Filipa Lopes

Identification of microbial communities involved in the methane cycle of a freshwater meromictic lake

Lake Pavin is a meromictic crater lake located in the French Massif Central area. In this ecosystem, most methane (CH(4)) produced in high quantity in the anoxic bottom layers, and especially in sediments, is consumed in the water column, with only a small fraction of annual production reaching the atmosphere. This study assessed the diversity of methanogenic and methanotrophic populations along the water column and in sediments using PCR and reverse transcription-PCR-based approaches targeting functional genes, i.e. pmoA (α-subunit of the particulate methane monooxygenase) for methanotrophy and mcrA (α-subunit of the methyl-coenzyme M reductase) for methanogenesis as well as the phylogenetic 16S rRNA genes. Although methanogenesis rates were much higher in sediments, our results confirm that CH(4) production also occurs in the water column where methanogens were almost exclusively composed of hydrogenotrophic methanogens, whereas both hydrogenotrophs and acetotrophs were almost equivalent in the sediments. Sequence analysis of markers, pmoA and the 16S rRNA gene, suggested that Methylobacter may be an important group actively involved in CH(4) oxidation in the water column. Two main phylotypes were characterized, one of which could consume CH(4) under conditions where the oxygen amount is undetectable.

Nitrogen and phosphate removal from wastewater with a mixed microalgae and bacteria culture

Graphical abstract

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.

Carbon dioxide biofixation by Chlorella vulgaris at different CO2 concentrations and light intensities

In order to develop an effective CO2 mitigation process using microalgae for potential industrial application, the growth and physiological activity of Chlorella vulgaris in photobioreactor cultures were studied. C. vulgaris was grown at two CO2 concentrations (2 and 13% of CO2 v/v) and at three incident light intensities (50, 120 and 180 μmol m−2 s−1) for 9 days. The measured specific growth rate was similar under all conditions tested but an increase in light intensity and CO2 concentration affected the biomass and cell concentrations. Although carbon limitation was observed at 2% CO2, similar cellular composition was measured in both conditions. Light limitation induced a net change in the growth behavior of C. vulgaris. Nitrogen limitation seemed to decrease the nitrogen quota of the cells and rise the intracellular carbon:nitrogen ratio. Exopolysaccharide production per cell appeared to be affected by light intensity. In order to avoid underestimation of the CO2 biofixation rate of the microalgae, exopolysaccharide production was taken into account. The maximum CO2 removal rate (0.98 g CO2 L−1 d−1) and the highest biomass concentration (4.14 g DW L−1) were determined at 13% (v/v) CO2 and 180 μmol m−2 s−1. Our results show that C. vulgaris has a real potential for industrial CO2 remediation.

Modeling the continuous lactic acid production process from wheat flour.

A kinetic model of the simultaneous saccharification, protein hydrolysis, and fermentation (SSPHF) process for lactic acid production from wheat flour has been developed. The model describes the bacterial growth, substrate consumption, lactic acid production, and maltose hydrolysis. The model was fitted and validated with data from SSPHF experiments obtained under different dilution rates. The results of the model are in good agreement with the experimental data. Steady state concentrations of biomass, lactic acid, glucose, and maltose as function of the dilution rate were predicted by the model. This steady state analysis is further useful to determine the operating conditions that maximize lactic acid productivity.

Atmospheric pressure argon surface discharges propagated in long tubes: physical characterization and application to bio-decontamination

Pulsed corona discharges propagated in argon (or in argon with added water vapor) at atmospheric pressure on the interior surface of a 49 cm long quartz tube were investigated for the application of surface bio-decontamination. H2O molecule dissociation in the argon plasma generated reactive species (i.e. OH in ground and excited states) and UV emission, which both directly affected bacterial cells. In order to facilitate the evaluation of the contribution of UV radiation, a DNA damage repair defective bacterial strain, Escherichia coli DH-1, was used. Discharge characteristics, including propagation velocity and plasma temperature, were measured. Up to ~5.5 and ~5 log10 reductions were observed for E. coli DH-1 bacteria (from 106 initial load) exposed 2 cm and 44 cm away from the charged electrode, respectively, for a 20 min plasma treatment. The factors contributing to the observed bactericidal effect include desiccation, reactive oxygen species (OH) plus H2O2 accumulation in the liquid phase, and UV-B (and possibly VUV) emission in dry argon. The steady state temperature measured on the quartz tube wall did not exceeded 29 °C; the contribution of heating, along with that of H2O2 accumulation, was estimated to be low. The effect of UV-B emission alone or in combination with the other stress factors of the plasma process was examined for different operating conditions.

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.

Control strategy for continuous lactic acid production from wheat flour

In this study, a control strategy for lactic acid production process from wheat flour is proposed. First, the process model to be used in the control strategy is chosen after performing an observability analysis of the system. Then, the optimal setpoint that maximizes the lactic acid productivity is determined based on this simplified model. Finally, the regulation of lactic acid concentration to its optimal value is considered. The designed control law uses a state-feedback linearizing controller in an inner loop together with a proportional integral (PI) regulator in an outer loop. An anti-windup mechanism is added in order to improve the control law performances. The efficiency of the proposed control strategy is illustrated and assessed by means of simulation results.

Modeling and parameter identification of the batch lactic acid production process from wheat flour

The application of white biotechnological processes to produce biopolymers has been increasing recently. In this study, a mathematical model describing the lactic acid production from wheat flour using lactic acid bacteria is presented. The model considers the kinetics degradation of maltose into glucose and the inhibitor effect of substrate and product at the simultaneous saccharification and fermentation step. The identification procedure developed to determine model parameters is described. Experimental data from batch fermentation experiments in a pilot fermenter under different conditions are used to fit and validate the model.