Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square

• 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.

Some key variables in the complex chemical processes are very difficult to measure due to the nonlinearity, the disturbances, and the technological limitations. In order to accurately predict the difficult-to-measure variables, soft sensor based on a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square (RB-PLSIELM) is developed. Motivated by the ensemble ideas, the proposed RB-PLSIELM model is based on the bagging ensemble scheme to combine some individual nonlinear models integrating improved extreme learning machine with partial least square (PLSIELM). The sub-data for building the individual PLSIELM model are re-sampled from the original training data using the bagging tool. The problem of over-training in the PLSELM model can be avoided by using the bagging re-sampling technology. The proposed RB-PLSIELM model was demonstrated by applying it to predicting the key variables of the Tennessee Eastman Process (TEP) and the Purified Terephthalic Acid Process (PTAP). The simulation results obtained by RB-PLSIELM are compared with those obtained by the individual PLSELM model, the ELM model, and the partial least square regression (PLSR) model. Compared with the other models, the RB-PLSIELM can achieve higher prediction accuracy and stability.