Model predictive control of continuous yeast bioreactors using cell population balance models

Zhu, Guang-Yan; Zamamiri, Abdelqader M.; Henson, Michael A.; Hjortsø, Martin A.

doi:10.1016/s0009-2509(00)00208-6

Cited by 95 publications

(48 citation statements)

References 29 publications

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“…in the chemical industry [15]- [17] among others). These controllers are developed using the state-space formulation of MPC and only steady-state control is considered.…”

Section: B R-gpcmentioning

confidence: 99%

Active filtering of physiological motion in robotized surgery using predictive control

et al. 2005

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Abstract-This paper presents a predictive control approach to active mechanical filtering of complex, periodic motions of organs induced by respiration or heart beating in robotized surgery. Two different predictive control schemes are proposed for the compensation of respiratory motions or cardiac motions.For respiratory motions, the periodic property of the disturbance has been included into the input-output model of the controlled system so as to have the robotic system learn and anticipate perturbation motions. A new cost function is proposed for the unconstrained generalized predictive controller (GPC) where reference tracking is decoupled from the rejection of predictable periodic motions.Cardiac motions are more complex since they are the combination of two periodic non-harmonic components. An adaptive disturbance predictor is proposed which outputs future predicted disturbance values. These predicted values are used to anticipate the disturbance by using the predictive feature of a regular GPC.Experimental results are presented on a laboratory testbed and in vivo on pigs. They demonstrate the effectiveness of the two proposed methods to compensate complex physiological motion.

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“…in the chemical industry [15]- [17] among others). These controllers are developed using the state-space formulation of MPC and only steady-state control is considered.…”

Section: B R-gpcmentioning

confidence: 99%

Active filtering of physiological motion in robotized surgery using predictive control

et al. 2005

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“…More recently Fredrickson and Mantzaris (2002) and Fredrickson (2003) expanded the CPB formulation to account for the transitions between the different phases of the cell cycle. The CPB framework has been successfully used to model cell cycle dynamics (Faraday and Kirkby, 1992;Liu et al, 2007), capture cell population heterogeneity (Mantzaris, 2005b;Mantzaris, 2005a), predict and control the dynamics of fermentation processes in batch or continuous bioreactors (Godin et al, 1999;Mantzaris et al, 1999;Zhu et al, 2000;Mantzaris and Daoutidis, 2004;Sharifian and Fanaei, 2009), study aggregation dynamics in suspension cultures (Kolewe et al, 2012), as well as investigate in vitro cell proliferation patterns (Fadda et al, 2012b;Fadda et al, 2012a). The deterministic CPB framework just discussed does not include stochasticity in intracellular reaction occurrence.…”

Section: Introductionmentioning

confidence: 99%

Cell population balance and hybrid modeling of population dynamics for a single gene with feedback

Stamatakis¹

2013

Computers & Chemical Engineering

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Latest research on biological systems is steadily shifting from isolated single cells to entire cell populations. The latter are inherently heterogeneous, and their modeling requires approaches that explicitly account for this property. A comprehensive such approach is the cell population balance (CPB), which, however, is computationally expensive and becomes intractable for multivariable models.In this work, we demonstrate the use of model-reduction to efficiently simulate cell population heterogeneity in a genetic network of a single gene with feedback. Starting from a 4-species model we use singular perturbation analysis to derive a single equation for the intracellular protein concentration. We subsequently incorporate this equation to a hybrid model consisting of a CPB for the cell volume, and a continuum equation for the protein concentration. We finally compare the results obtained with the hybrid model with those of the full CPB, demonstrating the accuracy and computational efficiency of the hybrid methodology.

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“…Model predictive controller (MPC) is a multi-step predictive method and has been shown to perform well in controlling the process in unstable operating regions [11][12][13][14][15][16][17][18]. The MPC technique relies on process models to predict the behavior of the process over some future time interval, and the control calculations are based on these model predictions [19,20]. The models used for these predictions have usually been linear approximation of the process or experimentally obtained step response data.…”

Section: Introductionmentioning

confidence: 99%

Control of continuous fed-batch fermentation process using neural network based model predictive controller

Kiran

Jana

2009

Bioprocess Biosyst Eng

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Cell growth and metabolite production greatly depend on the feeding of the nutrients in fed-batch fermentations. A strategy for controlling the glucose feed rate in fed-batch baker's yeast fermentation and a novel controller was studied. The difference between the specific carbon dioxide evolution rate and oxygen uptake rate (Qc - Qo) was used as controller variable. The controller evaluated was neural network based model predictive controller and optimizer. The performance of the controller was evaluated by the set point tracking. Results showed good performance of the controller.

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Model predictive control of continuous yeast bioreactors using cell population balance models

Cited by 95 publications

References 29 publications

Active filtering of physiological motion in robotized surgery using predictive control

Active filtering of physiological motion in robotized surgery using predictive control

Cell population balance and hybrid modeling of population dynamics for a single gene with feedback

Control of continuous fed-batch fermentation process using neural network based model predictive controller

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