LAUSR

Soft sensing of quality parameters in process industries has been an active area of research for the past two decades. To improve the performance of soft sensors in the scenario of time varying process states, adaptation capability is incorporated into the soft sensor model. In this work, recursive (R), sliding window (SW) and just-in-time learning (JITL) frameworks are used for adaptive soft sensor design. A rarely explored modeling technique in the adaptation framework, the generalized regression neural network (GRNN) is used as a local modeling strategy. A bias update procedure is applied during the model adaptation activity to improve the prediction accuracy. Further, the performances of the developed models are tested against input–output data dimension mismatch along with various concept drift phenomena by considering a different number of labeled samples for inputs and outputs. The proposed adaptation strategy is applied on two benchmark industrial processes. Simulation results show that the GRNN local modeling approach combined with the bias update strategy gives higher prediction accuracy than other adaptive soft sensors proposed in the literature. Moreover, GRNN local modeling strategy using SW adaptation mechanism has the least computation time among the three adaptation methods due to the use of a low number of samples for model development.