Abstract: Batch processes modeling， online monitoring with multivariate statistical analysis at the core have gradually became the research focus of process industry， however， the reliability of such methods would be affected by a large number of outliers in process data. Thus， an outlier detection method for batch processes based on partial robust Mregression was proposed to solve this issue. First， the robust prediction model was established based on maximum correntropy estimator. Then partial robust Mregression algorithm was utilized to calculate the model regression coefficients. Finally， Hampel identifier was used to analyze the final weights， and the outlier detection was fulfilled. The proposed outlier detection method was applied to a batch reaction process， and the experimental results indicated that effectiveness of the proposed method.

Key words: batch process, outlier, partial robust Mregression, correntropy, Hampel identifier