Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process View Full Text


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Article Info

DATE

2012-03

AUTHORS

Joong-Won Lee, Yoon-Seok Timothy Hong, Changwon Suh, Hang-Sik Shin

ABSTRACT

Online estimation of unknown state variables is a key component in the accurate modelling of biological wastewater treatment processes due to a lack of reliable online measurement systems. The extended Kalman filter (EKF) algorithm has been widely applied for wastewater treatment processes. However, the series approximations in the EKF algorithm are not valid, because biological wastewater treatment processes are highly nonlinear with a time-varying characteristic. This work proposes an alternative online estimation approach using the sequential Monte Carlo (SMC) methods for recursive online state estimation of a biological sequencing batch reactor for wastewater treatment. SMC is an algorithm that makes it possible to recursively construct the posterior probability density of the state variables, with respect to all available measurements, through a random exploration of the states by entities called 'particle'. In this work, the simplified and modified Activated Sludge Model No. 3 with nonlinear biological kinetic models is used as a process model and formulated in a dynamic state-space model applied to the SMC method. The performance of the SMC method for online state estimation applied to a biological sequencing batch reactor with online and offline measured data is encouraging. The results indicate that the SMC method could emerge as a powerful tool for solving online state and parameter estimation problems without any model linearization or restrictive assumptions pertaining to the type of nonlinear models for biological wastewater treatment processes. More... »

PAGES

359-369

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00449-011-0574-3

    DOI

    http://dx.doi.org/10.1007/s00449-011-0574-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1047926758

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/21792564


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