A Dynamic Time Warping Based Locally Weighted LSTM Modeling for Temperature Prediction of Recycled Aluminum Smelting

Abstract

In the process of recycled aluminum smelting, timely measurement of the temperature of the smelting furnace is very important for the aluminum yield and quality. However, it is sometimes difficult or costly to measure the temperature in a timely manner due to the high temperature and pressure environment in the furnace. To tackle this problem, a soft sensor modeling framework which combines an operating condition classification and a prediction model based on locally sample-weighted long short-term memory (LSTM) neural network is proposed. In the operating condition classification, a hybrid of dynamic time warping (DTW) based fuzzy c-means and convolutional neural network is used to cluster the training samples and to classify the query samples. In the prediction model, the dynamic time warping and locally sample-weighted technique are introduced to LSTM to solve time-varying and strong nonlinear problems of the process. By adopting the method of classifying the operating conditions of the query samples before temperature prediction, the prediction time can be effectively reduced and the prediction accuracy can be maintained. The results of the experiment show that the proposed method can meet the prediction accuracy and time efficiency requirements of the regenerative aluminum smelting furnace.

Keywords

• Temperature prediction
• just-in-time learning
• dynamic time warping
• fuzzy c-means
• long short-term memory neural network