Non-destructive monitoring of microalgae biofilms.

Biofilm-based cultivation systems are emerging as a promising technology for microalgae production. However, efficient and non-invasive monitoring routines are still lacking. Here, a protocol to monitor microalgae biofilms based on reflectance indices (RIs) is proposed. This framework was developed using a rotating biofilm system for astaxanthin production by cultivating Haematococcus pluvialis on cotton carriers. Biofilm traits such as biomass, astaxanthin, and chlorophyll were characterized under different light and nutrient regimes. Reflectance spectra were collected to identify the spectral bands and the RIs that correlated the most with those biofilm traits. Robust linear models built on more than 170 spectra were selected and validated on an independent dataset. Astaxanthin content could be precisely predicted over a dynamic range from 0 to 4% of dry weight, regardless of the cultivation conditions. This study demonstrates the strength of reflectance spectroscopy as a non-invasive tool to improve the operational efficiency of microalgae biofilm-based technology.

Exploring the dynamics of astaxanthin production in Haematococcus pluvialis biofilms using a rotating biofilm-based system.

Microalgae biofilm emerged as a solid alternative to conventional suspended cultures which present high operative costs and complex harvesting processes. Among several designs, rotating biofilm-based systems stand out for their scalability, although their primary applications have been in wastewater treatment and aquaculture. In this work, a rotating system was utilized to produce a high-value compound (astaxanthin) using Haematococcus pluvialis biofilms. The effect of nitrogen regime, light intensity, and light history on biofilm traits was assessed to better understand how to efficiently operate the system. Our results show that H. pluvialis biofilms follow the classical growth stages described for bacterial biofilms (from adhesion to maturation) and that a two-stage (green and red stages) allowed to reach astaxanthin productivities of 204 mg m-2 d-1 . The higher light intensity applied during the red stage (400 and 800 µmol m-2 s-1 ) combined with nitrogen depletion stimulated similar astaxanthin productivities. However, by training the biofilms during the green stage, using mild-light intensity (200 µmol m-2 s-1 ), a process known as priming, the final astaxanthin productivity was enhanced by 40% with respect to biofilms pre-exposed to 50 µmol m-2 s-1 . Overall, this study shows the possibility of utilizing rotating microalgae biofilms to produce high-value compounds laying the foundation for further biotechnological applications of these emerging systems.

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.

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.