Full-scale validation of an algal productivity model including nitrogen limitation

Béchet, Quentin; Coulombier, Noémie; Vasseur, Christophe; Lasserre, Thomas; Déan, Loïc Le; Bernard, Olivier

doi:10.1016/j.algal.2018.02.010

Cited by 16 publications

(8 citation statements)

References 30 publications

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“…It was also determined that the calibration provides acceptable results for around one month of operation (out of this period higher RSS errors -over 50 units-are achieved). Hence, it was concluded that the model should be calibrated once or twice each season of the year or it may be modified to include the variation of temperature [27] and its effect on the dynamics of the process.…”

Section: Calibration Of the Modelmentioning

confidence: 99%

Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production

et al. 2019

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Production of microalgae is one of the emerging biotechnological processes due to its potential applications to produce high value-added compounds. In photobioreactors for microalgae production, the biomass concentration is a desirable variable to be measured on-line to optimize the yield of the systems. However, biomass concentration can hardly be monitored in real time. There are few expensive commercial sensors that in fact provide uncertain measurements. State estimators, also known as software sensors, are algorithms designed to estimate unmeasured (or non-easily measurable) variables of a process. In this work, a state estimator using the extended Kalman filter algorithm is developed to estimate biomass concentration for an outdoor industrial raceway photobioreactor. The state estimator is based on a dynamic model for microalgae production specifically designed for this type of photobioreactor. Results demonstrate that, despite the complex non-linear dynamics that characterise this kind of bioprocess, a state estimator can provide a relatively accurate estimation of the biomass concentration. Furthermore, a state estimator could be used to optimize the operation of industrial photobioreactors by utilizing the estimated biomass concentration for automatic control of the process.

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Section: Calibration Of the Modelmentioning

confidence: 99%

Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production

et al. 2019

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“…The universal model for temperature prediction in shallow algal ponds developed by [29] has been used in this work. This model was validated against data collected from a high rate algal pond [13,25]. The temperature model, valid for any opaque water body having a uniform temperature profile, is based on eight heat fluxes that can be expressed from available meteorological data/system design parameters.…”

Section: Temperature Modelmentioning

confidence: 99%

“…Several models have been published in the last decade, which can accurately predict algal yields at full-scale depending on the species, weather conditions, system design, and operation [9][10][11]. Some studies have even proposed full scale validation when weather fluctuations were recorded [12,13].…”

Section: Introductionmentioning

confidence: 99%

Meteorological Data-Based Optimal Control Strategy for Microalgae Cultivation in Open Pond Systems

et al. 2019

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Outdoor biofuel production from microalgae is a complex dynamical process submitted to climatic variations. Controlling and optimizing such a nonlinear process strongly influenced by weather conditions is therefore tricky, but it is crucial to make this process economically sustainable. The strategy investigated in this study uses weather forecast coupled to a detailed predictive model of algal productivity for online optimization of the rates of fresh medium injection and culture removal into and from the pond. This optimization strategy was applied at various climatic conditions and significantly increased productivity compared to a standard operation with constant pond depth and dilution rate, by up to a factor of 2.2 in a Mediterranean climate in summer. A thorough analysis of the optimizer strategy revealed that the increase of productivity in summer was achieved by finding a trade-off between algal concentration to optimally distribute light and pond temperature to get closer to optimal growth temperature. This study also revealed that maintaining the temperature as high as possible is the best strategy to maximize productivity in cold climatic conditions.

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“…Since D. salina is the richest known source of natural β-carotene, a metabolic network model is highly beneficial to fully exploit the biotechnological potential of this alga. So far, for D. salina some metabolic profiling information is available [31,32], and the first growth models have recently been created [11,33,34]. In addition, the genome of D. salina has been released (http://genome.jgi.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction and analysis of a carbon-core metabolic network for Dunaliella salina

Fachet

Witte

Flassig

et al. 2019

Preprint

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Background The green microalga Dunaliella salina accumulates a high proportion of beta-carotene during abiotic stress conditions. To better understand the intracellular flux distribution leading to carotenoid accumulation, this work aimed at reconstructing a carbon core metabolic network for D. salina CCAP 19/18 based on the recently published. Results The reconstruction resulted in a network model with 221 reactions and 212 metabolites within three compartments: cytosol, chloroplast and mitochondrion. The network was implemented in the MATLAB toolbox CellNetAnalyzer and checked for feasibility. Furthermore, a flux balance analysis was carried out for different light and nutrient uptake rates. The comparison of the experimental knowledge with the model prediction revealed that the results of the stoichiometric network analysis are plausible and in good agreement with the observed behavior. Accordingly, our model provides an excellent new tool to interrogate and better understand the carbon core metabolism of D. salina. Conclusions The reconstructed metabolic network of D. salina presented in this work is able to predict the biological behavior under light and nutrient stress and will lead to an improved process understanding for the optimized production of high-value products in microalgae.

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Full-scale validation of an algal productivity model including nitrogen limitation

Cited by 16 publications

References 30 publications

Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production

Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production

Meteorological Data-Based Optimal Control Strategy for Microalgae Cultivation in Open Pond Systems

Reconstruction and analysis of a carbon-core metabolic network for Dunaliella salina

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