Rayen Filali

Nonlinear predictive control for maximization of CO2 bio-fixation by microalgae in a photobioreactor

In the framework of environment preservation, microalgae biotechnology appears as a promising alternative for CO2 mitigation. Advanced control strategies can be further developed to maximize biomass productivity, by maintaining these microorganisms in bioreactors at optimal operating conditions. This article proposes the implementation of Nonlinear Predictive Control combined with an on-line estimation of the biomass concentration, using dissolved carbon dioxide concentration measurements. First, optimal culture conditions are determined so that biomass productivity is maximized. To cope with the lack of on-line biomass concentration measurements, an interval observer for biomass concentration estimation is built and described. This estimator provides a stable accurate interval for the state trajectory and is further included in a nonlinear model predictive control framework that regulates the biomass concentration at its optimal value. The proposed methodology is applied to cultures of the microalgae Chlorella vulgaris in a laboratory-scale continuous photobioreactor. Performance and robustness of the proposed control strategy are assessed through experimental results.

Growth modeling of the green microalga Chlorella vulgaris in an air-lift photobioreactor

Abstract Considering the increasing impact of the environmental concerns in the current worldwide policy, the application of biological processes, namely the bio-fixation of CO 2 by microalgae, represents a promising solution and an increasingly attractive strategy. Indeed, these photosynthetic microorganisms have the great capacity to fix and tolerate high CO 2 concentrations converting it to biomass and highly valuable molecules. Thus, modeling of microalgae growth represents an essential tool for the optimization of the carbon dioxide consumption in engineered systems such as photobioreactors. In this context, the main goal of this work is the identification of the growth model parameters of Chlorella vulgaris , the model organism used in this study. The growth model developed in this study takes into account the combined influence of light intensity and the total inorganic carbon available per cell. First, an experimental campaign of batch culture was carried out in a well-stirred lab-scale photobioreactor under optimal conditions. Finally, model results of biomass dynamics and total inorganic carbon evolution over time are compared with data of batch and continuous cultures, confirming the accuracy of the identified model parameters.

Optimization of the interval approach for Chlorella vulgaris biomass estimation

As a result of different environmental issues, especially global warming and the greenhouse effect, biotechnology using microalgae has become a very promising alternative for carbon dioxide mitigation. Indeed, these unicellular microorganisms reduce efficiently carbon dioxide emissions through their photosynthetic activity. In order to maximize the efficacy of this biological process, one of the challenges is the efficient on-line estimation of the microalgae biomass for control strategies. In this context, several studies have established the performance and robustness of the interval observer for biomass estimation. This paper proposes a method of optimization of the gains tuning of the interval observer for the biomass concentration of Chlorella vulgaris culture in a continuous photobioreactor, using Total Inorganic Carbon measurements. This study provides two procedures for choosing the gains of the estimation strategy under a specific operating condition. The optimization methodology is validated by numerical simulations in the presence of uncertain model parameters and noisy measurements.

Identification of the growth model parameters for a culture of Chlorella vulgaris in a photobioreactor

The microalgae biotechnology is a very promising solution for environmental applications. In particular, these photosynthetic microorganisms have a great capacity to fix the carbon dioxide converting it into biomass and other secondary metabolites. Therefore the biological CO2 fixation by microalgae has attracted much attention. The optimization of this biological process by maintaining the algal culture under optimal growth conditions represents a major challenge. Thus microalgae growth models are needed to optimize the carbon dioxide consumption by microalgae in engineered systems such as photobioteactors. In this paper, a procedure to identify parameters of a microalgae growth model is described. First of all, experiments carried out in a lab-scale photobioreactor are presented. Thereafter, a description of the selected growth model, applied to cultures of Chlorella vulgaris, which allows the effective representation of the evolution of the specific growth rate and biomass of the Chlorella vulgaris culture in a perfectly stirred photobioreactor is described. At last, results of our model are compared to experimental data, which confirms the accuracy of the whole procedure.

Nonlinear Predictive Control for continuous Chlorella vulgaris culture in a photobioreactor

In the framework of environment preservation, microalgae biotechnology appears to be a promising alternative for CO2 mitigation. Implementation of advanced control strategies can be further considered to improve potential performances of these organisms. In this context, this paper proposes the implementation of predictive control combined with an on-line estimation of the Chlorella vulgaris biomass, using Total Inorganic Carbon measurements. The elaboration of interval observers for biomass estimation is first detailed. This estimation is further included in a Nonlinear Model Predictive Control (NMPC) framework considered to regulate the biomass concentration. Finally experimental results are presented which validate the proposed theoretical developments.

Interval observer for Chlorella vulgaris culture in a photobioreactor

Microalgae present a very promising alternative for carbon dioxide mitigation strategies. These unicellular photosynthetic microorganisms fix carbon dioxide efficiently, converting it into biomass and high-value compounds. One of the control system challenges is to maximize carbon dioxide consumption by maintaining the cellular concentration to an optimal value in the exponential phase. In this paper, we propose a software sensor for the biomass concentration of Chlorella vulgaris culture in a continuous photobioreactor with uncertain model parameters. From Total Inorganic Carbon measurements, the interval observer can provide a guaranteed upper and lower bound for the cellular concentration using the available interval of the uncertain initial condition, model parameters and measurements. Numerical simulations and experimental validation are included to illustrate the performance of the proposed estimation strategy.

Estimation of Chlorella vulgaris growth rate in a continuous photobioreactor

Abstract Microalgae consume carbon dioxide for their growth and to produce secondary metabolites. One of the control system challenges is to maximize this carbon dioxide consumption by controlling microalgae growth, and by maintaining its growth rate at an optimal value. However, there is no such thing as a microalgae growth rate sensor. This paper proposes a software sensor for the specific growth rate and biomass concentration of a microalgae culture in a continuous photobioreactor using the measurements of Total Inorganic Carbon in the medium. Numerical simulations are included to illustrate the performance of the proposed estimation strategy.