Dynamic correction of soft measurement model for evaporation process parameters based on ARMA

To address the significant soft measurement errors in traditional static models for evaporation process parameters, which are characterized by continuity and cumulativity, this paper proposes a dynamic correction method for soft measurement models of evaporation process parameters based on the autoregressive moving-average model (ARMA). Initially, the Powell's directional evolution (Powell-DE) algorithm is utilized to identify the autoregressive order and moving average order of the ARMA model. Subsequently, the prediction error of a mechanism-reduced robust least squares support vector machine ensemble model is utilized as input. An error time series prediction model, which compensates for the errors in the autoregressive moving average model, is then applied for dynamic estimation of the prediction error. Finally, an integration strategy using the entropy method is employed to combine the static soft measurement model, based on the mechanism-reduced robust least squares support vector machine, with the dynamic correction soft measurement model, which is based on the error time series compensation of the ARMA model. The new model is analyzed and validated using production data from an alumina plant's evaporation process. Compared to traditional models, the new model demonstrates significantly improved prediction accuracy and is capable of dynamic prediction of evaporation process parameters.