H. Meradi and S. Bouhouche and M. Lahreche
Prediction of Bath Temperature Using Neural Networks
920 - 924
2008
2
12
International Journal of Physical and Mathematical Sciences
https://publications.waset.org/pdf/5627
https://publications.waset.org/vol/24
World Academy of Science, Engineering and Technology
In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.
Open Science Index 24, 2008