A BP Neural Network Predictor Model for Desulfurizing Molten Iron.
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.
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ABSTRACT: Satisfying the customers' need for manufacturing plants and the demand for high-quality products becomes more challenging nowadays. Manufacturers need to retain advanced attributes of their products by applying high-quality automation process. In this paper, a genetic programming (GP) approach is applied in order to develop three mathematical models for the force, torque and slab temperature in the hot-rolling industrial process. A frequency-based analysis using GP is performed to provide an insight into the process significant factors. The performance of the GP developed models is evaluated with respect to the known soft computing models explored in the literature. Experimental data were collected from the Ereğli Iron and Steel Factory in Turkey and used to test the performance of the GP models. Genetic programming shows better performance modelling capabilities compared with models-based artificial neural networks and fuzzy logic.International Journal of Computer Applications in Technology 01/2014; 49(3/4). DOI:10.1504/IJCAT.2014.062360
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ABSTRACT: Steel making industry is becoming more competitive due to the high demand. In order to protect the market share, automation of the manufacturing industrial process is vital and represents a challenge. Empirical mathematical modelling of the process was used to design mill equipment, ensure productivity and service quality. This modelling approach shows many problems associated to complexity and time consumption. Evolutionary computing techniques show significant modelling capabilities on handling complex non-linear systems modelling. In this research, symbolic regression modelling via genetic programming is used to develop relatively simple mathematical models for the hot rolling industrial non-linear process. Three models are proposed for the rolling force, torque and slab temperature. A set of simple mathematical functions which represents the dynamical relationship between the input and output of these models shall be presented. Moreover, the performance of the symbolic regression models is compared to the known empirical models for the hot rolling system. A comparison with experimental data collected from the Ere[gtilde]li Iron and Steel Factory in Turkey is conducted for the verification of the promising model performance. Genetic programming shows better performance results compared to other soft computing approaches, such as neural networks and fuzzy logic.International Journal of Computer Integrated Manufacturing 08/2013; 26(8). DOI:10.1080/0951192X.2013.766937 · 1.02 Impact Factor