Method for Predicting Burning Through Point Based on Encoder-Decoder Network

A method for predicting burning through point (BTP) based on an encoder-decoder network, comprising:

• a first step determining auxiliary variables related to BTP as input features, reading and preprocessing data of a sintering process from a database;
• reading data of exhaust-gas temperatures in bellows from the database, and calculating BTP and burning rising point (BRP) with a polynomial fitting method;
• a second step segmenting the data with a sliding window method based on the input features to construct training samples, verification samples, and test samples;
• a third step establishing a BTP prediction model based on the encoder-decoder network, and training the model by means of the training samples;
• and
• a fourth step reading, at current time k, on-line historical data from time k - th to time k in real time from a sensor and the database, collecting and preprocessing the auxiliary variables;
• reading the data of the exhaust-gas temperatures in the bellows from the time k -th to the time k, calculating BTP and BRP with a least square method;
• segmenting the data with the sliding window method to obtain data segments and establish a many-to-many sequence data set from the time k - th to the time k;
• and inputting the many-to-many sequence data set into the trained BTP prediction model to obtain a BTP prediction result within a next prediction time length tf from the time k.