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We propose a soft sensing method using local partial least squares models with adaptive process state partition, referring to as the LPLS-APSP, which is capable of effectively handling time-varying characteristics and nonlinearities of processes, the two major adverse effects of common industrial processes that cause low-performance of soft sensors. In our proposed approach, statistical hypothesis testing is employed to adaptively partition the process state into the unique local model regions each consisting of certain number of consecutive-time data samples, and partial least squares is adopted to construct local models. Advantages of this adaptive strategy are that the number of local models does not need to be pre-defined and the local model set can be augmented online without retraining from scratch. Moreover, to improve the prediction accuracy, a novel online model adaptation criterion is proposed, which not only takes the current process dynamics into account, but also enables mining the information contained in the neighborhood of the query sample. The guidelines for tuning the model parameters are also presented. The LPLS-APSP scheme is applied to develop the dynamic soft sensors for a simulated continuous stirred tank reactor and a real industrial debutanizer column, and the results obtained demonstrate the effectiveness of this proposed approach, in comparison to several existing state-of-the-art methods, for online soft sensor design.

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Data-driven soft sensors have been widely used in process systems for delivering online estimations of hard-to-measure yet important quality-related variables. However, in many data-driven soft sensor applications, the process may be strongly nonlinear, and the number of labeled samples is limited, which are two major difficulties in developing high-accuracy soft sensors. To cope with this issue, a novel soft sensing method based on semi-supervised selective ensemble learning strategy is proposed. In the proposed method, the process is initially localized by an adaptive process state partition approach, where the information of unlabeled samples can be incorporated. Subsequently, a new distance to model (DM) criterion is defined for selective ensemble learning, which can overcome the drawback of the k nearest neighbor method. The newly defined DM criterion along with the incorporation of unlabeled samples can help to describe the relationships between query samples and local models more accurately, and therefore is able to provide higher estimation accuracy. The parameters of the proposed method are finalized automatically by the particle swarm optimization technique. The proposed method is investigated using three real-life benchmark datasets, and the simulation results demonstrate the effectiveness of the proposed method in dealing with nonlinear regression problems in the process system and in the urban pollutant monitoring area.

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