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 (LST… Show more
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