A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

Honggui Han; Xiaolong Wu; Luming Ge; Junfei Qiao

doi:10.1016/j.cjche.2017.08.007

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A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

Process Systems Engineering and Process Safety | Updated：2026-01-06

- A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method
- Chinese Journal of Chemical Engineering Vol. 26, Issue 5, Pages: 1071-1077(2018)
- Affiliations：
  
  2 Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing,China,100124
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2017.08.007
  CLC：
- Published：2018
- Accepted：
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Honggui Han, Xiaolong Wu, Luming Ge, Junfei Qiao. A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method[J]. Chin.J.Chem.Eng., 2018, 26(5): 1071-1077.
DOI：

Honggui Han, Xiaolong Wu, Luming Ge, Junfei Qiao. A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method[J]. Chin.J.Chem.Eng., 2018, 26(5): 1071-1077. DOI： 10.1016/j.cjche.2017.08.007.

摘要

Abstract

In this study

a multivariate local quadratic polynomial regression (MLQPR) method is proposed to design a model for the sludge volume index (SVI). In MLQPR

a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables

and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover

a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally

the proposed method is applied to predict the SVI values in a real wastewater treatment process (WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.

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