2026-09-19T08:05:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/174902022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nishikant Sonwalker.
Dhond, Anjali, 1977-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-06-02T15:26:55Z
2005-06-02T15:26:55Z
2000
2000
http://hdl.handle.net/1721.1/17490
46851447
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 93-96).
This thesis discusses the predictions of various output variables in a blast furnace. It compares the ability of multi-layer perceptron neural networks for prediction with other blast furnace prediction techniques. The output variables: Hot Metal Temperature, Silicon Content, Slag Basicity, RDI, and +10 are all modeled using the MLP networks. Different solutions are proposed for preprocessing the original data and finding the most relevant input variables. The NNRUN software is used to find the best MLP neural network. Finally, methods to control the output variables in the blast furnace are examined and a derivative-based sensitivity analysis is discussed.
by Anjali Dhond.
M.Eng.
96 leaves
3644815 bytes
3644624 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Application of neural network techniques for modeling of blast furnace parameters
Thesis
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