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Neural Network Prediction Model for Sinter Mixture Water Content Based on KPCA-GA Optimization

Authors :
Yuqian Ren
Chuanqi Huang
Yushan Jiang
Zhaoxia Wu
Source :
Metals, Vol 12, Iss 8, p 1287 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

The design and optimization of a sinter mixture moisture controlling system usually require complex process mechanisms and time-consuming field experimental simulations. Based on BP neural networks, a new KPCA-GA optimization method is proposed to predict the mixture moisture content sequential values with time more accurately so as to derive the optimal water addition to meet industrial requirements. Firstly, the normalized input variables affecting the output were dimensionalized using kernel principal component analysis (KPCA), and the contribution rates of the factors affecting the water content were analyzed. Then, a BP neural network model was established. In order to get rid of the randomness of the initial threshold and weights on the prediction accuracy of the model, a genetic algorithm is proposed to preferentially find the optimal initial threshold and weights for the model. Then, statistical indicators, such as the root mean square error, were used to evaluate the fit and prediction accuracy of the training and test data sets, respectively. The available experimental data show that the KPCA-GA model has high fitting and prediction accuracy, and the method has significant advantages over traditional neural network modeling methods when dealing with data sets with complex nonlinear characteristics, such as those from the sintering process.

Details

Language :
English
ISSN :
20754701
Volume :
12
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Metals
Publication Type :
Academic Journal
Accession number :
edsdoj.3652e5b5af40968edd3d57fc2c93f8
Document Type :
article
Full Text :
https://doi.org/10.3390/met12081287