Artículos en Revistas

Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/538

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Author: search.filters.author.Vega, Jorge RubénSubject: search.filters.subject.Process monitoring

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    An adaptive soft sensor for on-line monitoring the mass conversion in the emulsion copolymerization of the continuous SBR process
    (Macromolecular Reaction Engineering, 2023) Sanseverinatti, Carlos Ignacio; Perdomo, Mariano Miguel; Clementi, Luis Alberto; Vega, Jorge Rubén
    Soft sensors (SS) are of importance in monitoring polymerization processes because numerous production and quality variables cannot be measured online. Adaptive SSs are of interest to maintain accurate estimations under disturbances and changes in operating points. This study proposes an adaptive SS to online estimate the mass conversion in the emulsion copolymerization required for the production of Styrene-Butadiene rubber (SBR). The SS includes a bias term calculated from sporadic laboratory measurements. Typically, the bias is updated every time a new laboratory report becomes available, but this strategy leads to unnecessarily frequent bias updates. The SS includes a statistic-based tool to avoid unnecessary bias updates and reduce the variability of the bias with respect to classical approaches. A control chart (CC) for individual determinations combined with an algorithmic Cusum is used to monitor the statistical stability of the average prediction error. The adaptive SS enables a bias update only when a loss of said statistical stability is detected. Several bias update methods are tested on a simulated industrial train of reactors for the latex production in the SBR process. The best results are obtained by combining the proposed CC-based approach with a previously developed Bayesian bias update strategy.
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    New contributions to non linear process monitoring through kernel partial least squares
    (2013) Vega, Jorge Rubén; Godoy, José Luis; Marchetti, Jacinto; Zumoffen, David
    The kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to nonlinear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a nonlinear process. The effectiveness of the proposed methods is confirmed by using simulation examples.