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Apart from strong nonlinearity and time-varying behaviors in industrial processes, the hidden time-delay information, which is unfortunately overlooked in most existing modeling methods, should also be taken into account in soft sensor modeling. In view of this, a novel soft sensor, referred to as local time-delay reconstruction based moving window time difference Gaussian process regression (LTR-MWTDGPR), is proposed in this paper. To deal with the time-delay, a fuzzy curve analysis based local time-delay parameter extraction procedure is performed along with a strategy of a moving window, which simultaneously captures the process time-varying feature. Then the local window training dataset and new query sample are reconstructed according to the time-delay parameter set at the next sampling instant. Afterwards, the time difference Gaussian process regression is employed to handle the drifting feature of local reconstructed dataset. The effectiveness and accuracy of the proposed LTR-MWTDGPR approach in predicting quality variables are verified through a real sulfur recovery unit and an industrial debutanizer column. [ABSTRACT FROM AUTHOR]

Process nonlinearity, multiple operating modes and time-varying characteristics often deteriorate the prediction performance of process models. In this article, a multi-mode moving-window Gaussian process regression (MWGPR) based approach for ARX modeling is proposed to effectively capture process nonlinearity or switching dynamics. The Gaussian mixture model (GMM) is first introduced to separate the data into different operating modes. Then the MWGPR strategy is applied to identify the local ARX model. Just-in-time learning (JITL) and dual updating are applied for more effective tracking of process dynamics. A simulation of a continuous fermentation process and a pilot scale experiment are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]