In actual sinter production, batching is a complex metallurgical and mathematical problem. Aiming at the problem of the precising batching of iron ore in the process of sintering batching, an automatic batching algorithm based on a Cartesian product to batch sinter was proposed for the first time. When the algorithm is applied to the sintering batching process, a complete batching scheme can be obtained, which can realize the organic combination with other calculation processes, can effectively save the manpower and material cost of sintering batching, and is of great significance to the comprehensive use of iron ore resources. Taking the actual sintering production batching of a domestic iron and steel plant as an example, according to the batching requirements compared with various ore batching schemes, combined with the actual production situation, the automatic batching optimization algorithm based on a Cartesian product is applied to build a mathematical model of sintering batching. Through the algorithm test, the practicability of the automatic batching algorithm is verified. In addition, the automatic batching algorithm based on a Cartesian product has good performance in other batching fields.

The adjustment of sintering raw materials has a decisive influence on the composition of blast furnace slag and the properties of sinter. In order to smelt high-quality molten iron in the blast furnace, the composition of the sinter must be properly adjusted so that the composition of the blast furnace slag and the metallurgical properties of the sinter are optimal for the quality of the iron and are conducive to the smooth operation of the blast furnace. In view of the huge difference in the quality and price of sintering raw materials, this paper proposes an automatic sintering ore blending model to quickly configure sintering raw materials according to the requirements of the production line. This method is based on the calculation process of blast furnace charge, combined with the constraints of process composition and cost performance, to establish a multi-decision sintering ore blending model based on the OLS(Ordinary least squares) algorithm to automatically screen from available raw materials and give the sinter that meets the requirements of the furnace. The plan finally makes TFe, CaO, MgO, SiO₂, TiO₂, Al₂O₃, P, Mn, Na₂O, K₂O, Zn, and other components meet the requirements of the production line, and meet the cost performance requirements of the enterprise for sinter. The model can complete the screening and proportioning of 43 kinds of raw materials within 10 s, and its performance can meet the requirements of the production of variable materials. Combined with an example, a comparative analysis experiment is carried out on the accuracy and practicability of the designed sintering and ore blending model. The experimental results show that the accuracy and efficiency of the method proposed in this paper are higher than those of the current ore blending scheme designed by enterprise engineers. This method can provide an effective reference for the stable operation of the sintering production line.

The quality control process for sintered ore is cumbersome and time- and money-consuming. When the assay results come out and the ratios are found to be faulty, the ratios cannot be changed in time, which will produce sintered ore of substandard quality, resulting in a waste of resources and environmental pollution. For the problem of lagging sinter detection results, Long Short-Term Memory and Genetic Algorithm-Recurrent Neural Networks prediction algorithms were used for comparative analysis, and the article used GA-RNN quality prediction model for prediction. Through correlation analysis, the chemical composition of the sintered raw material was determined as the input parameter and the physical and metallurgical properties of the sintered ore were determined as the output parameters, thus successfully establishing a GA-RNN-based sinter quality prediction model. Based on 150 sets of original data, 105 sets of data were selected as the training sample set and 45 sets of data were selected as the test sample set. The results obtained were compared to the real value with an average prediction error of 1.24 for the drum index, 0.92 for the low-temperature reduction chalking index (RDI), 0.95 for the reduction index (RI), 0.40 for the load softening temperature T_10%, and 0.43 for the load softening temperature T_40%, with all within the running time thresholds. The study of this model enables the prediction of the quality of sintered ore prior to sintering, thus improving the yield of sintered ore, increasing corporate efficiency, saving energy, and reducing environmental pollution.