Adequate process data is the foundation for implementing a data-driven modeling approach. Nevertheless

it is often impossible to meet this requirement for a new batch process. A new quality prediction approach based on process transfer was proposed to establish an accurate prediction model for new batch processes without sufficient data. With application of multivariate statistical regression analysis model

JY-PLS (Joint-Y partial least squares) regression model

this approach realized rapid modeling and quality prediction of new batch processes by construction of common latent variable space between similar batch processes and transfer of present data information from similar batch processes to new and non-modeled batch processes. During online application

the process transfer model was updated with online data and simultaneously estimated confidence interval of prediction error to determine stability of the prediction error. In order to overcome adverse effects on process transfer model caused by possible differences between batch processes

similar process data was eliminated gradually according to data similarity. Finally

effectiveness of the proposed approach was verified by penicillin process simulation.