Liquidus Temperature of Aluminum Electrolytes Detected by Statistics

Lei WU


This paper presents a novel method of data-density algorithm of statistics to identify on-line accurately and reliably liquidus temperature of aluminium electrolyte. Data-density algorithm of statistics utilizes the principal of aggregate category to characterize the relationship between temperature curve and data-density of temperature curve. Comparing with traditional step-cooling algorithm of statistics, Data-density algorithm of statistics made it possible to identify liquidus temperature even under the bad circumstance that inflection point of temperature curve display slight or not present. The technical activities and experimental results shown that direct measurements of the liquidus temperature in industrial cells by data-density algorithm of statistics is clearly outperforms conventional step-cooling methods of statistics for determining the liquidus temperature.


Liquidus temperature, Data-density algorithm, Step-cooling algorithm, Statistics


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