A Soft Sensing Prediction Model of Superheat Degree in the Aluminum Electrolysis Production
| Autor: | Tao Sang, Xiaofang Chen, Hong Yu, Zhong Zou, Guoyin Wang, Jisen Yang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Measure (data warehouse)
business.industry Computer science Electrolytic cell Alloy Process (computing) 0102 computer and information sciences 02 engineering and technology Electrolyte engineering.material 021001 nanoscience & nanotechnology Aluminum industry 01 natural sciences Corrosion Specific strength Superheating Reduction (complexity) Data set 010201 computation theory & mathematics engineering Production (economics) Rough set 0210 nano-technology Process engineering business |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2018.8622489 |
| Popis: | Aluminum alloy is widely used in transportation, catering, industry, sports, health and other fields, because of their excellent high specific strength and corrosion resistance. Aluminum industry has been an important mainstay industry of a national economy. In the process of electrolytic aluminum, the superheat degree is a very important production target. When an electrolysis cell is working in the appropriate superheat degree state, the life of cell is prolonged and the amounts of aluminum released will enhanced. However, to measure the superheat degree is very difficult and the measured results cannot timely feedback to the process of production. To address the problem, a soft sensing prediction model of superheat degree is proposed in this paper, in which some new concepts such as the decay function of data weight, the credibility of a rule and the rule tree are introduced. Basically speaking, the processing of the soft sensing prediction model is mainly based on the rough set data analysis method and a tree data structure. The static rules are obtained from the history data by using the attribute reduction and value reduction method in the rough sets. The rule tree is updated timely based on the incremental data set accordingly. The effectiveness of the proposed model is verified with the aluminum production data provided by Shandong Weiqiao Aluminum Electrolysis limited company in China. |
| Databáze: | OpenAIRE |
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