A Model Study on Raw Material Chemical Composition to Predict Sinter Quality Based on GA-RNN

Abstract

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.

Data-Driven Methodology to Assess Raw Materials Impact on Manufacturing Systems Breakdowns

“…In the literature, numerous techniques exist for ETL and feature selection, but there is less exploration of works extending to raw material impact, covering manufacturing processes and supply chains (Y. Li et al, 2022). The ETL process has become a cornerstone of data management.…”

SCORN: Sinter Composition Optimization with Regressive Convolutional Neural Network

“…Zhang et al [7] developed an unsteady two-dimensional mathematical model for the iron ore sintering process and predicted sinter yield and strength by the method of numerical simulation. In view of the large time lag in the detection of sinter, Li et al [8] verified the relationship between the chemical compositions of the sintering raw material and the physical and metallurgical properties of the sinter through correlation analysis. However, the aforementioned mathematical models are mainly optimized from the aspects of sintering process parameters and properties and do not consider many other factors in the sintering process.…”