An effective RB-PLSIELM model is proposed for modeling complex processes.
The bagging strategy is adopted to generate the sub-training data.
An improved ELM with the double parallel structure is adopted in the individual model.
RB-PLSIELM can achieve high prediction accuracy and stability.
RB-PLSIELM based soft sensor is developed to predict the key process variables in TEP and PTAP.