Adaptive soft sensor development for multi-output industrial processes based on selective ensemble learning

Soft sensors are vital for online predictions of quality-related yet difficult-to-measure variables in process industry. In this paper, an adaptive soft sensing approach based on selective ensemble learning is proposed for multi-output nonlinear and time-varying industrial processes, which we refer to as the selective ensemble learning for multi-outputs (SEL-MO). Specifically, an adaptive localization approach is developed for dealing with the process nonlinearity based on the statistical hypothesis testing theory, which can construct redundancy-free local model set. At the online operation stage, these constructed local models are partially combined under an adaptive selective ensemble learning framework, where the weightings of local models are query-sample oriented such that both gradual and abrupt changes in the process characteristics can be handled. In addition, an insensitivity strategy is proposed to enhance the online computational efficiency of the SEL-MO by avoiding the unnecessary search of the historical dataset. Case studies are carried out on a simulated fed-batch penicillin process and a real-life industrial primary reformer, and the results obtained demonstrate the effectiveness of the proposed method.