Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression

• A Gaussian mixture regression (GMR) based soft sensor model is developed for quality prediction in multiphase/multimode processes.
• Mode identification and localized regression are integrated into one model
• A heuristic algorithm is adopted for parameter initialization and component number optimization
• Feasibility and efficiency of the developed method is tested through a numerical example and two benchmark processes.

For complex industrial plants with multiphase/multimode data characteristic, Gaussian mixture model (GMM) has been used for soft sensor modeling. However, almost all GMM-based soft sensor modeling methods only employ GMM for identification of different operating modes, which means additional regression algorithms like PLS should be incorporated for quality prediction in different localized modes. In this paper, the Gaussian mixture regression (GMR) model is introduced for multiphase/multimode soft sensor modeling. In GMR, operating mode identification and variable regression are integrated into one model; thus, there is no need to switch prediction models when the operating mode changes from one to another. To improve the GMR model fitting performance, a heuristic algorithm is adopted for parameter initialization and component number optimization. Feasibility and efficiency of GMR based soft sensor are validated through a numerical example and two benchmark processes.