Design of a multi-model observer-based estimator for anaerobic reactor monitoring

Morel, E.; Tartakovsky, B.; Guiot, Serge R.; Perrier, Michel

doi:10.1016/j.compchemeng.2006.05.003

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…The input−output linearization method was then used to design a multi-model adaptive controller. Below is a brief description of the design procedure, while more details can be found elsewhere ( , ).…”

Section: Resultsmentioning

confidence: 99%

“…Multi-model outputs were defined by the weighted sums of the submodel outputs. The weights were calculated by a knowledge-based system, which used online measurements of biogas composition and reactor pH for process diagnosis (11).…”

Section: Design Of a Multi-model Adaptive Controllermentioning

confidence: 99%

“…This study presents an experimental demonstration of anaerobic reactor stabilization using short-term temperature increases to avoid reactor failure due to organic overload. Because overexposure of the mesophilic sludge to thermophilic conditions can be harmful to the mesophilic methanogens, timely temperature adjustments were provided by a multi-model adaptive controller whose design was based on the multi-model approach ( , ). This approach to process modeling is distinctly different from using comprehensive process models as it combines the simplicity of individual submodels with the flexibility of a knowledge-based system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temperature-Based Control of an Anaerobic Reactor Using a Multi-Model Observer-Based Estimator

Morel

Tartakovsky

Perrier

et al. 2007

Environ. Sci. Technol.

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This study presents a temperature-based control strategy for the stabilization of an anaerobic reactor during organic overloads. To prove feasibility of the proposed approach the rate of methane production was followed in batch activity tests and reactor runs during mesophilic-thermophilic transitions. Within the first 0.25-6 h of temperature augmentation, an increase in the rate of methane production was observed with higher rates measured under thermophilic (above 40 degrees C) conditions. However, 24 h after startup both in batch tests and reactor runs, the rate of methane production under thermophilic conditions was inferior to that under optimal mesophilic conditions (35 degrees C). Following these results, a control strategy based on short-term augmentation of the reactor temperature was proposed and tested in a 10 L UASB reactor. The control strategy employed a multi-model observer-based estimator to stabilize the effluent COD concentration during organic overloads. The temperature-based control resulted in an increased methanization rate and improved reactor stability overall.

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Section: Resultsmentioning

confidence: 99%

Section: Design Of a Multi-model Adaptive Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temperature-Based Control of an Anaerobic Reactor Using a Multi-Model Observer-Based Estimator

Morel

Tartakovsky

Perrier

et al. 2007

Environ. Sci. Technol.

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“…The weights were calculated by a knowledge-based system, which used on-line measurements of biogas composition and reactor pH for process diagnosis (Morel et al, 2006a). The influence of temperature on the microbial activity was modeled using a modified Arrhenius equation, which linked the biotransformation rate with the temperature in the following form:…”

Section: Multi-model Adaptive Controllermentioning

confidence: 99%

Control of an Anaerobic Mesophilic Reactor Using Periodic Temperature Variations

Tartakovsky

Morel

Dansereau

et al. 2007

IFAC Proceedings Volumes

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“…While detailed first principles equations for bioreactors exist in general, and for the Satorius Bioreactor in particular [2], the associated parameter estimation problem is quite a challenging one. There have been several implementations of parameter estimation techniques and other mathematical approaches for first principles modeling of industrial-scale growth [5][6][7][8]10], including one used by Sartorius [2], however, further contributions to these methods remain the subject of another work. The focus in the present manuscript is on leveraging data to build data driven dynamic models.…”

Section: Introductionmentioning

confidence: 99%

Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms

et al. 2020

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This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.

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Design of a multi-model observer-based estimator for anaerobic reactor monitoring

Cited by 11 publications

References 17 publications

Temperature-Based Control of an Anaerobic Reactor Using a Multi-Model Observer-Based Estimator

Temperature-Based Control of an Anaerobic Reactor Using a Multi-Model Observer-Based Estimator

Control of an Anaerobic Mesophilic Reactor Using Periodic Temperature Variations

Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms

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