A BP Neural Network Predictor Model for Desulfurizing Molten Iron.

Conference PaperinLecture Notes in Computer Science 3584:728-735 · January 2005with7 Reads
Impact Factor: 0.51 · DOI: 10.1007/11527503_86 · Source: DBLP
Conference: Advanced Data Mining and Applications, First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005, Proceedings

    Abstract

    Desulfurization of molten iron is one of the stages of steel production process. A back-propagation (BP) artificial neural
    network (ANN) model is developed to predict the operation parameters for desulfurization process in this paper. The primary
    objective of the BP neural network predictor model is to assign the operation parameters on the basis of intelligent algorithm
    instead of the experience of operators. This paper presents a mathematical model and development methodology for predicting
    the three main operation parameters and optimizing the consumption of desulfurizer. Furthermore, a software package is developed
    based on this BP ANN predictor model. Finally, the feasibility of using neural networks to model the complex relationship
    between the parameters is been investigated.