In a regenerative aluminum smelting furnace, real-time liquid aluminum temperature measurements are essential for process control. However, it is often very expensive to achieve accurate temperature measurements. To address this issue, a just-in-time learning-based triple-weighted regularized extreme learning machine (JITL-TWRELM) soft sensor modeling method is proposed for liquid aluminum temperature prediction. In this method, a weighted JITL method (WJITL) is adopted for updating the online local models to deal with the process time-varying problem. Moreover, a regularized extreme learning machine model considering both the sample similarities and the variable correlations was established as the local modeling method. The effectiveness of the proposed method is demonstrated in an industrial aluminum smelting process. The results show that the proposed method can meet the requirements of prediction accuracy of the regenerative aluminum smelting furnace.
Infrared ship target recognition technology can automatically detect, analyze and identify ship targets, which is suitable for various types of working environments. This paper takes ship target image as the research object, and measures the effect of existing infrared detection technology, traditional target detection technology and target detection technology based on depth learning through data comparison. For infrared detection and target recognition, its working principle is summarized, and its ability to quickly identify ship types in practical applications is verified. The application and development status of Sea area planning、 supervision and Military reconnaissance field are summarized, and the future development trend is prospected
In the production of aluminum, the regenerative aluminum smelting process is an important part for energy efficiency and product quality. Aluminum liquid temperature is a significant variable in the aluminum smelting process, and it is costly to measure timely because it requires protective temperature sensor. To handle this problem, a kind of modeling framework which combine a mechanism model with multi-scale kernel technique is proposed. First, the mechanism model is built for the aluminum liquid temperature by the energy conservation law and heat transfer mechanism. Since the mechanism model is based on some assumptions, it often results in unknown variables. Thus, the multi-scale kernel technique is used to obtain the unknown variables. Finally, a hybrid temperature prediction model is built by combining the multi-scale kernel and the mechanism model. The parameter identification of the hybrid model is described as an optimization problem, and a hybrid strategy-based sparrow search algorithm (HSSA) is proposed to solve this problem. The experiment results show that HSSA has higher convergence accuracy and stronger global search ability than the original sparrow search algorithm (SSA), and the proposed hybrid model can correctly estimate the aluminum liquid temperature.
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.INDEX TERMS Temperature prediction, just-in-time learning (JITL), dynamic time warping, fuzzy cmeans (FCM), long short-term memory neural network (LSTM).
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