State estimation and predictive control of fed-batch cultures of hybridoma cells

Dewasme, Laurent; Fernandes, Sofia Afonso; Amribt, Zakaria; Santos, Lino O.; Bogaerts, Philippe; Wouwer, Alain Vande

doi:10.1016/j.jprocont.2014.12.006

Cited by 37 publications

(33 citation statements)

References 25 publications

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“…However, this large computational time (up to several minutes) is acceptable for such processes due to the lengthy process time (typically in weeks). In addition, NMPC employed to address parametric uncertainties using min-max optimization coupled with unscented Kalman filter (UKF) resulted in better performance and also dealt with sensor unavailability issue for fed batch processes efficiently [66]. Researchers have also used MPC for real-time control of quality attributes [67].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Bioprocess Control: Current Progress and Future Perspectives

Rathore

Mishra

Nikita

et al. 2021

Life

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Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

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Section: Model Predictive Controlmentioning

confidence: 99%

Bioprocess Control: Current Progress and Future Perspectives

Rathore

Mishra

Nikita

et al. 2021

Life

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“…Model uncertainties need to be captured for the bioprocess optimization (Liu and Gunawan, 2017). Nonlinear model predictive control (NMPC) and observation of non-measurable states based on an unscented Kalman filter (UKF) were used in (Dewasme et al, 2015).…”

Section: Bioprocess Modellingmentioning

confidence: 99%

Intelligent dynamic simulation of fed-batch fermentation processes

Juuso

2020

Linköping Electronic Conference Proceedings

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Batch bioprocesses are difficult to model due to strong nonlinearities, dynamic behaviour, lack of complete understanding and unpredictable disturbances. A cell produces more cells, chemical products and heat from chemical substrates. Typical growth characteristics include several phases whose appearances and lengths depend on the type of organisms and the environmental conditions. Large differences exist between different fermentation runs. The simulator developed for fed-batch fermentation processes consists of three interacting dynamic models, each with three phase specific versions. The models predict dissolved oxygen concentration, oxygen transfer rate and concentration of carbon dioxide in the exhaust gas through the whole process, by using only the control variables as inputs. A decision system based on fuzzy logic to provide smooth gradual changes between phases. The detection of the changes between process phases is improved by using the intelligent trend analysis. The dynamic simulator is suitable for an online forecasting tool in connection with the real process. The operation is based on the ideas of model predictive control (MPC): the previous online measurements on a chosen horizon are used for constructing a starting point and the simulator predicts the operation on a chosen prediction horizon by using the planned control actions. The simulation is started on fairly long time intervals.

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“…Although a specific sensor could be available, this kind of hardware is usually expensive. According to this, there are many studies for state estimation in chemical and biochemical processes (Bogaerts and Coutinho, 2014, Bogaerts and Wouwer, 2003, Dewasme et al, 2015, Hulhoven et al, 2006, Lara-Cisneros et al, 2016, Heidarinejad et al, 2012, Kravaris et al, 2007, Hulhoven et al, 2008, Oliveira-Esquerre et al, 2002, Pantano et al, 2017b.…”

Section: Neural State Estimation Designmentioning

confidence: 99%

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

Fernández¹,

Pantano²,

Rossomando³

et al. 2019

Braz. J. Chem. Eng.

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In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.

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State estimation and predictive control of fed-batch cultures of hybridoma cells

Cited by 37 publications

References 25 publications

Bioprocess Control: Current Progress and Future Perspectives

Bioprocess Control: Current Progress and Future Perspectives

Intelligent dynamic simulation of fed-batch fermentation processes

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

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