Estimation of FeO content in the steel slag using infrared imaging and artificial neural network

Abstract The paper presents the novel method of fast and contactless estimation of iron oxide (FeO) concentration in steel slag during discharge process in steelworks. The infrared imaging and artificial neural network are the key tools used in the research. The imaging system consists of three cameras that work in the different wavelength ranges. The novel idea based on steel and slag radiation parameters extracted from the sequences of images is described. Radiation data that are the most correlated with FeO content in steelmaking slag are selected and use as input variables for ensemble of Artificial Neural Networks (ANN). Different parameters and configuration of the ANN were tested to define the most effective ones. The final result of FeO content estimation is presented and validated with the values obtained from the chemical analysis.