Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

A multiscale modelling approach for Haematococcus pluvialis cultivation under different environmental conditions

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We develop a novel multiscale model for microalgal photoautotrophic growth.

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The model is segregated-structured type based on Population Balance Equations.

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We combine the model with cultivation experiments of Haematococcus pluvialis.

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We successfully predict cell number, average volume and density distribution dynamics.

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Model can accurately describe the nutrient depletion phase including cell lysis.

Haematococcus pluvialis can produce significant amounts of industrially important compounds belonging to lipids and starch classes, including various specific pigments such as β-carotene, lutein and astaxanthin, as well as lipids, carbohydrates and proteins. Their production can vary depending on environmental stress conditions like nutrient starvation. However, stress conditions lead also to undesired phenomena such as cell lysis, which is likely to be related to products loss. The microorganism develops towards smaller single cell volumes during the growth process, and eventually, more likely towards lysis when fission (i.e. cell division) slows down. The lysis process takes place simultaneously with nutrient depletion, so both growth and lysis are linked to the change of environmental conditions. In this work, we develop a novel multiscale segregated-structured model based on Population Balance Equations (PBEs) to describe the photoautotrophic growth of H.pluvialis, in particular cell growth, and lysis, making possible the description of the relationship between cell volume/transition, cell loss, and metabolic product availability. Cell volume is the internal coordinate of the population balance model, and its link with intrinsic concentrations is also presented. The model parameters are fitted against experimental data, extensive sensitivity analysis is performed and the model predictive capabilities are tested in terms of cell density distributions, as well as 0th and 1st order moments.

Bioenergy, Biofuels, Lipids and Pigments—Research Trends in the Use of Microalgae Grown in Photobioreactors

This scientometric review and bibliometric analysis aimed to characterize trends in scientific research related to algae, photobioreactors and astaxanthin. Scientific articles published between 1995 and 2020 in the Web of Science and Scopus bibliographic databases were analyzed. The article presents the number of scientific articles in particular years and according to the publication type (e.g., articles, reviews and books). The most productive authors were selected in terms of the number of publications, the number of citations, the impact factor, affiliated research units and individual countries. Based on the number of keyword occurrences and a content analysis of 367 publications, seven leading areas of scientific interest (clusters) were identified: (1) techno-economic profitability of biofuels, bioenergy and pigment production in microalgae biorefineries, (2) the impact of the construction of photobioreactors and process parameters on the efficiency of microalgae cultivation, (3) strategies for increasing the amount of obtained lipids and obtaining biodiesel in Chlorella microalgae cultivation, (4) the production of astaxanthin on an industrial scale using Haematococcus microalgae, (5) the productivity of biomass and the use of alternative carbon sources in microalgae culture, (6) the effect of light and carbon dioxide conversion on biomass yield and (7) heterotrophy. Analysis revealed that topics closely related to bioenergy production and biofuels played a dominant role in scientific research. This publication indicates the directions and topics for future scientific research that should be carried out to successfully implement economically viable technology based on microalgae on an industrial scale.

Kinetic modeling and process analysis for photo-production of β-carotene in Dunaliella salina

Dunaliella salina is a green microalga with the great potential to generate natural β-carotene. However, the corresponding mathematical models to guide optimized production of β-carotene in Dunaliella salina (D. salina) are not yet available. In this study, dynamic models were proposed to simulate effects of environmental factors on cell growth and β-carotene production in D. salina using online monitoring system. Moreover, the identification model of the parameter variables was established, and an adaptive particle swarm optimization algorithm based on parameter sensitivity analysis was constructed to solve the premature problem of particle swarm algorithm. The proposed kinetic model is characterized by high accuracy and predictability through experimental verification, which indicates its competence for future process design, control, and optimization. Based on the model established in this study, the optimal environmental factors for both β-carotene production and microalgae growth were identified. The approaches created are potentially useful for microalga Dunaliella salina cultivation and high-value β-carotene production.

Computational Analysis of Dynamic Light Exposure of Unicellular Algal Cells in a Flat-Panel Photobioreactor to Support Light-Induced CO2 Bioprocess Development

Cyanobacterial cell factories trace a vibrant pathway to climate change neutrality and sustainable development owing to their ability to turn carbon dioxide-rich waste into a broad portfolio of renewable compounds, which are deemed valuable in green chemistry cross-sectorial applications. Cell factory design requires to define the optimal operational and cultivation conditions. The paramount parameter in biomass cultivation in photobioreactors is the light intensity since it impacts cellular physiology and productivity. Our modeling framework provides a basis for the predictive control of light-limited, light-saturated, and light-inhibited growth of the Synechocystis sp. PCC 6803 model organism in a flat-panel photobioreactor. The model here presented couples computational fluid dynamics, light transmission, kinetic modeling, and the reconstruction of single cell trajectories in differently irradiated areas of the photobioreactor to relate key physiological parameters to the multi-faceted processes occurring in the cultivation environment. Furthermore, our analysis highlights the need for properly constraining the model with decisive qualitative and quantitative data related to light calibration and light measurements both at the inlet and outlet of the photobioreactor in order to boost the accuracy and extrapolation capabilities of the model.

Indirect regulation of temperature in raceway reactors by optimal management of culture depth

Temperature and irradiance are the two most relevant factors determining the performance of microalgae cultures in open raceway reactors. Moreover, inadequate temperature strongly reduces the biomass productivity in these systems even if enough sunlight is available. Controlling the temperature in large open raceway reactors is considered unaffordable because of the large amount of energy required. This study presents an indirect method for temperature regulation in microalgae raceway reactors by optimizing the culture depth. First, the effect of the culture depth on the raceway temperature is analyzed for different seasons of the year. Afterward, a simulation study is presented where the proposed control approach is compared to the normal operation mode with constant volume in the reactor. This study is also extended to industrial scale. Relevant improvements on the temperature factor and biomass production are presented. The developed knowledge allows the improvement of the performance in open raceway reactors up to 12% without involving additional energy and costs, being a suitable solution for large industrial reactors that until now have no options for controlling the temperature.

A new model to analyze the temperature effect on the microalgae performance at large scale raceway reactors

In this study a simplified temperature model for raceway reactors is developed, allowing to determine the temperature of the microalgae culture as a function of reactor design and environmental conditions. The model considers the major phenomena taking place in raceway reactors, especially heat absorption by radiation and heat losses by evaporation among others. The characteristic parameters of the model have been calibrated using genetic algorithms, next being validated with a long set of more than 50 days covering different weather conditions. It is worth to highlight the use of the developed model as a tool to analyze the influence of the temperature on the performance of microalgae cultures at large scale. As example, the annual variation of the performance of up to five different microalgae strains has been determined by computing the temperature index, thus the normalized value of performance of whatever microalgae at the real temperature with respect to that achievable at optimal temperature can be established. Results confirm that only strains tolerant to wide ranges of temperature can be efficiently produced all the year around in large scale outdoor raceway reactors without additional temperature control systems.

A Non-Vector Approach to Increase Lipid Levels in the Microalga Planktochlorella nurekis

Microalgae are freshwater and marine unicellular photosynthetic organisms that utilize sunlight to produce biomass. Due to fast microalgal growth rate and their unique biochemical profiles and potential applications in food and renewable energy industries, the interest in microalgal research is rapidly increasing. Biochemical and genetic engineering have been considered to improve microalgal biomass production but these manipulations also limited microalgal growth. The aim of the study was the biochemical characterization of recently identified microalgal strain Planktochlorella nurekis with elevated cell size and DNA levels compared to wild type strain that was achieved by a safe non-vector approach, namely co-treatment with colchicine and cytochalasin B (CC). A slight increase in growth rate was observed in twelve clones of CC-treated cells. For biochemical profiling, several parameters were considered, namely the content of proteins, amino acids, lipids, fatty acids, β-glucans, chlorophylls, carotenoids, B vitamins and ash. CC-treated cells were characterized by elevated levels of lipids compared to unmodified cells. Moreover, the ratio of carotenoids to chlorophyll a and total antioxidant capacity were slightly increased in CC-treated cells. We suggest that Planktochlorella nurekis with modified DNA levels and improved lipid content can be considered to be used as a dietary supplement and biofuel feedstock.

Kinetics simulation of Cu and Cd removal and the microbial community adaptation in a periphytic biofilm reactor

Periphytic biofilm reactor (PBfR) shows great potential in pollutants removal. However, few studies were focused on mathematical model of pollutants removal in PBfR. A three-step PBfR was designed and a new model was developed to simulate the kinetics of Cu and Cd removal from simulated wastewater. The results show that the PBfR could remove 99.0% Cu and 99.7% Cd from liquid wastewater. The experiment data could be well fitted with a high correlation coefficients both for Cu and Cd. The microbial community in the PBfR could be self-adjusted to tolerate the toxicities of Cu and Cd, resulting in sustainable and high decontamination efficiencies. The eukaryote in the PBfR played a vital role in Cu and Cd removal. The prokaryote showed negative effect on Cu and Cd removal, though it had more diversity than eukaryote. This study provides a new approach for Cu and Cd removal and their kinetics simulation in photoautotrophic bioreactor.

Application of growth-phase based light-feeding strategies to simultaneously enhance Chlorella vulgaris growth and lipid accumulation

Considering the variations of optimal light intensity required by microalgae cells along with growth phases, growth-phase light-feeding strategies were proposed and verified in this paper, aiming at boosting microalgae lipid productivity from the perspective of light conditions optimization. Experimental results demonstrate that under an identical time-averaged light intensity, the light-feeding strategies characterized by stepwise incremental light intensities showed a positive effect on biomass and lipid accumulation. The lipid productivity (235.49 mg L^-1 d^-1) attained under light-feeding strategy V (time-averaged light intensity: 225 μmol m^-2 s^-1) was 52.38% higher over that obtained under a constant light intensity of 225 μmol m^-2 s^-1. Subsequently, based on light-feeding strategy V, microalgae lipid productivity was further elevated to 312.92 mg L^-1 d^-1 employing a two-stage based light-feeding strategy V₅₆₀ (time-averaged light intensity: 360 μmol m^-2 s^-1), which was 79.63% higher relative to that achieved under a constant light intensity of 360 μmol m^-2 s^-1.