Application of neural network techniques for modeling of blast furnace parameters
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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| Format: | Thesis |
| Sprog: | eng |
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Massachusetts Institute of Technology
2005
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| Online adgang: | http://hdl.handle.net/1721.1/17490 |
| _version_ | 1826217588656439296 |
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| author | Dhond, Anjali, 1977- |
| author2 | Nishikant Sonwalker. |
| author_facet | Nishikant Sonwalker. Dhond, Anjali, 1977- |
| author_sort | Dhond, Anjali, 1977- |
| collection | MIT |
| description | Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000. |
| first_indexed | 2024-09-23T17:06:03Z |
| format | Thesis |
| id | mit-1721.1/17490 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:06:03Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/174902019-04-12T16:05:35Z Application of neural network techniques for modeling of blast furnace parameters Dhond, Anjali, 1977- Nishikant Sonwalker. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2005-06-02T15:26:55Z 2005-06-02T15:26:55Z 2000 2000 Thesis http://hdl.handle.net/1721.1/17490 46851447 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 96 leaves 3644815 bytes 3644624 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Dhond, Anjali, 1977- Application of neural network techniques for modeling of blast furnace parameters |
| title | Application of neural network techniques for modeling of blast furnace parameters |
| title_full | Application of neural network techniques for modeling of blast furnace parameters |
| title_fullStr | Application of neural network techniques for modeling of blast furnace parameters |
| title_full_unstemmed | Application of neural network techniques for modeling of blast furnace parameters |
| title_short | Application of neural network techniques for modeling of blast furnace parameters |
| title_sort | application of neural network techniques for modeling of blast furnace parameters |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/17490 |
| work_keys_str_mv | AT dhondanjali1977 applicationofneuralnetworktechniquesformodelingofblastfurnaceparameters |