Modeling the effects of light and temperature on algae growth: State of the art and critical assessment for productivity prediction during outdoor cultivation

Interchangeable modular design and operation of photo-bioreactors for Chlorella vulgaris cultivation towards a zero-waste biorefinery

This study explores diverse cultivation modes for Chlorella vulgaris within a biorefinery at pilot scale that produces both biodiesel by transesterification of waste frying oils and syngas by gasification of organic wood waste. Given microalgae's comparatively modest biofuel yield relative to principal biorefinery products, the microalgae cultivation process is designed on the biofuels production rates. Liquid and gaseous waste streams are recycled inside the biorefinery: crude glycerol is mixed with wood to enhance the quality of syngas, wastewater is fed to microalgae so as flue gas. Also, the oil extracted from microalgae contributes to produce biodiesel and the waste cells are gasified. Considering that the optimal fit for each cultivation mode varies with the shape of the reactor, we propose a modular approach to assemble them in batteries of tubular, bubble flow, and airlift reactors, and present an operating design criterion that can fulfill the mass balance of the plant by adding/transforming the number of units inside the different batteries. Methods to adjust the operating conditions and control the operating parameters are also discussed. The designed configurations were operated recycling nominal waste streams of about 30 L d-1 of wastewater and 90 Nm3 h-1 of flue gas. Results confirm that the most advantageous one, in terms of volume per recycled waste streams, is a battery of 16 airlift reactors, operating in mixotrophic mode, with growing rate of 0.427 d-1, yield of 3.06, glycerol conversion 39 %, CO2 removal 64 % of inlet 6-10 %(mol) concentration. The same nominal waste streams can also be managed by 40 tubular reactors in almost heterotrophic conditions coupled with 12 bubble columns in autotrophic conditions; working respectively at growing rates of 0.395 d-1 and 0.362 d-1 and yields of 2.94 and 2.84. The battery of tubular reactors reached a glycerol conversion of 45 % and the array of bubble columns removed about 51 % of inlet 12-20 %(mol) CO2 concentration. A complete comparison is reported also in terms of dimensionless numbers and pumping/mixing requirements.

Selection of photosynthetic traits by turbulent mixing governs formation of cyanobacterial blooms in shallow eutrophic lakes

Prediction of the complex cyanobacteria-environment interactions is vital for understanding harmful bloom formation. Most previous studies on these interactions considered specific properties of cyanobacterial cells as representative for the entire population (e.g. growth rate, mortality, and photosynthetic capacity (Pmax)), and assumed that they remained spatiotemporally unchanged. Although, at the population level, the alteration of such traits can be driven by intraspecific competition, little is known about how traits and their plasticity change in response to environmental conditions and affect the bloom formation. Here we test the hypothesis that intraspecific variations in Pmax of cyanobacteria (Microcystis spp.) play an important role in its population dynamics. We coupled a one-dimensional hydrodynamic model with a trait-based phytoplankton model to simulate the effects of physical drivers (turbulence and turbidity) on the Pmax of Microcystis populations for a range of dynamic conditions typical for shallow eutrophic lakes. Our results revealed that turbulence acts as a directional selective driver for changes in Pmax. Depending on the intensity of daily-periodic turbulence, representing wind-driven mixing, a shift in population-averaged phenotypes occurred toward either low Pmax, allowing the population to capture additional light in the upper layers, or high Pmax, enhancing the efficiency of light utilization. Moreover, we observed that a high intraspecific diversity in Pmax accelerated the formation of surface scum by up to more than four times compared to a lower diversity. This study offers insights into mechanisms by which cyanobacteria populations respond to turbulence and underscores the significance of intraspecific variations in cyanobacterial bloom formation.

HIGHLIGHTS

Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study

Accurate prediction of microalgae growth is crucial for understanding the impacts of light dynamics and optimizing production. Although various mathematical models have been proposed, only a few of them have been validated in outdoor cultivation. This study aims to investigate the use of machine learning algorithms in microalgae growth modeling. Outdoor cultivation data of Phaeodactylum tricornutum in flat-panel airlift photobioreactors for 50 days were used to compare the performance of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) with traditional models, namely Monod and Haldane. The results indicate that the machine learning models outperform the traditional models due to their ability to utilize light history as input. Moreover, the LSTM model shows an excellent ability to describe the light acclimation effect. Last, two potential applications of these models are demonstrated: 1) use as a biomass soft sensor and 2) development of an optimal harvest strategy for outdoor cultivation.

A precise microalgae farming for CO2 sequestration: A critical review and perspectives

Microalgae are great candidates for CO2 sequestration and sustainable production of food, feed, fuels and biochemicals. Light intensity, temperature, carbon supply, and cell physiological state are key factors of photosynthesis, and efficient phototrophic production of microalgal biomass occurs only when all these factors are in their optimal range simultaneously. However, this synergistic state is often not achievable due to the ever-changing environmental factors such as sunlight and temperature, which results in serious waste of sunlight energy and other resources, ultimately leading to high production costs. Most control strategies developed thus far in the bioengineering field actually aim to improve heterotrophic processes, but phototrophic processes face a completely different problem. Hence, an alternative control strategy needs to be developed, and precise microalgal cultivation is a promising strategy in which the production resources are precisely supplied according to the dynamic changes in key factors such as sunlight and temperature. In this work, the development and recent progress of precise microalgal phototrophic cultivation are reviewed. The key environmental and cultivation factors and their dynamic effects on microalgal cultivation are analyzed, including microalgal growth, cultivation costs and energy inputs. Future research for the development of more precise microalgae farming is discussed. This study provides new insight into developing cost-effective and efficient microalgae farming for CO2 sequestration.

Biostimulation of green microalgae Chlorella sorokiniana using nanoparticles of MgO, Ca10(PO4)6(OH)2, and ZnO for increasing biodiesel production

Microalgae have the potential to become the primary source of biodiesel, catering to a wide range of essential applications such as transportation. This would allow for a significant reduction in dependence on conventional petroleum diesel. This study investigates the effect of biostimulation techniques utilizing nanoparticles of Magnesium oxide MgO, Calcium hydroxyapatite Ca10(PO4)6(OH)2, and Zinc oxide ZnO to enhance the biodiesel production of Chlorella sorokiniana. By enhancing cell activity, these nanoparticles have demonstrated the ability to improve oil production and subsequently increase biodiesel production. Experimentally, each nanomaterial was introduced at a concentration of 15 mg L-1. The results have shown that MgO nanoparticles yielded the highest biodiesel production, with a recorded yield of 61.5 mg L-1. Hydroxyapatite nanoparticles, on the other hand, facilitated lipid accumulation. ZnO nanoparticles showcased a multifaceted advantage by enhancing both growth and lipid content. Thus, it is suggested that these nanoparticles can be used effectively to increase the lipid content of microalgae. These findings highlight the potential of biostimulation strategies utilizing MgO, hydroxyapatite, and zinc oxide nanoparticles to bolster biodiesel production.

A critical review on remediation of microplastics using microalgae from aqueous system

Microplastics (MPs) are deemed to be a global concern due to their harmful negative effects on the aquatic environment and human beings. MPs have a significant impact on both fresh and marine water ecosystems. In many countries, there is concern about the deleterious consequences of MPs on human health due to the presence of MPs in aquatic life for higher intake of marine food (fish and shellfish). Exposure to MPs causes fish to suffer from growth retardation, neurotoxicity, and behavioural abnormalities and it affects human as well. It causes oxidative stress, neurotoxicity, cytotoxicity, and immune system disruption after being ingested to these contaminated fish in human body. Due to these reasons, it has become imperative to find ways to resolve this problem. This review paper represents a pioneering endeavor by consolidating comprehensive information on microplastic-polluted Indian riverine ecosystems and effective MPs removal methods into a single, cohesive document. It meticulously evaluates the principles, removal efficiency, benefits, and drawbacks of various techniques, aiming to identify the most optimal solution. Furthermore, this paper provides a comprehensive exploration of the interesting interactions between MPs and microalgae, delving into the intricate processes of hetero-aggregation. Additionally, it shines a spotlight on the latest advancements in understanding the efficacy of microalgae in removing MPs, showcasing recent breakthroughs in this field of research. Moreover, the work goes beyond conventional assessments by elucidating the characteristics of MPs and exploring diverse influencing parameters that impact MPs removal by microalgae and also addresses the potential future aspects. This thorough investigation uncovers important factors that could significantly contribute to the development of more efficient and sustainable remediation strategies.

Influence of light absorption rate on the astaxanthin production by the microalga Haematococcus pluvialis during nitrogen starvation

Large amounts of astaxanthin (about 4% DW) can be produced under nitrogen starvation of Haematococcus pluvialis in photobioreactors (PBRs) exposed to high light conditions to induce a light stress. However, in PBR, the large biomass concentration usually achieved leads to strong light attenuation conditions, which makes complex the analysis of this "light stress". This study aims to elucidate the role of light transfer in astaxanthin cell content and productivity from the microalga Haematococcus pluvialis during nitrogen starvation. Haematococcus pluvialis was cultivated in a flat-panel PBR in a batch mode with sudden nitrogen starvation conditions and an incident photon flux density (PFD) of 250 µmolhν m-2 s-1. Different initial biomass concentrations ( C x 0 ) were evaluated, 0.21, 0.52, 1.39 and 2.21 kg m-3. As a result, spectral mass absorption cross-sections of Haematococcus pluvialis were measured at different times during nitrogen starvation, and were used to relate the mean rate of photon absorption (MRPA) to the astaxanthin productivity. A minimum initial MRPA of 7000 ± 500 µmolhν kgx-1 s-1 was found necessary to trigger large accumulation of astaxanthin in Haematococcus pluvialis cells (up to 3.21% DW) during nitrogen starvation conditions. The results also demonstrated the link between the MRPA and the daily astaxanthin productivity of Haematococcus pluvialis cultures, introducing then the MRPA as a physical quantity of interest for a rational optimization of the light culture conditions in PBRs.

Experimental and theoretical investigations of rotating algae biofilm reactors (RABRs): Areal productivity, nutrient recovery, and energy efficiency

Microalgae biofilms have been demonstrated to recover nutrients from wastewater and serve as biomass feedstock for bioproducts. However, there is a need to develop a platform to quantitatively describe microalgae biofilm production, which can provide guidance and insights for improving biomass areal productivity and nutrient uptake efficiency. This paper proposes a unified experimental and theoretical framework to investigate algae biofilm growth on a rotating algae biofilm reactor (RABR). Experimental laboratory setups are used to conduct controlled experiments on testing environmental and operational factors for RABRs. We propose a differential-integral equation-based mathematical model for microalgae biofilm cultivation guided by laboratory experimental findings. The predictive mathematical model development is coordinated with laboratory experiments of biofilm areal productivity associated with ammonia and inorganic phosphorus uptake by RABRs. The unified experimental and theoretical tool is used to investigate the effects of RABR rotating velocity, duty cycle (DC), and light intensity on algae biofilm growth, areal productivity, nutrient uptake efficiency, and energy efficiency in wastewater treatment. Our framework indicates that maintaining a reasonable light intensity range improves biomass areal productivity and nutrient uptake efficiency. Our framework also indicates that faster RABR rotation benefits biomass areal productivity. However, maximizing the nutrient uptake efficiency requires a reasonably low RABR rotating speed. Energy efficiency is strongly correlated with RABR rotating speed and DC.

Theoretical growth rate of microalgae under high/low-flashing light

Dynamic light regimes strongly impact microalgal photosynthesis efficiency. Finding the optimal way to supply light is then a tricky problem, especially when the growth rate is inhibited by overexposition to light and, at the same time, there is a lack of light in the deepest part of the culture. In this paper, we use the Han model to study the theoretical microalgal growth rate by applying periodically two different light intensities. Two approaches are considered depending on the period of the light pattern. For a large light period, we demonstrate that the average photosynthetic rate can be improved under some conditions. Moreover, we can also enhance the growth rate at steady state as given by the PI-curve. Although, these conditions change through the depth of a bioreactor. This theoretical improvement in the range of 10-15% is due to a recovery of photoinhibited cells during the high irradiance phase. We give a minimal value of the duty cycle for which the optimal irradiance is perceived by the algae culture under flashing light regime.

Expanding mechanistic models to represent purple phototrophic bacteria enriched cultures growing outdoors

The economic feasibility of purple phototrophic bacteria (PPB) for resource recovery relies on using enriched-mixed cultures and sunlight. This work presents an extended Photo-Anaerobic Model (ePAnM), considering: (i) the diverse metabolic capabilities of PPB, (ii) microbial clades interacting with PPB, and (iii) varying environmental conditions. Key kinetic and stoichiometric parameters were either determined experimentally (with dedicated tests), calculated, or gathered from literature. The model was calibrated and validated using different datasets from an outdoors demonstration-scale reactor, as well as results from aerobic and anaerobic batch tests. The ePAnM was able to predict the concentrations of key compounds/components (e.g., COD, volatile fatty acids, and nutrients), as well as microbial communities (with anaerobic systems dominated by fermenters and PPB). The results underlined the importance of considering other microbial clades and varying environmental conditions. The model predicted a minimum hydraulic retention time of 0.5 d^-1. A maximum width of 10 cm in flat plate reactors should not be exceeded. Simulations showed the potential of a combined day-anaerobic/night-aerobic operational strategy to allow efficient continuous operation.

Robust control designs for microalgae cultivation in continuous photobioreactors

Microalgae are used to produce renewable biofuels and high-value components and in bioremediation and CO2 sequestration tasks. These increasing applications, in conjunction with a desirable constant large-scale productivity, motivate the development and application of practical controllers. This paper addresses the application of robust control schemes for microalgae cultivation in continuous photobioreactors. Due to the model uncertainties and external perturbations, robust control designs are required to guarantee the desired microalgae productivity. Furthermore, simple controller designs are desirable for practical implementation purposes. Therefore, two robust control designs are applied and evaluated in this paper for two relevant case studies of microalgae cultivation in photobioreactors. The first control design is based on an enhanced simple-input output model with uncertain estimation. The second control design is the robust nonlinear model predictive control considering different uncertain scenarios. Numerical simulations of two case studies aimed at lipid production and CO2 capture under different conditions are presented to evaluate the robust closed-loop performance.

Flashing lights affect the photophysiology and expression of carotenoid and lipid synthesis genes in Nannochloropsis gaditana

Nannochloropsis gaditana is a promising microalga for biotechnology. One of the strategies to stimulate its full potential in metabolite production is exposure to flashing lights. Here, we report how N. gaditana adapts to different flashing light regimes (5, 50, and 500 Hz) by changing its cellular physiology and the relative expression of genes related to critical cellular functions. We analyzed the differential mRNA abundance of genes related to photosynthesis, nitrogen assimilation and biosynthesis of chlorophyll, carotenoids, lipids, fatty acids and starch. Analysis of photosynthetic efficiency and high mRNA abundance of photoprotection genes supported the inference that excess excitation energy provided by light absorbance during photosynthesis was produced under low frequency flashing lights and was dissipated by photopigments via the xanthophyll-cycle. Increased relative expression levels of genes related to the synthesis of carotenoids and chlorophyll confirmed the accumulation of photopigments previously observed at low frequency flashing lights. Higher differential mRNA abundance of genes related to the triacylglycerol biosynthesis were observed at lower frequency flashing lights, possibly triggered by a poor nitrogen assimilation caused by low mRNA abundance of a nitrate reductase gene. This study advances a new understanding of algal physiology and metabolism leading to improved cellular performance and metabolite production.

Photosynthetic Carbon Uptake Correlates with Cell Protein Content during Lipid Accumulation in the Microalga Chlorella vulgaris NIES 227

Large-scale microalgae cultivation for biofuel production is currently limited by the possibility of maintaining high microalgae yield and high lipid content, concomitantly. In this study, the physiological changes of Chlorella vulgaris NIES 227 during lipid accumulation under nutrient limitation was monitored in parallel with the photosynthetic capacity of the microalgae to fix carbon from the proxy of oxygen productivity. In the exponential growth phase, as the biomass composition did not vary significantly (approx. 53.6 ± 7.8% protein, 6.64 ± 3.73% total lipids, and 26.0 ± 9.2% total carbohydrates of the total biomass dry-weight), the growth capacity of the microalgae was preserved (with net O2 productivity remaining above (4.44 ± 0.93) × 10−7 g O2·µmol PAR−1). Under nutrient limitation, protein content decreased (minimum of approx. 18.6 ± 6.0%), and lipid content increased (lipid content up to 56.0 ± 0.8%). The physiological change of the microalgae was associated with a loss of photosynthetic activity, down to a minimum (1.27 ± 0.26) × 10−7 g O2·µmol PAR−1. The decrease in photosynthetic O2 productivity was evidenced to correlate to the cell internal-protein content (R2 = 0.632, p = 2.04 × 10−6, N = 25). This approach could serve to develop productivity models, with the aim of optimizing industrial processes.

Features of Temperature Adaptation of Phaeodactylum tricornutum, Nitzschia sp., and Skeletonema costatum (Bacillariophyceae) under Different Light Conditions

Two types of possible adaptive response of the photosynthetic apparatus of diatoms to the changes in growth temperature conditions are shown. The first type is a temperature-dependent change in the content of chlorophyll in the cell, aimed at matching the rates of light and dark reactions of photosynthesis (noted in Phaeodactylum tricornutum and Nitzschia sp. 3). At the limiting light intensity, a temperature decrease from 20 to 5°C leads to an increase in the initial slope of light dependence of the C/Chl ratio; under the optimal light conditions at a temperature decrease from 20 to 10°C and from 10 to 5°C, the C/Chl ratio increases 1.5-fold in both species. The second type of response to the changes in growth temperature conditions was observed in Skeletonema costatum, for which the chlorophyll content in the cell does not depend on the temperature in the range of 10-20°C. The adaptation of the photosynthetic apparatus in this case probably occurs due to the changes in the activity of enzyme systems and in the rate of enzyme processes. The potential productivity of all studied species of algae at 10°C, calculated as the increase in biomass per unit of chlorophyll per day, does not differ significantly. Under the conditions of light inhibition, a temperature decrease leads to a progressive decrease in the content of chlorophyll in the cells of all algal species under study due to a decrease in the rate of pigment synthesis against the background of its intense photooxidation.

Flow cytometry and 5-ethynyl-2'-deoxyuridine (EdU) labeling to detect the cell cycle dynamics of Phaeodactylum tricornutum under light

Cell cycle studies in plants and algae are highly dependent on reliable methods for detecting cellular DNA replication. With its short growth cycle and ease of genetic transformation, Phaeodactylum tricornutum is an important model organism for the study of pennate diatoms. Here we explored two different methods to detect the cell cycle of P. tricornutum, one using SYBR-green I to via flow cytometry, and the other using EdU labeling to observe cell cycle changes under fluorescence microscopy. Both EdU labeling fluorescence microscopy and SYBR-green I staining flow cytometry accurately indicated that the cells of P. tricornutum enter the G2/M phase after 12 h of light exposure. The results indicate that SYBR Green I was an adequate detection method for nuclear DNA quantitation in cells of P. tricornutum using a flow cytometer and without RNase A treatment. In addition, EdU can be applied to P. tricornutum to reliably detect cell proliferation. Besides, Mg-ProtoIX was able to reverse the cell cycle division inhibition of P. tricornutum and allow the nuclear DNA replication to proceed normally. Taken together, the photoperiodic division time point was clearly identified, which sheds light on the regulation of cell division mechanism in P. tricornutum.

Fatty Acid Accumulations and Transcriptome Analyses Under Different Treatments in a Model Microalga Euglena gracilis

With the continuous growth of the world’s population and the increasing development of industrialization, the demand for energy by human beings has been expanding, resulting in an increasingly severe energy crisis. Microalgae are considered the most potential alternatives to traditional fossil fuels due to their many advantages, like fast growth rate, strong carbon sequestration capacity, and low growth environment requirements. Euglena can use carbon sources such as glucose, ethanol, and others for heterotrophic growth. Moreover, Euglena is highly adaptable to the environment and has a high tolerance to various environmental stresses, such as salinity, heavy metals, antibiotics, etc. Different treatments of Euglena cells could affect their growth and the accumulation of bioactive substances, especially fatty acids. To expand the industrial application of Euglena as a potential biodiesel candidate, we determine the physiological responses of Euglena against environmental stresses (antibiotics, heavy metals, salinity) or carbon resources (glucose and ethanol), and evaluate the potential for higher quality and yield of fatty acid with a high growth rate. Adding glucose into the culture media increases cell biomass and fatty acid production with high-quality biodiesel characters. The transcriptome analysis helped explore the possible regulation and biosynthesis of fatty acids under different treatments and exploited in the improvement of biodiesel production. This study provides insights for further improvement and various culture treatments for Euglena-based biodiesel and jet fuels.

Stochastic LCA Model of Upscaling the Production of Microalgal Compounds

Microalgae are currently being investigated for their promising metabolites but assessing the environmental impact of producing these compounds remains a challenge. Microalgae cultivation performance results from the complex interaction of biological, technological, geographical, and physical factors, which bioengineers try to optimize during the upscaling process. The path from the discovery of a microalgal compound to its industrial production is therefore highly uncertain. Nonetheless, it is key to anticipate the potential environmental impacts associated with the future production of a microalgal target compound. This is achieved in this study by developing an ex-ante, parameterized, and consequential LCA model that performs dynamic simulations of microalgae cultivation. The model is applied to calculate the environmental impacts of 9000 stochastically generated combinations of photobioreactor geometries and operational setups. The demonstration of the model is done for a fictive microalgal strain, parameterized to resemble Chlorella vulgaris, and a fictive target compound assumed to be a carbohydrate. The simulations are performed in Aalborg, Denmark, and Granada, Spain to appreciate geographical variability, which highly affects the requirements for thermoregulation. Open-source documentation allows full reproducibility and further use of the model for the ex-ante assessment of microalgal products.

Light attenuation in enriched purple phototrophic bacteria cultures: Implications for modelling and reactor design

Light attenuation in enriched purple phototrophic bacteria (PPB) cultures has not been studied, and its understanding is critical for proper process modelling and reactor design, especially for scaled systems. This work evaluated the effect of different biomass concentrations, reactor configurations, wastewater matrices, and growth conditions, on the attenuation extent of near infra-red (NIR) and ultraviolet-visible (UV-VIS) light spectra. The results show that increased biomass concentrations lead to higher light attenuation, and that PPB absorb both VIS and NIR wavelengths, with both fractions of the spectrum being equally absorbed at biomass concentrations above 1,000 g COD·m^-3. A flat plate configuration showed less attenuation compared with cylindrical reactors illuminated from the top, representative for open ponds. Neither a complex wastewater matrix nor the presence of polyhydroxyalkanoates (under nutrient limited conditions) affected light attenuation significantly. The pigment concentration (both bacteriochlorophyll and carotenoids) however, had a strong effect, with significant attenuation in the presence of pigments. Attenuation predictions using the Lambert-Beer law (excluding scattering) and the Schuster model (including scattering) indicated that light scattering had a minimal effect. A proposed mathematical model, based on the Lambert-Beer law and a Monod function for light requirements, allowed effective prediction of the kinetics of photoheterotrophic growth. This resulted in a half saturation coefficient of 4.6 W·m^-2. Finally, the results showed that in dense outdoor PPB cultures (≥1,000 g COD·m^-3), effective light penetration is only 5 cm, which biases design away from horizontal lagoons, and towards non-incident multi-panel systems such as flat plate reactors.

Modelling Nannochloropsis gaditana Growth in Reactors with Different Geometries, Determination of Kinetic Parameters and Biochemical Analysis in Response to Light Intensity

Microalgae are unicellular and photosynthetic microorganisms which grow thanks to inorganic salts, CO2 and light, and find applications in several fields thanks to their variety. The industrial application of microalgae has not often been fully exploited because of a lack of information about how microalgae respond to inputs and to different growth environments. In the present work a model able to predict the microalgae growth in reactors with different geometries was developed. We combined a Monod-like model for the specific growth rate with the Lambert-Beer law of homogeneous light distribution in thick photobioreactors. Kinetic parameters related to the cultivation of the microalga Nannochloropsis gaditana were obtained, for the first time through batch cultivation under different photon flux densities inside a quasi-isoactinic photobioreactor, in order to obtain a practically homogeneous light distribution. The maximum specific growth rate and saturation constant resulted, respectively as µmax = 0.0256 h−1 and Ik = 15.28 µE s−1m−2. These parameters were applied to the model to obtain data on microalgae growth in different geometries. Model simulation results are presented and discussed. Furthermore, biochemical analysis was performed on the biomass obtained at the end of each batch cultivation, grown both under different light intensities and in reactors with different configurations. Results indicated that lipid content increases with increasing average photon flux density. The fatty acid and carotenoids profiles markedly shift when the average light intensity varies: the PUFA content decreases and the SFA content increases when the average light intensity rises, and an accumulation of carotenoids at lower photon flux densities is observed. In conclusion, the model resulted in a useful tool, able to predict the growth of the microalga Nannochloropsis gaditana in reactors with different configurations.

A parametric logistic equation with light flux and medium concentration for cultivation planning of microalgae

Microalgae are considered to be promising producers of bioactive chemicals, feeds and fuels from carbon dioxide by photosynthesis. Thus, the prediction of microalgal growth profiles is important for the planning of cost-effective and sustainable cultivation–harvest cycles. This paper proposes a mathematical model capable of predicting the effect of light flux into culture and medium concentration on the growth profiles of microalgae by incorporating these growth-limiting factors into a logistic equation. The specific form of the equation is derived based on the experimentally measured growth profiles of Monoraphidium sp., a microalgal strain isolated by the authors, under 16 conditions consisting of combinations of incident light fluxes into culture and initial medium concentrations. Using a cross-validation method, it is shown that the proposed model has the ability to predict necessary incident light flux into culture and initial medium concentration for harvesting target biomass at a target time. Finally, model-guided cultivation planning is performed and is evaluated by comparing the result with experimental data.

Modeling and Simulation of Photobioreactors with Computational Fluid Dynamics—A Comprehensive Review

Computational Fluid Dynamics (CFD) have been frequently applied to model the growth conditions in photobioreactors, which are affected in a complex way by multiple, interacting physical processes. We review common photobioreactor types and discuss the processes occurring therein as well as how these processes have been considered in previous CFD models. The analysis reveals that CFD models of photobioreactors do often not consider state-of-the-art modeling approaches. As a comprehensive photobioreactor model consists of several sub-models, we review the most relevant models for the simulation of fluid flows, light propagation, heat and mass transfer and growth kinetics as well as state-of-the-art models for turbulence and interphase forces, revealing their strength and deficiencies. In addition, we review the population balance equation, breakage and coalescence models and discretization methods since the predicted bubble size distribution critically depends on them. This comprehensive overview of the available models provides a unique toolbox for generating CFD models of photobioreactors. Directions future research should take are also discussed, mainly consisting of an extensive experimental validation of the single models for specific photobioreactor geometries, as well as more complete and sophisticated integrated models by virtue of the constant increase of the computational capacity.

A seasonal simulation approach for culture depth influence on the temperature for different characterized microalgae strains

Irradiance and temperature are among the most important variables that affect microalgae growth, being both difficult to control in outdoor raceway reactors utilized for large-scale production of microalgae biomass. They are mainly a function of the location of the reactors, thus, producing certain strains of microalgae in inappropriate places conduces to the failure of the systems. To be able to determine important parameters of any microalgae strains on the performance of the culture, such as the influence of irradiance and temperature, is a powerful tool in decision-making processes. In addition, whatever the strain and location, operation strategies must be defined for each specific case, such as the imposed dilution rate and culture depth, both influencing the light availability and temperature of the culture as major variables determining the biomass productivity. In this paper, a simulation-based methodology is proposed to establish the influence of season and culture depth on the 1-year age irradiance and temperature of the culture, and thus on the biomass productivity of different microalgae strains. Up to five of the most frequently produced strains, such as Spirulina platensis, Chlorella vulgaris, Nannochloropsis gaditana, Isochrysis galbana and Scenedesmus almeriensis have been considered. The challenge is to develop an easy-to-manage decision-making tool for the optimal design and operation of large-scale microalgae facilities. Especially, dates for microalgae production and culture depth at which the reactors must be operated will be provided, being valid for any microalgae strain. The proposed methodology will largely contribute to the risk of investment in this field, then to enlarge the relevance of the microalgae production industry. This article is protected by copyright. All rights reserved.

Controlling average number of photons received per biomass to promote the growth of Synechocystis sp. PPC 6803

To investigate the actually received light of cells in the photo bioreactor, a light attenuation model of Synechocystis sp. PCC 6803 was established. The relationship between the average number of photons received per biomass (APRPB) and the growth of cell was analyzed. The results demonstrated, Cornet model was accurately fitted with the light attenuation of Synechocystis sp. PCC 6803 and the cell growth rate was affected by APRPB. When the value of APRPB is 3.2 μmol g^-1 s^-1, the cell have the maximum light efficiency. A maximum specific growth rate of 0.05 h^-1 was achieved with APRPB from 3.2 to 12.8 μmol g^-1 s^-1. After 156 h cultivation, compared to cells cultured under constant light [light intensity: 100 and 1800 μmol/(m² s)], the DCW under controlled light intensity (light intensity increasing with the cell density) was higher by 79.1% and 20.0%, respectively. This study indicated that APRPB could be used as a light intensity regulation criterion to improve cell production despite different types of reactor and cell density, which provided a theoretical basis for improving the biomass yield of Synechocystis sp. PCC 6803 or other photosynthetic auto-trophic organism.

A comprehensive review on the use of algal-bacterial systems for wastewater treatment with emphasis on nutrient and micropollutant removal

The scarcity of water resources and environmental pollution have highlighted the need for sustainable wastewater treatment. Existing conventional treatment systems are energy-intensive and not always able to meet stringent disposal standards. Recently, algal-bacterial systems have emerged as environmentally friendly sustainable processes for wastewater treatment and resource recovery. The algal-bacterial systems work on the principle of the symbiotic relationship between algae and bacteria. This paper comprehensively discusses the most recent studies on algal-bacterial systems for wastewater treatment, factors affecting the treatment, and aspects of resource recovery from the biomass. The algal-bacterial interaction includes cell-to-cell communication, substrate exchange, and horizontal gene transfer. The quorum sensing (QS) molecules and their effects on algal-bacterial interactions are briefly discussed. The effect of the factors such as pH, temperature, C/N/P ratio, light intensity, and external aeration on the algal-bacterial systems have been discussed. An overview of the modeling aspects of algal-bacterial systems has been provided. The algal-bacterial systems have the potential for removing micropollutants because of the diverse possible interactions between algae-bacteria. The removal mechanisms of micropollutants - sorption, biodegradation, and photodegradation, have been reviewed. The harvesting methods and resource recovery aspects have been presented. The major challenges associated with algal-bacterial systems for real scale implementation and future perspectives have been discussed. Integrating wastewater treatment with the algal biorefinery concept reduces the overall waste component in a wastewater treatment system by converting the biomass into a useful product, resulting in a sustainable system that contributes to the circular bioeconomy.

Industrial CO2 Capture by Algae: A Review and Recent Advances

The problem of global warming and the emission of greenhouse gases is already directly affecting the world’s energy. In the future, the impact of CO2 emissions on the world economy will constantly grow. In this paper, we review the available literature sources on the benefits of using algae cultivation for CO2 capture to decrease CO2 emission. CO2 emission accounts for about 77% of all greenhouse gases, and the calculation of greenhouse gas emissions is 56% of all CO2 imports. As a result of the study of various types of algae, it was concluded that Chlorella sp. is the best at capturing CO2. Various methods of cultivating microalgae were also considered and it was found that vertical tubular bioreactors are emerging. Moreover, for energy purposes, thermochemical methods for processing algae that absorb CO2 from flue gases were considered. Of all five types of thermochemical processes for producing synthesis gas, the most preferred method is the method of supercritical gasification of algae. In addition, attention is paid to the drying and flocculation of biofuels. Several different experiments were also reviewed on the use of flue gases through the cultivation of algae biomass. Based on this literature review, it can be concluded that microalgae are a third generation biofuel. With the absorption of greenhouse gases, the growth of microalgae cultures is accelerated. When a large mass of microalgae appears, it can be used for energy purposes. In the results, we present a plan for further studies of microalgae cultivation, a thermodynamic analysis of gasification and pyrolysis, and a comparison of the results with other biofuels and other algae cultures.

Matlab implementation of a novel semi-structured kinetic model for methanotroph-photoautotroph cocultures

This paper presents the matlab implementation details of a novel semi-structured kinetic model for methanotroph-photoautotroph cocultures. This includes the parameterization of the modeling equations, and the initialization of the simulation based on experimental conditions. More importantly, it provides details on how the differential equations governing mass balances in both gas and liquid phases are integrated together to simulate the system dynamics over time. The semi-structured kinetic model for methanotroph-photoautotroph coculture is validated using a wide range of experimental conditions. The model:•

Accurately predicts both the coculture growth in liquid phase and the gas composition changes in head space over time.

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Explicitly models the exchange of in situ produced O₂ and CO₂ within the coculture.

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Considers the self-shading effect on the growth of photoautotroph.

Process Engineering of Biopharmaceutical Production in Moss Bioreactors via Model-Based Description and Evaluation of Phytohormone Impact

The moss Physcomitrella is an interesting production host for recombinant biopharmaceuticals. Here we produced MFHR1, a synthetic complement regulator which has been proposed for the treatment of diseases associated to the complement system as part of human innate immunity. We studied the impact of different operation modes for the production process in 5 L stirred-tank photobioreactors. The total amount of recombinant protein was doubled by using fed-batch or batch compared to semi-continuous operation, although the maximum specific productivity (mg MFHR1/g FW) increased just by 35%. We proposed an unstructured kinetic model which fits accurately with the experimental data in batch and semi-continuous operation under autotrophic conditions with 2% CO₂ enrichment. The model is able to predict recombinant protein production, nitrate uptake and biomass growth, which is useful for process control and optimization. We investigated strategies to further increase MFHR1 production. While mixotrophic and heterotrophic conditions decreased the MFHR1-specific productivity compared to autotrophic conditions, addition of the phytohormone auxin (NAA, 10 µM) to the medium enhanced it by 470% in shaken flasks and up to 230% and 260%, in batch and fed-batch bioreactors, respectively. Supporting this finding, the auxin-synthesis inhibitor L-kynurenine (100 µM) decreased MFHR1 production significantly by 110% and 580% at day 7 and 18, respectively. Expression analysis revealed that the MFHR1 transgene, driven by the Physcomitrella actin5 (PpAct5) promoter, was upregulated 16 h after NAA addition and remained enhanced over the whole process, whereas the auxin-responsive gene PpIAA1A was upregulated within the first 2 hours, indicating that the effect of auxin on PpAct5 promoter-driven expression is indirect. Furthermore, the day of NAA supplementation was crucial, leading to an up to 8-fold increase of MFHR1-specific productivity (0.82 mg MFHR1/g fresh weight, 150 mg accumulated over 7 days) compared to the productivity reported previously. Our findings are likely to be applicable to other plant-based expression systems to increase biopharmaceutical production and yields.

The Oxygen Paradigm—Quantitative Impact of High Concentrations of Dissolved Oxygen on Kinetics and Large-Scale Production of Arthrospira platensis

The cultivation of Arthrospira platensis in tubular photobioreactors (tPBRs) presents a promising approach for the commercial production of nutraceuticals and food products as it can achieve high productivity and effective process control. In closed photobioreactors, however, high amounts of photosynthetically produced oxygen can accumulate. So far, there has been a wide range of discussion on how dissolved oxygen concentrations (DOCs) affect bioprocess kinetics, and the subject has mainly been assessed empirically. In this study, we used photorespirometry to quantify the impact of DOCs on the growth kinetics and phycocyanin content of the widely cultivated cyanobacterium A. platensis. The photorespirometric routine revealed that the illumination intensity and cell dry weight concentration are important interconnected process parameters behind the impact that DOCs have on the bioprocess kinetics. Unfavorable process conditions such as low biomass concentrations or high illumination intensities yielded significant growth inhibition and reduced the phycocyanin content of A. platensis by up to 35%. In order to predict the biomass productivity of the large-scale cultivation of A. platensis in tPBRs, a simple process model was extended to include photoautotrophic oxygen production and accumulation in the tPBR to evaluate the performance of two configurations of a 5000 L tPBR.

Different Regulatory Modes of Synechocystis sp. PCC 6803 in Response to Photosynthesis Inhibitory Conditions

Cyanobacteria are promising industrial platforms owing to their ability to produce diverse natural secondary metabolites and nonnative value-added biochemicals from CO₂ and light. To fully utilize their industrial potency, it is critical to understand their photosynthetic efficiency under various environmental conditions. In this study, we elucidated the inhibitory mechanisms of photosynthesis under high-light and low-temperature stress conditions in the model cyanobacterium Synechocystis sp. PCC 6803. Under each stress condition, the transcript abundance and translation efficiency were measured using transcriptome sequencing (RNA-seq) and ribosome profiling, and the genome-wide transcription unit architecture was constructed by data integration of transcription start sites and transcript 3′-end positions obtained from differential RNA-seq and sequencing of 3′-ends (Term-seq), respectively. Our results suggested that the mode of photosynthesis inhibition differed between the two stress conditions; high light stress induced photodamage responses, while low temperature stress impaired the translation efficiency of photosynthesis-associated genes. In particular, poor translation of photosystem I resulted from ribosome stalling at the untranslated regions, affecting the overall photosynthetic yield under low temperature stress. Our comprehensive multiomics analysis with transcription unit architecture provides foundational information on photosynthesis for future industrial strain development.

IMPORTANCE Cyanobacteria are a compelling biochemical production platform for their ability to propagate using light and atmospheric CO₂ via photosynthesis. However, the engineering of strains is hampered by limited understanding of photosynthesis under diverse environmental conditions such as high-light and low-temperature stresses. Herein, we decipher the transcriptomic and translatomic responses of the photosynthetic efficiency to stress conditions using the integrative analysis of multiomic data generated by RNA-seq and ribosome profiling, respectively. Through the generated massive data, along with the guide of the genome-wide transcription unit architecture constructed by transcription start sites and transcript 3′-end positions, we identified the factors affecting photosynthesis at transcription, posttranscription, and translation levels. Importantly, the high-light stress induces photodamage responses, and the low-temperature stress cripples the translation efficiency of photosynthesis-associated genes. The resulting insights provide pivotal information for future cyanobacterial cell factories powered by the engineering toward robust photosynthesis ability.

Spatiotemporal BME characterization and mapping of sea surface chlorophyll in Chesapeake Bay (USA) using auxiliary sea surface temperature data

Improving the spatiotemporal coverage of remote sensing (RS) products, such as sea surface chlorophyll concentration (SSCC), can offer a better understanding of the spatiotemporal SSCC distribution for ocean management purposes. In the first part of this work, 834 in-situ SSCC measurements of the SeaBASS-NASA (National Aeronautics and Space Administration) during 2002-2016 served as the empirical dataset. A moving window with ±3 days and ±0.5° centered at each of the in-situ SSCC measurements established a search neighborhood for Moderate Resolution Imaging Spectroradiometer Level 2 (MODIS L2) SSCC and MODIS L2 sea surface temperature (SST) data, and the matched SSCC and SST data were used for building a linear SSCC-SST relationship. The unmatched SST was introduced to the linear model for generating soft SSCC data with uniform distributions. The inherent spatiotemporal dependency of the SSCC distribution was then represented by the Bayesian maximum entropy (BME) method, which incorporated the soft SSCC data as auxiliary variable for SSCC estimation and mapping purposes. The results showed that a 75.3% accuracy improvement of remote SSCC retrieval in terms of R² can be achieved by BME-based method compared to the original MODIS L2 product. Subsequently, the BME-based method was applied to obtain daily SSCC dataset in Chesapeake Bay (USA) during the period 2010-2019. It was found that the SSCC distribution exhibited a decreasing spatial trend from the upper bay to the outer bay, whereas decreasing and increasing temporal trends were detected during the periods 2011-2014 and 2016-2019, respectively. The generalized Cauchy process was used to quantitatively describe the autocorrelation SSCC function in the Chesapeake Bay. The results showed that the outer bay exhibited the strongest long-range dependence among the four sub-regions, whereas the middle bay exhibited the weakest long-range dependence. Finally, one-point and two-point stochastic site indicators (SSIs) were employed to explore the spatiotemporal SSCC characteristics in Chesapeake Bay. The one-point SSI results showed that nearly 100% of the upper, middle and the lower bay areas experienced a high SSCC level (>5 mg/m³) during the entire study period. The area with SSCC >5 mg/m³ in the outer bay increased a lot during the winter season, but the area with SSCC >10 or 20 mg/m³ decreased significantly in the upper, middle and lower bay. Simultaneously, the SSCC dispersion in these areas was rather small during the winter season. On the other hand, the two-point SSI results showed that although the SSCC levels differ among the four sub-regions, but the SSCC connectivity structures between pairs of points also displayed some similarities in terms of their spatiotemporal dependency. In conclusion, the proposed BME-based method was shown to be a promising remote SSCC mapping technique that exhibited a powerful ability to improve both accuracy and coverage of RS products. The SSIs can be also used to explore the spatiotemporal characteristics of a variety of natural attributes in waters.

Quantitative analysis on photon numbers received per cell for triggering β-carotene accumulation in Dunaliella salina

Accumulation of β-carotene in Dunaliella salina is highly dependent on light exposure intensity and duration, but quantitative analysis on photon numbers received per cell for triggering β-carotene accumulation is not available so far. In this study, experiment results showed that significant β-carotene accumulation occurred after at least 8 h illumination at 400 µmol photons·m-2·s-1. To quantify the average number of photons received per cell, correlations of light attenuation with light path, biomass concentration, and β-carotene content were, respectively, established using both Lambert-Beer and Cornet models, and the latter provided better simulation. Using Cornet model, average number of photons received per cell (APRPC) was calculated and proposed as a parameter for β-carotene accumulation, and constant APRPC was maintained by adjusting average irradiance based on cell concentration and carotenoids content changes during the whole induction period. It was found that once APRPC reached 0.7 µmol photons cell-1, β-carotene accumulation was triggered, and it was saturated at 9.9 µmol photons cell-1. This study showed that APRPC can be used as an important parameter to precisely simulate and control β-carotene production by D. salina.

How Do Operational and Design Parameters Effect Biomass Productivity in a Flat-Panel Photo-Bioreactor? A Computational Analysis

Optimal production of microalgae in photo-bioreactors (PBRs) largely depends on the amount of light intensity received by individual algal cells, which is affected by several operational and design factors. A key question is: which process parameters have the highest potential for the optimization of biomass productivity? This can be analyzed by simulating the complex interplay of PBR design, hydrodynamics, dynamic light exposure, and growth of algal cells. A workflow was established comprising the simulation of hydrodynamics in a flat-panel PBR using computational fluid dynamics, calculation of light irradiation inside the PBR, tracing the light exposure of individual cells over time, and calculation the algal growth and biomass productivity based on this light exposure. Different PBR designs leading to different flow profiles were compared, and operational parameters such as air inlet flowrate, microalgal concentration, and incident light intensity were varied to investigate their effect on PBR productivity. The design of internal structures and lighting had a significant effect on biomass productivity, whereas air inlet flowrate had a minimal effect. Microalgal concentration and incident light intensity controlled the amount of light intensity inside the PBR, thereby significantly affecting the overall productivity. For detailed quantitative insight into these dependencies, better parameterization of algal growth models is required.

Microalgal growth, nitrogen uptake and storage, and dissolved oxygen production in a polyculture based-open pond fed with municipal wastewater in northern Sweden

Microalgal-based wastewater treatment and CO₂ sequestration from flue gases with subsequent biomass production represent a low-cost, eco-friendly, and effective procedure of removing nutrients and other pollutants from wastewater and assists in the decrease of greenhouse gas emissions. Thus, it supports a circular economy model. This is based on the ability of microalgae to utilise inorganic nutrients, mainly nitrogen and phosphorous, as well as organic and inorganic carbon, for their growth, and simultaneously reduce these substances in the water. However, the production of microalgae biomass under outdoor cultivation is dependent on several abiotic and biotic factors, which impact its profitability and sustainability. Thus, this study's goal was to evaluate the factors affecting the production of microalgae biomass on pilot-scale open raceway ponds under Northern Sweden's summer conditions with the help of a mathematical model. For this purpose, a microalgae consortium and a monoculture of Chlorella vulgaris were used to inoculate outdoor open raceway ponds. In line with the literature, higher biomass concentrations and nutrient removals were observed in ponds inoculated with the microalgae consortium. Our model, based on Droop's concept of macronutrient quotas inside the cell, corresponded well to the experimental data and, thus, can successfully be applied to predict biomass production, nitrogen uptake and storage, and dissolved oxygen production in microalgae consortia.

Multi-scale modeling of intensive macroalgae cultivation and marine nitrogen sequestration

Multi-scale macroalgae growth models are required for the efficient design of sustainable, economically viable, and environmentally safe farms. Here, we develop a multi-scale model for Ulva sp. macroalgae growth and nitrogen sequestration in an intensive cultivation farm, regulated by temperature, light, and nutrients. The model incorporates a range of scales by incorporating spatial effects in two steps: light extinction at the reactor scale (1 m) and nutrient absorption at the farm scale (1 km). The model was validated on real data from an experimental reactor installed in the sea. Biomass production rates, chemical compositions, and nitrogen removal were simulated under different seasons, levels of dilution in the environment and water-exchange rate in the reactor. This multi-scale model provides an important tool for environmental authorities and seaweed farmers who desire to upscale to large bioremediation and/or macroalgae biomass production farms, thus promoting the marine sustainable development and the macroalgae-based bioeconomy.

Zollmann et al. develop a multi-scale model for Ulva sp. macroalgae growth, biochemical composition, and nitrogen sequestration, regulated by temperature, light and nutrients. Their results demonstrate that this model can be used to design environmentally friendly and economically sustainable seaweed farms.

Techno-economic evaluation of microalgae high-density liquid fuel production at 12 international locations

BACKGROUND

Microalgae-based high-density fuels offer an efficient and environmental pathway towards decarbonization of the transport sector and could be produced as part of a globally distributed network without competing with food systems for arable land. Variations in climatic and economic conditions significantly impact the economic feasibility and productivity of such fuel systems, requiring harmonized technoeconomic assessments to identify important conditions required for commercial scale up.

METHODS

Here, our previously validated Techno-economic and Lifecycle Analysis (TELCA) platform was extended to provide a direct performance comparison of microalgae diesel production at 12 international locations with variable climatic and economic settings. For each location, historical weather data, and jurisdiction-specific policy and economic inputs were used to simulate algal productivity, evaporation rates, harvest regime, CapEx and OpEx, interest and tax under location-specific operational parameters optimized for Minimum Diesel Selling Price (MDSP, US$ L^-1). The economic feasibility, production capacity and CO_2-eq emissions of a defined 500 ha algae-based diesel production facility is reported for each.

RESULTS

Under a for-profit business model, 10 of the 12 locations achieved a minimum diesel selling price (MDSP) under US$ 1.85 L^-1 / US$ 6.99 gal^-1. At a fixed theoretical MDSP of US$ 2 L^-1 (US$ 7.57 gal^-1) these locations could achieve a profitable Internal Rate of Return (IRR) of 9.5-22.1%. Under a public utility model (0% profit, 0% tax) eight locations delivered cost-competitive renewable diesel at an MDSP of < US$ 1.24 L^-1 (US$ 4.69 gal^-1). The CO_2-eq emissions of microalgae diesel were about one-third of fossil-based diesel.

CONCLUSIONS

The public utility approach could reduce the fuel price toward cost-competitiveness, providing a key step on the path to a profitable fully commercial renewable fuel industry by attracting the investment needed to advance technology and commercial biorefinery co-production options. Governments' adoption of such an approach could accelerate decarbonization, improve fuel security, and help support a local COVID-19 economic recovery. This study highlights the benefits and limitations of different factors at each location (e.g., climate, labour costs, policy, C-credits) in terms of the development of the technology-providing insights on how governments, investors and industry can drive the technology forward.

Climate-Simulated culturing suggests high microalgal biomass and oil productivities in most of the South American continent

BACKGROUND

Current production costs of microalgal biomass indicate that only highly-productive cultivation facilities will approach commercial feasibility. Geographical site selection for siting those facilities is critical for achieving target productivities. The aim of this study was to provide a semi-empirical estimation of microalgal biomass and lipids productivity in South America.

METHODS AND RESULTS

Simulated-climate was programed in environmental photobioreactors (Phenometrics) for a simulation of cultivation in open raceway ponds at different geographical sites. The mean annual South American biomass productivity of 20-cm deep ponds was 12 ± 4 g · m^{- 2} · d^-1 . The most productive regions were clustered in the subtropical and tropical regions of the continent. Fortaleza (Brazil) showed a low seasonality and a high annual mean productivity of 23 g · m^-2 · d^-1 in 5-cm deep ponds, closely approaching the productivity target. Lipids accumulation and productivity in Fortaleza showed a high microalgal oil accumulation up to 46% (w/w) and a maximum oil productivity of 5 g · m^-2 · d^-1 for biomass containing around 20% lipids (w/w).

CONCLUSION

This study provides the first semi-empirical estimation of microalgal productivity in South America and supports a high potential of a vast region of the continent.

A new approach for calculating microalgae culture growth based on an inhibitory effect of the surrounding biomass

Ever since the potential of algae in biotechnology was recognized, models describing the growth of algae inside photobioreactors have been proposed. These models are the basis for the optimization of process conditions and reactor designs. Over the last few decades, models became more and more elaborate with the increase of computational capacity. Thus far, these models have been based on light attenuation due to the absorption and scattering effects of the biomass. This manuscript presents a new way of predicting the apparent growth inside photobioreactors using simple models for enzymatic kinetics to describe the reaction between photons and the photosynthetic unit. The proposed model utilizes an inhibition kinetic formula based on the surrounding biomass to describe the average growth rate of a culture, which is determined by the local light intensities inside the reactor. The result is a mixed-inhibition scheme with multiple inhibition sites. The parameters of the new kinetic equation are replaced by empirical regression functions to correlate their dependency on incident light intensity and reactor size. The calibrations of the parameters and the regression functions are based on the numerical solutions of the growth rate computed with a classical Type II model. As a final verification, we apply the new equation in predicting the growth behavior of three phototrophic organisms in reactors of three different sizes.