Laurent Dewasme

Nonlinear model predictive control of fed-batch cultures of micro-organisms exhibiting overflow metabolism: Assessment and robustness

Overflow metabolism characterizes cells strains that are likely to produce metabolites as, for instance, ethanol for yeasts or acetate for bacteria, resulting from an excess of substrate feeding and inhibiting the cell respiratory capacity. The critical substrate level separating the two different metabolic pathways is generally not well defined. This occurs for instance in Escherichia coli cultures with aerobic acetate formation. This work addresses the control of a lab-scale fed-batch culture of E. coli with a nonlinear model predictive controller (NMPC) to determine the optimal feed flow rate of substrate. The objective function is formulated in terms of the kinetics of the main metabolic pathways, and aims at maximizing glucose oxidation, while minimizing glucose fermentation. As bioprocess models are usually uncertain, a robust formulation of the NMPC scheme is proposed using a min–max optimization problem. The potentials of this approach are demonstrated in simulation using a Monte-Carlo analysis.

Monitoring of Bioprocesses: Mechanistic and Data-Driven Approaches

Nowadays, bioprocesses play a key role in the production of high-added value products in the pharmaceutical industry and measurements of the main component concentrations are of great importance for monitoring cell cultures. Although some hardware sensors are readily available, they often have several drawbacks, including purchase and maintenance costs, sample destruction, discrete-time measurements (instead of continuous ones), processing delay, calibration, sterilization, disturbances in the hydrodynamic conditions inside the bioreactor, etc. It is therefore of interest to use software sensors which reconstruct on-line some component concentrations in continuous time. Software sensors are based on the theory of state estimation. In this chapter, some state estimation techniques are reviewed, and two important situations are distinguished: (a) some component concentrations can be measured and a dynamic model of the bioprocess can be established and (b) only basic operating signals, such as pH, base addition, stirrer speed, feed rates, can be measured on-line and it is difficult (or even impossible) to build a mechanistic model linking these variables. In the latter case, a neural network approach appears particularly suitable, and is largely illustrated in this chapter by real-life experimental applications.

Adaptive extremum-seeking control applied to productivity optimization in yeast fed-batch cultures

Abstract In this study, we consider the problem of optimizing the productivity of fed-batch cultures of S. cerevisiae , which are characterized by strongly nonlinear kinetic models based on the bottleneck assumption of Sonnleitner and Kappeli [1986] and ethanol inhibition resulting from the fermentation of a possible excess of substrate feeding. In contrast with most published studies where the critical substrate level is assumed constant, we investigate the situation where this critical substrate level depends on the yeast respiratory capacity, and in turn on the oxygen and etahnol concentration in the culture medium. The challenge is thus to maintain the process at a high level of productivity by avoiding the accumulation of ethanol. To this end, an adaptive extremum seeking control scheme, coupled to an asymptotic observer, is developed based on Lyapunov stability arguments.

Adaptive extremum-seeking control of fed-batch cultures of micro-organisms exhibiting overflow metabolism

Abstract Abstract Overflow metabolism characterizes cells strains that are likely to produce inhibiting metabolites resulting from an excess of substrate feeding and a saturated respiratory capacity. The critical substrate level separating the two different metabolic pathways is generally not well defined. This paper proposes two non-model based extremum-seeking strategies preventing a too important accumulation of inhibiting metabolites in fed-batch cultures, by estimating the critical substrate level on the basis of 3 simple measurements related to the feeding, oxygen and carbon dioxide. A simple substrate controller based on Lyapunov stability arguments is then designed and tested in combination with the two extremum-seeking schemes.

A simple output-feedback controller for fed-batch cultures of microbial strains with overflow metabolism

Abstract An output-feedback controller is proposed for fed-batch cultures of microbial strains exhibiting overflow metabolism, i.e., cultures where cell strains may produce inhibiting metabolites when excess substrate is fed. The control objective is therefore to control the substrate feed rate in order to remain near the optimal substrate concentration during the whole culture. The proposed controller uses measurements of an output signal that can be calculated from on-line process data and determines the necessary dilution rate to maintain the output near its local, time-varying, maximum at all times. The controller is hybrid, combining a proportional plus integral controller with antiwindup, modified so that the error signal used in the feedback may change its sign according to a state machine which keeps track of whether the control action should be positive or negative. The transition is made smoothly to avoid chattering and based on an auxiliary variable which follows the local output maximum. Partial proof of convergence is given and simulations of the model of a yeast production system show the applicability of the proposed strategy.

Hybridoma cell culture optimization using nonlinear model predictive control

Abstract This work addresses the application of control systems to the optimization of a monoclonal antibodies (MAb) production chain. The attention is focused on the maximization of hybridoma fed-batch culture productivity. The proposed model presents kinetics showing strong nonlinearities through min-max functions expressing overflow metabolism. A nonlinear model predictive control (NMPC) algorithm, choosing the best trajectory over a moving finite horizon among different sequences of inputs, is suggested in order to optimize productivity. Sensitivities of selected objective functions are considered in a minimax robust version of the NMPC in order to choose the best configuration with respect to practical operating conditions.

Adaptive and Robust Linearizing Control Strategies for Fed-Batch Cultures of Microorganisms Exhibiting Overflow Metabolism

Linearizing control is a popular approach to control bioprocesses, which has received considerable attention is the past several years. This control approach is however quite sensitive to modeling uncertainties, thus requiring some on-line parametric adaptation so as to ensure performance. In this study, this usual adaptive strategy is compared in terms of implementation and performance to a robust strategy, where the controller has a fixed parametrization which is determined using the LMI framework so as to ensure robust stability and performance. Fed-batch cultures of yeast and bacteria are considered as application examples.

Nonlinear model predictive control of fed-batch cultures of micro-organisms exhibiting overflow metabolism

Overflow metabolism characterizes cells strains that are likely to produce inhibiting metabolites resulting from an excess of substrate feeding and a saturated respiratory capacity. The critical substrate level separating the two different metabolic pathways is generally not well defined. This occurs for instance in Escherichia coli cultures with aerobic acetate formation. This paper considers the control problem of a lab-scale E. coli biomass production. A preliminary study is presented to access the application of a multivariable nonlinear model predictive control approach to maximize the biomass production. This strategy is tested by simulation and its performance to control the bioreactor system is evaluated with various objective cost functions, and in the presence of noise and dead-time on the acetate concentration measurement.

Parameter Identification for State Estimation: Design of an Extended Kalman Filter for Hybridoma Cell Fed-Batch Cultures

The monitoring and optimization of hybridoma cell fed-batch cultures depend on the availability of appropriate on-line sensors for the main culture components. A simple and efficient approach to maintain hybridoma cultures in the optimal operating conditions is to regulate the substrate concentrations at the critical values (G=Gcrit and/or Gn=Gncrit) such as to control the hybridoma cells at the critical metabolism state. However, reliable glucose and glutamine probes are currently rare and/or very expensive on the market and it is necessary to design software sensors which are at same time cheap and reliable and that can be used for online measurement. In this study, the overflow metabolism model is used to develop an extended Kalman filter for online estimation of glucose and glutamine in hybridoma cell fed-batch cultures based on the considered available measurements (biomasses (on-line), lactate and ammonia (on-line or off-line)). The observability conditions are examined, and the performances are analysed with simulations of hybridoma cell fed-batch cultures. Glutamine estimation sensitivity is enforced by minimizing a cost function combining a usual least-squares criterion with a state estimation sensitivity criterion.

Robust Control of Yeast Fed-Batch Cultures for Productivity Enhancement

Abstract Abstract This work proposes a robust control strategy for the optimizing control of fed-batch cultures of S. cerevisae. The process dynamics is characterized by a nonlinear kinetic model based on the bottleneck assumption and ethanol inhibition for a possible excess of substrate feeding. The control strategy is based on the feedback linearization technique, where the resulting free linear dynamics is designed so as to ensure a certain robustness to plant parameter variations. A feedforward loop achieves the correct critical substrate value, which is a function of the ethanol and oxygen in the culture medium. In addition, a robust Luenberger-like observer is designed taking plant parameter variations into account. Numerical experiments demonstrate the potential of the proposed approach as a tool for control design of fed-batch cultures.