Study and analysis of wastewater treatment in the refining and chemical industry based on multidimensional data analysis

Abstract

To improve the efficiency of wastewater treatment in the refining chemical industry, this paper designs a wastewater treatment process in the refining chemical industry based on multidimensional data analysis to guarantee the optimal operation of wastewater treatment operations. In the process of multidimensional data analysis, triangular polynomials are introduced to calculate the robust distance of high-dimensional data and construct a multidimensional data model. The optimal multidimensional clustering levels are divided using data operations to obtain high-dimensional data sequences in the multidimensional data set to accelerate data clustering. A parallel coordinate system realizes the visual representation of the wastewater treatment process stems to improve the capacity representation of wastewater treatment data. Simulation analysis was conducted to verify the treatment effect of the wastewater treatment process based on multidimensional data analysis in the refining and chemical industries. The results showed that the process designed in this paper resulted in a dissolved organic matter fraction concentration of 16.28, and the humic acid-like fraction increased by 36.2%, which was significantly higher than the protein-like fraction of 21.6%. And among all protein-like fractions, only C4 was positively and significantly correlated with fluorescent organic matter at the P < 0.05 level. It can be seen that the multidimensional data analysis model is conducive to promoting the development of wastewater treatment technology in the refining chemical industry and ensuring that the refining chemical industry steps into a circular and sustainable development track.

Keywords

• multidimensional data model
• triangular polynomial
• high-dimensional data series
• dissolved organic fraction concentration
• wastewater treatment process
• 62-07