Microalgae Growth In Photobioreactor Research Articles

A reduced-order hybrid model for photobioreactor performance and biomass prediction

This paper introduces a hybrid approach for photobioreactor modeling tailored to microalgae cultivation, combining data-driven and mechanistic concepts to improve modeling efficiency and practicality for industrial scale-up applications. Most growth models for microalgae are nonlinear and require experimental measurement of several parameters. The aim of this work is to develop linear practical models for monitoring purposes. A model based on linear coefficients and polynomial features is proposed, balancing interpretability with non-linear representation focusing on model transparency. To simplify the growth model, Taylor series expansion is applied to the Monod and logistic population models. Two scale-specific models are developed and evaluated, offering practical solutions for monitoring microalgae growth in photobioreactors. Therefore, this reduced order representation allows the biomass growth rate to be dependent directly on the biomass concentration. These models do not require exhaustive data collection of substrate concentration over time, making them cost-effective and efficient for industrial applications. This work provides a step forward in photobioreactor modeling, contributing to the sustainable production of microalgae.

Dynamic Optimization Approach to Estimate Kinetic Parameters of Monod-Based Microalgae Growth Models.

The biomass concentration of microalgae growth in photobioreactor was predicted using the Monod-based growth models. Kinetic parameters such as maximum specific growth rate and saturation constant of light intensity were evaluated by nonlinear least squares methods that focused on minimizing the sum of squares error (SSE). The importance of good initial guess for the nonlinear least squares method was also discussed. The optimal control problem of the microalgae growth model was determined based on parameter sensitivity. Therefore, a dynamic optimization approach was used where an optimal input design method was formulated to obtain a control function of a problem. The dynamic state equations, additional state equations, cost function, and Hamiltonian function were used to establish a control function of microalgae growth in a photobioreactor. Hence, the biomass production of microalgae can be predicted using numerical methods such as the Taylor series method.

Estimation of the microalgae growth using the optimization approach

This study estimates the parameters value of the microalgae growth kinetic model which are Monod, Tessier and modified Moser model using the Levenberg-Marquardt method, by minimizing the sum of squares error. The optimal control problem for microalgae growth in photobioreactor is formulated and solved by the optimal input design method and taking into account the parametric sensitivities in order to obtain the control function. Hence, the fourth-order Runge-Kutta method is implemented to predict the microalgae biomass concentration by solving the state equations of the optimal control problem. The result shows the Tessier model best describes the growth of microalgae with the produced biomass concentration of weight 1.813 × 1010 g.

Light attenuation in photobioreactors and algal pigmentation under different growth conditions – Model identification and complexity assessment

Microalgae are photosynthetic organisms, and thus one of the most important factors affecting their growth is light. Yet, effective design and operation of algal cultivation systems still lacks robust numerical tools. Here, a comprehensive and mathematically consistent simulation model is presented in the ASM-A framework that can accurately predict light availability and its impact on microalgae growth in photobioreactors (PBR). Three cylindrical column reactors, mimicking typical open pond reactors, with different diameters were used to conduct experiments where the light distribution was monitored inside the reactor. A batch experiment was conducted where the effect of nutrients and light availability on the pigmentation of the microalgae and light distribution was monitored. The effect of reactor size and cultivation conditions on the light distribution in PBRs was evaluated. Moreover, we assessed the effect of using different simulation model structures on the model prediction accuracy and uncertainty propagation. Results obtained show that light scattering can have a significant effect on light distribution in reactors with narrow diameter (typical to panel-type PBRs) and under cultivation conditions that promote low pigmentation or low biomass concentration. The light attenuation coefficient was estimated using the Lambert-Beer equation and it was compared to Schuster's law. The light attenuation was found to be dependent on biomass concentration and microalgae pigmentation. Using a discretized layer model to describe the light distribution in PBRs resulted in the most accurate prediction of microalgal growth and lowest uncertainty on model predictions. Due to model complexity a trade-off needs to be made between accuracy of the prediction ac simulation time.

Numerical simulation on promoting light/dark cycle frequency to improve microalgae growth in photobioreactor with serial lantern-shaped draft tube

Computational fluid dynamics were employed to simulate microalgal cells movement with enhanced flash-light effects in a gaslift loop-current column photobioreactor (GLCP) with serial lantern-shaped draft tube (LDT). Clockwise and anticlockwise vortexes were formed in outer down-flow region of GLCP with LDT. The radial velocity, axial velocity, and turbulent kinetic energy of microalgal solution appeared periodical change around the lanterns. The average radial velocity showed a sixfold improvement from 0.003 m/s to 0.021 m/s, and average turbulent kinetic energy was enhanced by 18.2% from 22.5 × 10–4 m2/s2 to 26.6 × 10–4 m2/s2, thus increasing light/dark cycle frequency by 54%. The light/dark cycle frequency increased first and then decreased with an increase of individual lantern height. The increased lantern number promoted the light/dark cycle frequency and light time ratio. Microalgal biomass yield in the GLCP with LDT was improved by 30%, and CO2 fixation peak rate was promoted by 35%.

Maximizing microalgae productivity in a light-limited chemostat

Light supply is one of the most important parameters to be considered for enhancing microalgae growth in photobioreactors (PBR) with artificial light. However, most of the mathematical works do not consider incident light as a parameter to be optimized. In this work based on a simple model of light-limited growth, we determine optimal values for the dilution rate and the incident light intensity in order to maximize the steady-state microalgal surface productivity in a continuous culture. We also show that in optimal conditions there is a minimal initial microalgal concentration (and we give a simple expression to determine it) to guarantee the persistence of the population. Finally, in the context of enhancing microalgae productivity by reducing light absorption by microalgae, we conclude our work by studying the influence of the chlorophyll-carbon quota on the maximal productivity.

Dynamic pH model for autotrophic growth of microalgae in photobioreactor: A tool for monitoring and control purposes

A dynamic model for the photoautotrophic growth of microalgae in photobioreactor that describes the main variables of the system and allows the precise prediction of the pH in the culture was proposed and validated. The dynamic behavior of the biological system was expressed through a multistate model in continuous‐time formulation, based on mass‐balance equations and local photosynthetic responses of the anisotropic medium, further associated with a set of algebraic equations that describes the thermodynamic properties of the ammonia—carbon dioxide—water ternary solute system. The global photoautotrophic growth model was validated on experimental data acquired from a torus reactor inoculated with Chlamydomonas reinhardtii cells. The model response was studied in simulation for all identified input variables (dilution rate, incident light intensity, temperature, and flow rates of input gases). © 2013 American Institute of Chemical Engineers AIChE J 60: 585–599, 2014

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions(1). Microalgae not only capture solar energy more efficiently than plants(3), but also synthesize advanced biofuels(2-4). Generally, atmospheric CO2 is not a sufficient source for supporting maximal algal growth(5). On the other hand, the high concentrations of CO2 in industrial exhaust gases have adverse effects on algal physiology. Consequently, both cultivation conditions (such as nutrients and light) and the control of the flue gas flow into the photo-bioreactors are important to develop an efficient "flue gas to algae" system. Researchers have proposed different photobioreactor configurations(4,6) and cultivation strategies(7,8) with flue gas. Here, we present a protocol that demonstrates how to use models to predict the microalgal growth in response to flue gas settings. We perform both experimental illustration and model simulations to determine the favorable conditions for algal growth with flue gas. We develop a Monod-based model coupled with mass transfer and light intensity equations to simulate the microalgal growth in a homogenous photo-bioreactor. The model simulation compares algal growth and flue gas consumptions under different flue-gas settings. The model illustrates: 1) how algal growth is influenced by different volumetric mass transfer coefficients of CO2; 2) how we can find optimal CO2 concentration for algal growth via the dynamic optimization approach (DOA); 3) how we can design a rectangular on-off flue gas pulse to promote algal biomass growth and to reduce the usage of flue gas. On the experimental side, we present a protocol for growing Chlorella under the flue gas (generated by natural gas combustion). The experimental results qualitatively validate the model predictions that the high frequency flue gas pulses can significantly improve algal cultivation.

Multivariable feedback linearizing control of Chlamydomonas reinhardtii photoautotrophic growth process in a torus photobioreactor

The objective of this paper is to design and to validate a nonlinear multivariable controller, based on the exact feedback linearization technique, which has the capacity to stabilize the photoautotrophic microalgae growth in photobioreactor regardless of the operation point or the transient trajectory. Two measurable outputs were selected, namely the biomass concentration and the pH, whose simultaneous control can be realized by manipulating the dilution rate and the injected carbon dioxide gas flowrate. A mechanistic dynamical model describing the interactions between physicochemical and biological phenomena inside the photobioreactor was used as a starting point for the controller design. Given its complexity, the model was reduced by differentiating the states with slow dynamics from the ones with fast dynamics that were converted into algebraic expressions. In addition, a dynamic time-varying expression was derived for the concentration of hydrogen ions (negative antilogarithm of pH) thus obtaining an appropriate I/O model for control purposes. The degree of interaction between I/O channels was determined through a relative gain array analysis, in order to establish if the system allows the implementation of decentralized SISO controllers or requires a centralized MIMO controller. The feedback linearization method was associated with a model based technique to furnish the immeasurable variables required by the nonlinear control algorithm. The nonlinear controller was implemented on a laboratory torus photobioreactor piloted at constant incident light flux intensity throughout the photoautotrophic growth of a Chlamydomonas reinhardtii culture, its efficiency being proved under several operating points.

Experimental analysis and model-based optimization of microalgae growth in photo-bioreactors using flue gas

This study tested the growth of three algal species ( Chlorella sp., Synechocystis sp. PCC 6803, and Tetraselmis suecica ) using flue gas (generated by natural gas combustion). All the cultures showed poor biomass growth if they were exposed to continuous flue gas. To optimize the flue gas utilization in algal photo-bioreactors, we performed both model simulations and experimental analysis. First, we employed an un-segregated Monod-based model to describe the microalgal growth in response to CO 2 in the photo-bioreactor. Via the dynamic optimization approach (DOA), the model profiled time-dependent CO 2 concentrations (volume fraction ranging from 0.1 to 0.6%) to support maximal biomass growth. Second, we designed an on–off flue gas pulse mode to reduce CO 2 inhibition (a volume fraction up to 15% CO 2 ) to the algal cells. Based on the reported algal kinetic parameters, our model predicted that gas-on (∼10 s CO 2 pulse) and gas-off (5–9 min) could achieve over 90% of the maximum theoretical algal growth rate predicted by the DOA. Third, we used mass flow controllers to apply on–off flue gas pulses in photo-bioreactors, and the experimental results verified that the flue gas pulses could reduce flue gas inhibition and improve Chlorella growth compared to cultures exposed to atmospheric CO 2 . ► We studied a rectangular pulse mode for feeding flue gas into photo-bioreactors. ► A Monod-model examined pulse functions for maximum microalgal growth. ► Continuous on–off flue gas pulses with short pulse width and high on–off frequency improve Chlorella growth.

Modelling microalgae growth in nitrogen limited photobiorector for estimating biomass, carbohydrate and neutral lipid productivities

AbstractMicroalgae culture for energy production has emerged as an interesting alternative to fossil fuel and biofuel from terrestrial plants. In this paper, we propose a dynamical model of microalgae growth in photobioreactor in order to further optimize productivity. We consider light and nitrogen effects on microalgae growth and on the intracellular carbon flows between a functional compartment (proteins, nucleic acids, membranes) and two storage pools (carbohydrates and neutral lipids). In a second step, we take into account the photoacclimation dynamics. We also compute the light distribution inside the photobioreactor using a Beer-Lambert law. The proposed model has been assessed with experimental data of Isochrysis affinis galbana under day/night cycles. Finally, the model is used to predict carbohydrate, neutral lipid, and biomass productivities and to identify optimal operating conditions (dilution rate and influent nitrogen concentration).