Batch process plays an important role in the processing of specialty chemical

semi-conductor

food and biology industries for producing high-value-added products to meet rapidly changing market

which causes their monitoring and control to emerge as essential techniques.A novel method for uneven-length-stage batch process was proposed by using a new stage segmentation strategy based on k-means clustering method.First

the three-dimension data array is unfolded in variable ways

resulting in two-dimension forms.Then according to data correlations

two-dimension training data set is divided into many clusters by applying the conventional k-means clustering method

and sequentially principal component analysis (PCA) is performed for each separated cluster.For online monitoring

a similarity index is introduced to choose the most suitable local model.The effectiveness and utility of the proposed method was validated through the simulation benchmark of fed-batch penicillin production.