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What is Ensemble Models for Furnace Temperature Prediction about?

This paper presents three ensemble methods for predicting temperature settings in continuous annealing furnaces used in hot dip galvanising lines, emphasizing the need for accurate models to maintain product quality and reduce energy consumption. The proposed models, including additive regression, bagging, and dagging, are compared to traditional artificial intelligence models, demonstrating superior performance in prediction accuracy and generalization capacity. The study highlights the importance of histo

Author

esraa.mansour0258

Language

EN