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International Journal An Automated Blowing Control System Using the Hybrid Concept of Case Based Reasoning and Neural Networks in Steel Industry
2015.07.31 16:38
| author | Jonghan Kim |
|---|---|
| 2nd author | / Eoksu Sim (Dept. of Industrial Engineering, Seoul National University) / Sungwon Jung (Dept. of Industrial Engineering, Seoul National University) |
| info | Lecture Notes in Computer Science (SCIE, 0.402), Vol. 3498, pp.807-812, 2005 |
| year | 2005 |
| c | IJ |
| 저널/학회 | LNCS |
| group | SCIE |
| keyword | steel industry, CBR, NN, blowing control system |
| abstract | Singapore, May 9-12, 2005 Temperature adjustment is one of the critical tasks affecting the quality of manufactured steel. This is controlled by the Basic Oxygen Furnace’s(BOF) blowing procedures. As many factors influence variations in temperature, it is often difficult to predict the blowing quantity necessary to achieve al required temperature. In this study, we assume the framework used by the intelligent blowing control system uses the Case Based Reasoning(CBR) and Neural Network(NN) to predict the appropriate blowing quantity in the BOF. Our proposed framework consists of three steps. First, we retrieve the similar cases for a new order requirement using CBR. Next, we train the NN engine with the selected case set. Finally, we predict the appropriate blowing quantity using a trained neural network. Experimental results show that the proposed framework performs more effectively than the framework without using CBR process. |