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

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

Authors :
Yifan Li
Qunwei Zhang
Yi Zhu
Aimin Yang
Weixing Liu
Xinfeng Zhao
Xinying Ren
Shilong Feng
Zezheng Li
Source :
Computational Intelligence and Neuroscience. 2022:1-17
Publication Year :
2022
Publisher :
Hindawi Limited, 2022.

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.

Details

ISSN :
16875273 and 16875265
Volume :
2022
Database :
OpenAIRE
Journal :
Computational Intelligence and Neuroscience
Accession number :
edsair.doi.dedup.....41e41f9305714cb6f44d186aab20fe9c
Full Text :
https://doi.org/10.1155/2022/3343427