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Timeliness and Stability-Based Operation Optimization for Copper Flotation Industrial Process
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AI
DOI：10.1109/TIM.2022.3225924.png)
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Abstract
En 中文
Making full use of data knowledge for operation optimization is a challenging problem in industrial production, which has a decisive significance to improve the level of industrial automation. Meanwhile, the timeliness and stability of operation optimization for the copper flotation industrial process is one of the most concerned topics. In this article, a framework of the timeliness and stability-based operation optimization for the copper flotation industrial process is constructed, which integrates data processing, category analysis, knowledge mining (KM), and operation optimization. Then, an improved density peak clustering (IDPC) strategy is investigated that the satisfactory categories can be obtained compared with traditional clustering methods. After, KM and information entropy (IE) techniques are applied to mine the valuable operation rules and construct the adjustment decision tables for each category. Next, both the strategy optimization method and the strategy priority method are investigated for operation optimization with the guarantee of the timeliness and stability of the copper flotation industrial process as much as possible. Later, the universality and computational complexity of the proposed algorithms are analyzed. Finally, the effectiveness of the proposed methods is verified by the data experiments on a real-world copper flotation industrial process.
Keywords:
Copper
Optimization
Production
Clustering algorithms
Data mining
Ores
Automation
Copper flotation process
improved density peak clustering (IDPC)
knowledge mining (KM)
operation optimization
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W