ARTIFICIAL NEURAL NETWORK USED IN THE MATERIAL AND MASS BALANCE IN THE REDUCTION ZONE DURING HCFMN II

1 WA KALENGA Michel Kalenga
Co-authors:
1 NYEMBWE Didier Kasongo
Institution:
1 https://doi.org/10.37904/metal.2024.4925
Conference:
33rd International Conference on Metallurgy and Materials, Orea Congress Hotel Brno, Czech Republic, EU, May 22 - 24, 2024
Proceedings:
Proceedings 33rd International Conference on Metallurgy and Materials
Pages:
603-608
ISBN:
978-80-88365-21-1
ISSN:
2694-9296
Published:
2nd December 2024
Proceedings of the conference have been sent to Web of Science and Scopus for evaluation and potential indexing.
Metrics:
168 views / 97 downloads
Abstract

Simple material and mass balance were efficiently drawn in high carbon ferromanganese production processes based on simple calculations. However the prediction on different products in different reactive zones using a mathematical model using artificial intelligence nueral network has not been well conducted. The current project has investigated means to use AI to predict products. The establishement of a mathematical model to predict each product in each zone is not a trivial exercise. To ease the prediction of every product quantitatively, MATLAB was used as an efficient tool to generate the mathematical programming model for each product. Theoretical assumptions were used to generate the mathematical programming model which was developed per reactive zone.

Keywords: Metallurgy, HCFeMn, Artificial Neural Network, prediction

© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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