@article{MAKHILLAJIT20181726721,
    title = {Steel Process Modeling Based on Computational Intelligence Techniques},
    journal = {Asian Journal of Information Technology},
    volume = {17},
    number = {2},
    pages = {124-130},
    year = {2018},
    issn = {1682-3915},
    doi = {ajit.2018.124.130},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2018.124.130},
    author = {M. Pravin and},
    keywords = {Alloy materials,ladle refining,resilient backpropagation,steel making,subtractive clustering,effectively},
    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.}
    }