Aluminum smelting process furnace box temperature prediction method based on deep belief network

Abstract

The invention discloses an aluminium smelting process furnace box temperature prediction method based on a deep belief network. The method comprises the following steps that 1, a plurality of sets oforiginal data are acquired; 2, for the original data acquired in the step 1, abnormal data is eliminated, and noise is removed to obtain normal data; 3, for the normal data obtained in the step 2, thedeep belief network is used for feature extraction to obtain feature vectors; 4, the sets of feature vectors are divided into a training set and a testing set, and a prediction model is built, wherein through the sets of feature vectors in the training set, the prediction model is trained constantly to obtain a trained prediction model; 5, each set of feature vectors in the testing set is used for testing the trained prediction model, if the testing stability is good, the prediction model can be used for predicting a furnace box temperature, and if the testing stability is not good, the step3 is returned to. According to the aluminium smelting process furnace box temperature prediction method based on the deep belief network, the furnace box temperature can be predicted through other indexes easy to detect, components and parts are not prone to be damaged, and the economic benefits are good.

Description

technical field [0001] The invention relates to the technical field of aluminum smelting, in particular to a furnace temperature prediction method in an aluminum smelting process based on a deep belief network. Background technique [0002] The aluminum smelting process is the first production process of the entire aluminum alloy processing process. This process directly affects the subsequent steps of heat preservation, casting and processing, and ultimately has a great impact on the quality and performance of the product. During the aluminum smelting process, precise temperature control plays a vital role in casting aluminum ingots. In the process of aluminum smelting, the physical changes and chemical reaction processes are closely related to the temperature. If the smelting temperature is too low, it is not conducive to the dissolution of alloy elements and the discharge of gases and inclusions, which will increase the tendency to form segregation and undercasting; too h...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for predicting the furnace temperature in the aluminum smelting process based on a deep belief network, so as to overcome the existing methods of using thermocouples to detect the furnace temperature to obtain the trend of the temperature. The sensors and other components are easily damaged and the economic benefits are not good. high disadvantage

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