Investigation of sinter plant production rate and RDI by neural networks

Kinnunen, Kimmo; Laitinen, Petteri

doi:10.1051/metal:2005130

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…In recent years, some scholars have also applied intelligent algorithms to basic research on the influence of sintering ore-forming behavior. K Kinnunen et al [ 12 ] used neural networks to analyze data from sintering plant and studied important sintering quality indicators such as optimization of productivity and reduction degradation index (RDI). Wang Ai-min [ 13 ] combines the gray theory to weaken the volatility of data series and the advantages of neural network processing nonlinear adaptive information.…”

Section: Prediction Mechanism Of Sinter Drum Strengthmentioning

confidence: 99%

The Prediction of Sinter Drums Strength Using Hybrid Machine Learning Algorithms

Ren

Yang

Luo³

et al. 2022

Computational Intelligence and Neuroscience

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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.

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Section: Prediction Mechanism Of Sinter Drum Strengthmentioning

confidence: 99%