Optimization of PGNAA device and algorithm for testing basicity index of sinter mixture

Sinter is the main raw material in the blast furnace iron-making process, and basicity (CaO/SiO2) is an important quality index of sinter. Prompt gamma neutron activation analysis is a multi-elemental online detection technology that has been successfully applied in cement, coal, etc. Compared with cement as a raw material, sinter exhibits poor moderation ability and a large neutron absorption cross section. Therefore, cement detection devices are not suitable for sinter mixture detection. In this study, a prompt gamma neutron activation analysis equipment used for testing cement was re-optimized to render it suitable for measuring a sinter mixture. Using Monte Carlo simulation, the comprehensive detection efficiency of the detection device improved by 71.52%. Because of the gamma-ray self-shielding effect of the sinter mixture, the detection errors of CaO and SiO2 are significant. By applying the gamma-ray self-shielding correction algorithm, the detection accuracies improved, and their linear correlation coefficients R2 exceeded 0.99. Furthermore, by applying an improved analyzer to a sintering plant, the first-grade product rate of the factory increased by 4.64%.