Intelligent prediction of burning through point based on sparse representation pruning ensemble modeling
ZHOU Ping
WU Zhong-wei
ZHANG Rui-yao
WU Yong-jian
Abstract：The burning through point(BTP)is a crucial parameter in sintering process,which directly determines the quality of the final sinter.Since the BTP is difficult to directly detect online,it is of great significance to realize the online prediction of BTP through intelligent learning modeling and adjust the operating parameters on this basis to improve the quality of sinter.Aiming at this practical engineering problem,a Wrapper feature selection method based on the genetic algorithm is firstly proposed in this paper,which can select the feature combination that optimizes the subsequent predictive modeling performance as much as possible.Secondly,in order to solve the problem of easy overfitting in intelligent modeling of a single learner,a sparse representation pruning(SRP)ensemble modeling algorithm based on the random vector functional-link networks(RVFLNs)is proposed,namely SRP-ERVFLNs.The proposed method uses RVFLNs with fast modeling speed and good generalization performance as individual base learners,and perturbs the parameters of the base learner to increase the difference between the ensemble learning sub-models.At the same time,in order to further improve the generalization performance and computational efficiency of the ensemble model,a sparse representation pruning algorithm is introduced to achieve effective pruning of the ensemble model.Finally,the proposed SRP-ERVFLNs algorithm is used for prediction modeling of the BTP in the sintering process.Experiments using industrial data show that the proposed method has better prediction accuracy,generalization performance and computational efficiency than other methods.
Keywords：intelligent predictionfeature selectionensemble learningsparse representationpruningburning through point(BTP)random vector functional-link networks(RVFLNs)
Publication Date：2024-03-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 436-446 )