Enhancing the reliability and accuracy of data-driven dynamic soft sensor based on selective dynamic partial least squares models

Abstract

Data-driven soft sensors have been widely applied to a broad range of process industries for virtually sensing difficult-to-measure but of-great-concern variables. However, it is still nontrivial to develop dynamic soft sensors with satisfactory performance. A crucial obstacle lies in strong dependence on quality and representativity of collected data. In other words, the predictive accuracy of the developed soft sensor could be quite sensitive to both offline training data and online unseen data, leading to unreliable and degraded generalization performance. In order to deal with such a troublesome issue, this paper proposes a framework for developing reliable dynamic soft sensor called selective dynamic partial least squares (SDPLS). The SDPLS consists of two-stage operations. At the offline stage, aided by intelligent optimization algorithm a model library is established through constructing various DPLS models, each of which accounts for certain ‘mode’. At the online stage, adaptive online model updating for adapting to the current working condition is carried out based on evaluating the performance of the stored individual models, where a correction scheme is also developed for bias elimination. Extensive case studies have been conducted based on a numerical example and two real-life industrial processes, and the results efficaciously demonstrate the effectiveness and promising application prospects of the proposed schemes.