Acid concentration prediction model of steel pickling process based on orthogonal signal correction and robust regression

In order to get and control acid concentration values in cold-rolled strip steel pickling, a soft measurement method was proposed for real-time predicting the acid concentration. Because of the influence of irrelevant components and outliers in acid concentration data on the accuracy of the acid concentration prediction model, orthog-onal signal correction (OSC) and iterative weighted least squares (IRLS) regression were combined to build the model. Firstly, orthogonal signal correction was used to remove irrelevant components which have nothing to do with tile mea-sured variables. Then robust regression based on the iteratively reweighted least squares algorithm was applied in the model to reduce the influence of outliers. Finally, the prediction results were compared with multiple linear regression (MLR), IRLS, and OSC-MLR. It is found that OSC-IRLS has the best prediction accuracy. In comparison with MLR, the relative error of OSC-IRLS decrease from 1.82% to 1.17% in predicting the concentration of ferrous ions and from 5.87% to 4.73% in predicting the concentration of hydrogen ions. The proposed method has a better model prediction accuracy to meet the requirements of industrial applications.