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This paper presents the results of research on the analysis of historical data from a vision system developed by the authors, installed inside a glass furnace in one of the packaging glassworks. In particular, the authors focused on analysis of the batch blanket asymmetry coefficient and the batch line position in the furnace tank. The information obtained from the vision system allows to see phenomena occurring in the glass furnace that were previously difficult to notice. Based on this, recommendations for operators have been formulated to facilitate the glass melting process more efficiently. Based on historical process data from the SCADA production system, several models were developed that allow the batch line position in the glass furnace to be estimated. The best accuracy was achieved using the model based on neural networks. Such a model can be used to optimise furnace operation, which is at present based on the experience of human operators.

Analysis of batch asymmetry and batch line position for the decision support in the glass melting process