Compressive strength is an important indicator for evaluating the quality of pellet ore,and it is also the core control objective of pellet production.However,its detection cycle is long and the control is seriously lagging behind.Therefore,accurate real-time prediction of pellet compressive strength is of great significance for improving and stabilizing pellet quality.A prediction method of pellet compressive strength based on Filter and Wrapper mixed feature parameter selection combined with Bayesian optimization algorithm was proposed.Field production data was used to train and test the model.The prediction results show that:feature selection and the introduction of Bayesian optimization algorithm can significantly improve the prediction accuracy of the model.The gradient Boosting decision tree(GBDT)model based on feature selection and Bayesian optimization has the best fitting effect,with the prediction accuracy of 95.31%,laying a good foundation for the optimization and control of pellet quality.
compressive strength of pelletgradient boosting decision treefeature selectionmodel prediction
NETL
NSTL
万方数据
