of counterpressing in professional football. Data Min Knowl Disc 35(5):2009–2049. https://doi.org/10.1007/s10618-021-00763-7
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Bhattacharjee, A., Chattopadhyaya, S. (2022). Carbon Rate Prediction Model Using Artificial Neural Networks (ANN). In: Misra, R., Kesswani, N., Rajarajan, M., Veeravalli, B., Patel, A. (eds) Internet of Things and Connected Technologies. ICIoTCT 2021. Lecture Notes in Networks and Systems, vol 340. Springer, Cham. https://doi.org/10.1007/978-3-030-94507-7_8
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DOI: https://doi.org/10.1007/978-3-030-94507-7_8
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