Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
This study presents computational intelligence techniques to reduce the computation error in determining the amount of alloy materials to be added during the ladle refining process to produce the specific steel grade. In this approach subtractive clustering technique is used primarily to compute the optimal cluster centers and then, the obtained optimal cluster centers are fed as input to the resilient backpropagation algorithm to reduce the computation error. The outcome indicates that the proposed method effectively ascertains the volume of alloy materials with reduced error. This technique can be used in steel making to help the operatives and also to reduce the wastage of alloy materials.
How to cite this article:
M. Pravin Kumar and S. Vijayachitra. Steel Process Modeling Based on Computational Intelligence Techniques.
DOI: https://doi.org/10.36478/ajit.2018.124.130
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2018.124.130