Xinying Ren’s research while affiliated with North China University of Technology and other places

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 prediction model with the sinter drum strength as the evaluation index was established based on the index data and historical sintering data generated during the sintering process. The regression prediction model in the algorithm of machine learning was applied to the prediction of the strength of the sinter drum. After verifying the feasibility of drum strength prediction, different data preprocessing methods were used to preprocess the data. Ten regression prediction algorithms such as linear regression, ridge regression, regression tree, support vector regression, and nearest neighbor regression were used for predicting the sinter drum strength to obtain preliminary prediction results. By comparing the prediction results, the most suitable combinations of data preprocessing algorithms and prediction algorithms for sinter drum strength prediction is obtained. The prediction results show that, for the drum strength of the sinter, using the function data standardization algorithm for data preprocessing has the best effect. Then, using gradient boosting regression, random forest regression, and extra tree regression prediction algorithms resulted in higher prediction accuracy. On this basis, the regression prediction model algorithm parameters are optimized and improved. The parameters of the regression prediction algorithm that are most suitable for the prediction of sinter drum strength are obtained.

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 T10%, and 0.43% for the load softening temperature T40%, 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.