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Prediction of Coke Quality in Ironmaking Process: A Data Mining Approach

Coke is an indispensable material in Ironmaking process by blast furnace. To provide good and constant quality coke for stable and efficient blast furance operation is very important. Furthermore, a challenging issue in the cokemaking process is the prediction of coke quality. An accurate prediction can support production planning decision and reduce business operation costs.
The objective of this thesis is to apply the backpropagation neural network and the model tree techniques for predicting the strength and meansize of coke. Specifically, we developed the coke- physical&chemical-property model, coal-usage model, coal-group-usage model, and extended model for the target prediction task. Experimentally, we found that the coal-usage model achieves the highest Correlation Coefficient and the lowest Mean Absolute Error. Moreover, the model trees technique reaches higher accuracy and better efficiency than does the backpropagation neural network technique.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0828106-170027
Date28 August 2006
CreatorsHsieh, Hsu-huang
ContributorsChih-Ping Wei, Tsang-Hsiang Cheng, Han-Wei Hsiao, Yi-Cheng Ku
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0828106-170027
Rightsoff_campus_withheld, Copyright information available at source archive

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