Soft sensor modeling of steel pickling concentration based on IGEP algorithm

被引：0

作者：

Wang, Li ^{[1
]}

Xin, Yugang ^{[1
]}

Gao, Xunyang ^{[2
]}

Zhang, Lei ^{[1
]}

Sun, Jie ^{[3
]}

机构：

[1] Shenyang Univ Chem Technol, Sch Mech & Power Engn, Shenyang 110142, Liaoning, Peoples R China

[2] PetroChina Liaohe Oilfield Co, Drilling Technol Res Inst, Panjin, Peoples R China

[3] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang 110819, Liaoning, Peoples R China

来源：

CANADIAN METALLURGICAL QUARTERLY | 2024年

基金：

中国国家自然科学基金;

关键词：

Improved gene expression programming; acid pickling of strip steel; concentration prediction; soft measurement; support vector regression; INVERSE MODEL;

D O I：

10.1080/00084433.2024.2430067

中图分类号：

TF [冶金工业];

学科分类号：

0806 ;

摘要：

Accurate measurement of acid concentration is paramount for ensuring the quality of strip steel pickling. Online measurement, a method that reduces operational complexity and lags effectively, is gradually replacing offline measurement of acid concentration. In this study, an indirect soft sensor model based on the improved gene expression programming (IGEP) algorithm has been constructed, leveraging easily measurable indexes from a large-scale dataset. The IGEP-based model predicted that the mean absolute errors for H+ and Fe2+ concentrations were 1.72 and 1.98 g/L, respectively. Additionally, the goodness of fit values for the H+ and Fe2+ prediction models were 0.945 and 0.933, respectively. Compared with the model based on support vector regression (SVR), which is suitable for small samples, it was demonstrated that the IGEP-based model achieved better predictive performance. Taken together, our study has designed a more effective and practical model for determining the acid concentration of strip steel pickling, providing a new ideal choice for the steel industry, which is of profound significance in the concentration control of pickling solution and the production of strip steel.

引用

页数：13

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