Soft sensor modeling of steel pickling concentration based on IGEP algorithm

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.