Overall models based on ensemble methods for predicting continuous annealing furnace temperature settings

The prediction of the set points for continuous annealing furnaces on hot dip galvanising lines is essential if high product quality is to be maintained and energy consumption and related emissions into the atmosphere are to be reduced. Owing to the global and evolving nature of the galvanising industry, plant engineers are currently demanding better overall prediction models that maintain accuracy while working with continual changes in the production cycle. This paper presents three promising prediction models based on ensemble methods (additive regression, bagging and dagging) and compares them with models based on artificial intelligence to highlight how good ensembles are at creating overall models with lower generalisation errors. The models are trained using coil properties, chemical compositions of the steel and historical data from a galvanising process operating in Spain. The results show that the potential benefits from such ensemble models, once configured properly, include high performance in terms of both prediction and generalisation capacity, as well as reliability in prediction and a significant reduction in the difficulty of setting up the model.