Investigation of sinter plant production rate and RDI by neural networks

摘要

Data from the Rautaruukki Raahe sinter plant were analyzed with feed-forward neural networks. The resulting models were used to investigate and optimize the sinter plant production rate and the reduction degradation index (RDI) that is an important sinter quality indicator for small blast furnaces. Especially, the effects of controllable parameters such as the chemical composition of sinter, physical conditions of raw materials and factors reflecting the sintering event were studied.