Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process |
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Authors: | Joong-Won Lee Yoon-Seok Timothy Hong Changwon Suh Hang-Sik Shin |
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Institution: | (1) Department of Civil and Environmental Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Republic of Korea;(2) Department of Civil and Environmental Engineering, London South Bank University, 103 Borough Road, London, UK;(3) Water Environment Center, KIST, 39-1 Hawolgok-dong, Wolsong-gil 5 Seongbuk-gu, Seoul, Republic of Korea |
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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. |
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