A unified just-in-time learning paradigm and its application to adaptive soft sensing for nonlinear and time-varying chemical process

Abstract

In traditional just-in-time learning (JITL)-based adaptive soft sensors for complex chemical process, relevant sample selection and local model construction are carried out separately, resulting in that the developed predictive models are usually lack of optimality and validity. This has long been a tough issue for the JITL-based soft sensors. Therefore, this paper proposes a novel unified JITL paradigm based on representation learning and ridge regression. In the UniJITL, the tasks of relevant sample selection and local expert construction are integrated into a unified framework, where the two tasks are cooperatively considered and designed into one optimization problem. Moreover, an efficient solution to the optimization problem with guaranteed convergence is developed. The performance of the UniJITL is evaluated using two real-life chemical processes, and is compared with the state-of-the-art JITL methods, which effectively demonstrate both the predictive advantage and computational feasibility of the UniJITL.