Method and system for predicting the external temperature of a rotary kiln shell

Abstract

The external temperature (T n A method (100) for predicting a temperature (t), t≧0, comprising a training feature providing step (110) for providing a training feature dataset (Equation I) and a step of providing a training external temperature dataset (T n (t), t<0) and at least one model A for at least one slice n of the rotary kiln. n a model setup step (130) for setting up at least one algorithm A for at least one slice n of the rotary kiln (1) from a machine learning algorithm; n an algorithm selection step (150) for selecting at least one algorithm A n an algorithm selection step (150) for at least one slice n of the rotary kiln (1), wherein the algorithm selection step (150) includes model parameters (Equation II) and hyperparameters (Equation III); n , and at least one algorithm A for at least one slice n of the rotary kiln (1) in the trained state. n the training feature dataset (Equation IV) and the training external temperature dataset (T n an algorithm training step (170) for training by using at least a portion of the time series (t), t<0), wherein the algorithm training step (170) is based on supervised learning; and a predictive model A comprising at least one algorithm A for at least one slice n of the rotary kiln (1) in the trained state. n *, and a prediction feature providing step (180) for providing a prediction feature dataset (Equation V), and an external temperature dataset (Tn (t), t≧0) is used as the prediction model A n *, and a temperature prediction step (190) for making a prediction based on the prediction feature dataset (Equation V).

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