Comparison of State Estimation Techniques, Applied to a Biological Wastewater Treatment Process

Chai, Qian; Furenes, Beathe; Lie, Bernt

doi:10.3182/20070604-3-mx-2914.00061

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Furthermore, if M S i,G is written as M S i,G = S i,G V G , with V G assumed to be constant in time, (7) becomes, after some rearrangements, for CH 4 and O 2 as substrates…”

Section: Model Equationsmentioning

confidence: 99%

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Observer and controller design for a methane bioconversion process

Becker

Michiels

Knoors

et al. 2021

European Journal of Control

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“…Furthermore, if M S i,G is written as M S i,G = S i,G V G , with V G assumed to be constant in time, (7) becomes, after some rearrangements, for CH 4 and O 2 as substrates…”

Section: Model Equationsmentioning

confidence: 99%

“…(extended or unscented) Kalman filters [3,5,7,20,59] and (super-twisting) sliding mode observers [38,56].…”

Section: Introductionmentioning

confidence: 99%

Observer and controller design for a methane bioconversion process

Becker

Michiels

Knoors

et al. 2021

European Journal of Control

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“…The UKF is much simpler to implement as compared to the EKF and usually leads to better estimates [26,27]. Chai et al [28] also concluded that the UKF is simpler to implement and that it provides in biological waste water plants more accurate estimates than the EKF. Similar conclusions were made by Zhu and Feng [29] who applied EKF and UKF algorithms in the glycerol fermentation process.…”

Section: Dynamic State Estimators For Biomass and Its Specific Growthmentioning

confidence: 99%

Bioreactor control improves bioprocess performance

Simutis

Lübbert

2015

Biotechnology Journal

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The performance of bioreactors is not only determined by productivity but also by process quality, which is mainly determined by variances in the process variables. As fluctuations in these quantities directly affect the variability in the product properties, combatting distortions is the main task of practical quality assurance. The straightforward way of reducing this variability is keeping the product formation process tightly under control. Purpose of this keynote is to show that there is enough evidence in literature showing that the performance of the fermentation processes can significantly be improved by feedback control. Most of the currently used open loop control procedures can be replaced by relatively simple feedback techniques. It is shown by practical examples that such a retrofitting does not require significant changes in the well-established equipment. Feedback techniques are best in assuring high reproducibility of the industrial cultivation processes and thus in assuring the quality of their products. Many developments in supervising and controlling industrial fermentations can directly be taken over in manufacturing processes. Even simple feedback controllers can efficiently improve the product quality. It's the time now that manufacturers follow the developments in most other industries and improve process quality by automatic feedback control.

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“…The solution in the EKF is recursive in that each updated estimate of the state is computed from the previous estimate and the new input data, so only the previous estimate requires storage. Successful applications of this EKF to wastewater treatment processes have been reported [20][21][22][23][24]. However, when the EKF is applied to complex nonlinear wastewater treatment processes, a few implementation and numerical problems may arise, because the EKF is based on the principle of linearizing the system functions and measurements using the first two terms of the Taylor series expansions [25,26].…”

Section: Introductionmentioning

confidence: 99%

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

Lee

Hong

Suh

et al. 2011

Bioprocess Biosyst Eng

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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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Comparison of State Estimation Techniques, Applied to a Biological Wastewater Treatment Process

Cited by 7 publications

References 9 publications

Observer and controller design for a methane bioconversion process

Observer and controller design for a methane bioconversion process

Bioreactor control improves bioprocess performance

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

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