Online quality prediction for cobalt oxalate synthesis process using least squares support vector regression approach with dual updating

Abstract

Online measurement of the average particle size is typically unavailable in industrial cobalt oxalate synthesis process, soft sensor prediction of the important quality variable is therefore required. Cobalt oxalate synthesis process is a complex multivariable and highly nonlinear process. In this paper, an effective soft sensor based on least squares support vector regression (LSSVR) with dual updating is developed for prediction the average particle size. In this soft sensor model, the methods of moving window LSSVR (MWLSSVR) updating and the model output offset updating is activated based on model performance assessment. Feasibility and efficiency of the proposed soft sensor are demonstrated through the application to an industrial cobalt oxalate synthesis process.