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1 2007

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Neural networks modeling of oxidation layer thickness of titanium oxidized in fluidized bed

Szota M., Jasiński J., Mroziński T., Jeziorski L.

Advances in Materials Science

2007

Vol. 7, nr 4(14)

108-113

In this publication a manner of designed structure of neural networks and using it for modeling of oxidations process in fluidized bed is presented. This paper presents a neural network model used for designing the thickness of oxidation layer after oxidizing of titanium in fluidized bed. This process is very complicated and difficult as multi-parameters changes are non-linear. This fact and lack of mathematical algorithms describing this process makes modeling required curve of hardness by traditional numerical methods difficult or even impossible. In this case it is possible to try using artificial neural networks. The neural network structure is designed and prepared by choosing input and output parameters of process. The method of learning and testing neural network, the way of limiting nets structure and minimizing learning and testing error are discussed. Such prepared neural network model, after putting expected values of thickness of oxidation layer in output layer, can give answers to a lot of questions about running oxidizing process in fluidized bed. The neural network model can be used to build control system capable of on-line controlling running process and supporting engineering decision in real time. This paper presents different conception to obtain assumed titanium's thickness of oxidizing layer in fluidized bed. The specially prepared neural networks model could be a help for engineering decisions and may be used in designing oxidizing process in fluidized bed as well as in controlling changes of this process.