A BP Neural Network Predictor Model for Desulfurizing Molten Iron

• Published in: Advanced Data Mining and Applications pp. 728 - 735
• Main Authors: Rong, Zhijun, Dan, Binbin, Yi, Jiangang
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial Neural Network; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Hide Neuron; Memory organisation. Data processing; Molten Iron; Operation Parameter; Software; Steel Plant; Backpropagation algorithm; Data analysis; Software package; Feasibility; Neural network; Steel; Data mining; Modeling; Optimization; Operating conditions; Production process
• ISBN: 354027894X; 9783540278948
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11527503_86

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.