Advanced nonlinear control strategies for a fermentation bioreactor used for ethanol production.

This study addresses the design of advanced control schemes implemented for a continuous fermentation process used to produce ethanol. Due to the inaccuracy of the models that express this complex process, a feasible controller is required to maximize the production of ethanol and to minimize its environmental impact, despite the existence of some significant uncertainties. Therefore, novel estimation and control schemes are designed and tested. These schemes are adaptive control laws including nonlinear estimation algorithms: a sliding mode observer to estimate the unknown influent concentration, but also state observers and parameter estimators used to estimate the unknown states and kinetics. Since the temperature is an important factor for an efficient operation of the process, an algorithm for temperature control in the bioreactor is also developed. To verify the control algorithms effectiveness, several tests performed via numerical simulations under realistic conditions are presented.

Control schemes for a complex biorefinery plant for bioenergy and biobased products.

This work proposes innovative feedback control schemes for a complex biorefinery plant which contains two continuous bioreactors: an anaerobic digester and a photobioreactor. The anaerobic digester is used to decompose organic matter inside a wastewater treatment process, the most useful final product being biogas/methane. The photobioreactor is used for a microalgae photosynthetic growth process where some components with added value are produced, and bio-mitigation of the carbon dioxide emissions is achieved. By using realistic models of the anaerobic digester and of the photobioreactor, novel adaptive and robust control schemes are designed. These proposed structures contain linearizing controllers, state observers and parameter estimators for the bioprocess unknown kinetics. The control designs are validated via numerical simulations that consider several realistic restrictions and disturbances which act on the process: unavailability of some biological variables, unknown and time-varying reaction kinetics, uncertain and time-varying influent flow rates, noisy measurements.

Nonlinear estimation and control schemes for a complex anaerobic digestion of microalgae with unknown kinetics and inputs.

This work addresses estimation and advanced control schemes for a complex microalgae anaerobic digestion process, used for wastewater treatment as well as for converting organic waste and biomass into biogas. The control goal is to keep the inhibitory compounds at imposed low levels despite the influent pollutant concentration and load variations. The proposed innovative control schemes work in accurate operating conditions and can cope with harsh assumptions: unknown kinetic rates, unknown feed-in pollutant concentration, unmeasurable state variables. The adaptive and robust-adaptive control laws are based on nonlinear algorithms: a sliding mode observer for the unknown inputs, kinetics parameter estimators, asymptotic and interval state observers. The tests performed under realistic conditions showed that the closed-loop bioprocess is preserved at specific operating points such that the control goal is fulfilled. The state and parameter estimators provide enough on-line information such that the control schemes can handle all the input and kinetic uncertainties.

Mammalian cell culture process for monoclonal antibody production: nonlinear modelling and parameter estimation.

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

Enzymatic synthesis of ampicillin: nonlinear modeling, kinetics estimation, and adaptive control.

Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies.