Prediction of raw material batches for the production of clinker by means of artificial neural networks-Analysis of behaviour

This research deals with the analysis of the behaviour of artificial neural nets for prediction of raw material batches for the production of clinker. During the production several oxides that are present in raw materials in quarries have to be extracted for homogenization of the mixture suitable for clinker production. There is some delay between the measurement of the mixture and the material which is send from quarry. It is necessary to "send" precise chemical composition to ensure a good quality of clinker and resulting product-cement. Artificial neural networks (ANN) are suitable for such kind of time-independent prediction. The results show that not all oxides are necessary to use for the prediction of one oxide. The ANN were designed into several nets with one input similarly as pseudo neural networks are able to work. The results will be used for the purpose of further research of pseudo neural nets which currently serve only as classifiers. © ECMS Valen M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova (Editors)