Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

Abstract

The dynamic soft sensor based on a single Gaussian process regression (GPR) model has been developed in fermentation processes. However, limitations of single regression models, for multiphase/multimode fermentation processes, may result in large prediction errors and complexity of the soft sensor. Therefore, a dynamic soft sensor based on Gaussian mixture regression (GMR) was proposed to overcome the problems. Two structure parameters, the number of Gaussian components and the order of the model, are crucial to the soft sensor model. To achieve a simple and effective soft sensor, an iterative strategy was proposed to optimize the two structure parameters synchronously. For the aim of comparisons, the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process. Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.