Models of microalgal cultivation for added-value products - A review

A robust multinutrient kinetic model for enhanced lutein and biomass yields in mixotrophic microalgae cultivation: A step towards successful large-scale productions

Mixotrophic cultivation holds great promise to significantly enhance the productivities of biomass and valuable metabolites from microalgae. In this study, a new kinetic model is developed, explicitly describing the effect of the most influential environmental factors on both biomass growth and the production of the high-value product lutein. This extensive study of multinutrient kinetics for Tetradesmus obliquus in a mixotrophic regime covers various nutritional conditions. Crucial nutrients governing the model include nitrate, phosphate, and glucose. Using seven state variables and 13 unknown parameters, the model's accuracy was ensured through a well-designed two-factor, four-level experimental setup, providing ample data for reliable calibration and validation. Results accurately predict dynamic concentration profiles for all validation experiments, revealing broad applicability. Optimizing nitrogen availability led to significant increases in biomass (up to fourfold) and lutein production (up to 12-fold), with observed maximum biomass concentration of 6.80 g L-1 and lutein reaching 25.58 mg L-1. Noticeably, the model exhibits a maximum specific growth rate of 4.03 day-1, surpassing reported values for photoautotrophic and heterotrophic conditions, suggesting synergistic effects. Valuable guidance is provided for applying the method to various microalgal species and results are large-scale production-ready. Future work will exploit these results to develop real-time photobioreactor operation strategies.

A high throughput array microhabitat platform reveals how light and nitrogen colimit the growth of algal cells

A mechanistic understanding of algal growth is essential for maintaining a sustainable environment in an era of climate change and population expansion. It is known that algal growth is tightly controlled by complex interactive physical and chemical conditions. Many mathematical models have been proposed to describe the relation of algal growth and environmental parameters, but experimental verification has been difficult due to the lack of tools to measure cell growth under precise physical and chemical conditions. As such, current models depend on the specific testing systems, and the fitted growth kinetic constants vary widely for the same organisms in the existing literature. Here, we present a microfluidic platform where both light intensity and nutrient gradients can be well controlled for algal cell growth studies. In particular, light shading is avoided, a common problem in macroscale assays. Our results revealed that light and nitrogen colimit the growth of algal cells, with each contributing a Monod growth kinetic term in a multiplicative model. We argue that the microfluidic platform can lead towards a general culture system independent algal growth model with systematic screening of many environmental parameters. Our work advances technology for algal cell growth studies and provides essential information for future bioreactor designs and ecological predictions.

Comparative assessment of microalgal growth kinetic models based on light intensity and biomass concentration

The comprehensive evaluation and validation of mathematical models for microalgal growth dynamics are essential for improving cultivation efficiency and optimising photobioreactor design. A considerable gap in comprehending the relation between microalgal growth, light intensity and biomass concentration arises since many studies focus solely on associating one of these factors. This paper compares microalgal growth kinetic models, specifically focusing on the combined impact of light intensity and biomass concentration. Considering a dataset (experimental results and literature values) concerning Chlorella vulgaris, nine kinetic models were assessed. Bannister and Grima models presented the best fitting performance to experimental data (RMSE ≤ 0.050 d-1; R2≥0.804; d2≥0.943). Cultivation conditions conducting photoinhibition were identified in some kinetic models. After testing these models on independent datasets, Bannister and Grima models presented superior predictive performance (RMSE = 0.022-0.023 d-1; R2 = 0.878-0.884; d2: 0.976-0.975). The models provide valuable tools for predicting microalgal growth and optimising operational parameters, reducing the need for time-consuming and costly experiments.

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.

Thermal response analysis and compilation of cardinal temperatures for 424 strains of microalgae, cyanobacteria, diatoms and other species

Microalgae and other phototrophic microorganisms can be cultivated to produce food and valuable bioproducts, also allowing to remove nutrients from wastewater and CO₂ from biogas or polluted gas streams. Among other environmental and physico-chemical parameters, microalgal productivity is strongly influenced by the cultivation temperature. In this review, cardinal temperatures identifying the thermal response, i.e., the optimal growth condition (T_OPT), and the lower and upper limits for microalgae cultivation (T_MIN and T_MAX), have been included in a structured and harmonized database. Literature data for 424 strains belonging to 148 genera of green algae, cyanobacteria, diatoms, and other phototrophs were tabulated and analysed, with a focus on the most relevant genera that are currently cultivated at the industrial scale in Europe. The dataset creation aimed at facilitating the comparison of different strain performances for different operational temperatures and assisting in the process of thermal and biological modelling, to reduce energy consumption and biomass production costs. A case study was presented, to illustrate the effect of temperature control on the energetic expenditure for cultivating different Chorella sp. strains under a greenhouse located in different European sites.

A reliable multi-nutrient model for the rapid production of high-density microalgal biomass over a broad spectrum of mixotrophic conditions

The superior microalgal biomass productivities obtained under mixotrophic conditions have been widely demonstrated. However, to attain the full potential of the method, optimal conditions for biomass production and resource utilization need to be determined and successfully exploited throughout the process operation. Detailed kinetic mathematical models have often proved most efficient tools for predicting process behavior and governing its overall operation. This paper presents an extensive study for obtaining a highly reliable model for mixotrophic production of microalgae covering a wide set and range of nutritional conditions (10-fold the concentration range of Bold's Basal Medium) and biomass yields up to 6.68 g.L^-1 after only 6 days. The final reduced model includes a total of five state variables and nine parameters: model calibration resulted in very small 95% confidence intervals and relative errors below 5% for all parameters. Model validation showed high reliability with R² correlation values between 0.77 and 0.99.

Structured population balances to support microalgae-based processes: Review of the state-of-art and perspectives analysis

Design and optimization of microalgae processes have traditionally relied on the application of unsegregated mathematical models, thus neglecting the impact of cell-to-cell heterogeneity. However, there is experimental evidence that the latter one, including but not limited to variation in mass/size, internal composition and cell cycle phase, can play a crucial role in both cultivation and downstream processes. Population balance equations (PBEs) represent a powerful approach to develop mathematical models describing the effect of cell-to-cell heterogeneity. In this work, the potential of PBEs for the analysis and design of microalgae processes are discussed. A detailed review of PBE applications to microalgae cultivation, harvesting and disruption is reported. The review is largely focused on the application of the univariate size/mass structured PBE, where the size/mass is the only internal variable used to identify the cell state. Nonetheless, the need, addressed by few studies, for additional or alternative internal variables to identify the cell cycle phase and/or provide information about the internal composition is discussed. Through the review, the limitations of previous studies are described, and areas are identified where the development of more reliable PBE models, driven by the increasing availability of single-cell experimental data, could support the understanding and purposeful exploitation of the mechanisms determining cell-to-cell heterogeneity.

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.

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

•

We develop a novel multiscale model for microalgal photoautotrophic growth.

•

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

•

We combine the model with cultivation experiments of Haematococcus pluvialis.

•

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

•

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.

Algal biorefinery culminating multiple value-added products: recent advances, emerging trends, opportunities, and challenges

Algal biorefinery is rising as a prominent solution to economically fulfill the escalating global requirement for nutrition, feed, fuel, and medicines. In recent years, scientific productiveness associated with microalgae-based studies has elaborated in multiplied aspects, while translation to the commercial level continues to be missing. The present microalgal biorefinery has a challenge in long-term viability due to escalated market price of algal-mediated biofuels and bioproducts. Advancements are required in a few aspects like improvement in algae processing, energy investment, and cost analysis of microalgae biorefinery. Therefore, it is essential to recognize the modern work by understanding the knowledge gaps and hotspots driving business scale up. The microalgae biorefinery integrated with energy-based products, bioactive and green compounds, focusing on a circular bioeconomy, is urgently needed. A detailed investigation of techno-economic analysis (TEA) and life cycle assessment (LCA) is important to increase the market value of algal products. This review discusses the valorization of algal biomass for the value-added application that holds a sustainable approach and cost-competitive algal biorefinery. The current industries, policies, technology transfer trends, challenges, and future economic outlook are discussed. This study is an overview through scientometric investigation attempt to describe the research development contributing to this rising field.

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.

Improving carbohydrate accumulation in Chlamydomonas debaryana induced by sulfur starvation using response surface methodology

Most methods that promote carbohydrate production negatively affect cell growth and microalgal biomass production. This study explores, in a two-stage cultivation strategy, in Chlamydomonas debaryana the optimization of certain culture conditions for high carbohydrate production without loss of biomass. In the first stage, the interaction between sodium bicarbonate supplementation, aeration, and different growth periods was optimized using the response surface methodology (RMS). The 3-factor Box-Behnken design (BBD) was applied, and a second-order polynomial regression analysis was used to analyze the experimental data. The results showed that 0.45 g L^-1 of sodium bicarbonate combined with a good aerated agitation (0.6 L min^-1) and a cultivation period of 18 days are optimal to produce 5.02 g L^-1 of biomass containing 43% of carbohydrates.Under these optimized growth conditions, accumulation of carbohydrates was studied using different modes of nutritional stress. The results indicated that carbohydrate content was improved and the maximum accumulation (about 60% of the dry weight) was recorded under sulfur starvation with only a 14% reduction in biomass as compared to control. This study showed promising results as to biomass production and carbohydrate yield by the microalgae C. debaryana in view of production of third-generation biofuels.

Continuous Culture of Auxenochlorella protothecoides on Biodiesel Derived Glycerol under Mixotrophic and Heterotrophic Conditions: Growth Parameters and Biochemical Composition

As crude glycerol comprises a potential substrate for microalga fermentation and value added products’ biosynthesis, Auxenochlorella protothecoides was grown on it under heterotrophic and mixotrophic conditions and its growth kinetics were evaluated in a continuous system under steady state conditions. Increasing initial glycerol concentration (from 30 to 50 g/L) in the heterotrophic culture led to reduced biomass yield (Y_x/S) and productivity (P_x), but favored lipid accumulation. Under heterotrophic conditions, the microalga was found to grow better (biomass up to 7.888 g/L) and faster (higher growth rates), the system functioned more effectively (higher P_x) and crude glycerol was exploited more efficiently. Heterotrophy also favored proteins synthesis (up to 53%), lipids (up to 9.8%), and carbohydrates (up to 44.6%) accumulation. However, different trophic modes had no significant impact on the consistency of proteins and lipids. Oleic acid was the most abundant fatty acid detected (55–61.2% of the total lipids). The algal biomass contained many essential and non-essential amino acids, especially arginine, glutamic acid, lysine, aspartic acid, leucine, and alanine. In all the experimental trials, the protein contents in the microalgal biomass increased with the increasing dilution rate (D), with a concomitant decrease in the lipids and carbohydrates fractions.

Improving microalgae for biotechnology - From genetics to synthetic biology - Moving forward but not there yet

Microalgae are a diverse group of photosynthetic organisms that can be exploited for the production of different compounds, ranging from crude biomass and biofuels to high value-added biochemicals and synthetic proteins. Traditionally, algal biotechnology relies on bioprospecting to identify new highly productive strains and more recently, on forward genetics to further enhance productivity. However, it has become clear that further improvements in algal productivity for biotechnology is impossible without combining traditional tools with the arising molecular genetics toolkit. We review recent advantages in developing high throughput screening methods, preparing genome-wide mutant libraries, and establishing genome editing techniques. We discuss how algae can be improved in terms of photosynthetic efficiency, biofuel and high value-added compound production. Finally, we critically evaluate developments over recent years and explore future potential in the field.

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high-fidelity model is challenging due to the system's variability, also known as batch-to-batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar-based fermentation and provides a novel approach for bioprocess dynamic modeling.

Indoor mass cultivation of marine diatoms for biodiesel production using induction plasma synthesized nanosilica

In this work, two benthic marine diatoms Chaetoceros sp. and Thalassiosira sp. were grown in modified f/2 medium in which normal silica was replaced with inductively coupled plasma (ICP) nanosilica for indoor mass cultivation and its impact on growth, lipid content, lipid quality and metabolite production were monitored. Results indicate thatunder mass cultivation using ICP nano silica medium, Thalassiosirasp. reached 3.6 and Chaetoceros sp. reached 3.2-fold higher cell density compared to normal Si medium. The primary metabolite production and total lipid content was higher in Chaetoceros sp. (44.33 ± 2.51% DCW) compared to Thalassiosira sp. (29.66 ± 1.52% DCW). In mass cultivation, ICP synthesized nanosilica powder was effective in enhancing the cell density, production of metabolites, pigments, and lipids in the marine diatoms studied. This is the first report on the use of ICP nanosilica in carrying out indoor mass cultivation of marine diatom isolates as potential biodiesel and biomolecule feedstocks.

The Antihypertensive Effects and Potential Molecular Mechanism of Microalgal Angiotensin I-Converting Enzyme Inhibitor-Like Peptides: A Mini Review

Hypertension causes many deaths worldwide and has shown an increasing trend as a severe non-communicable disease. Conventional antihypertensive drugs inevitably cause side effects, and great efforts have been made to exploit healthier and more-available substitutes. Microalgae have shown great potential in this regard and have been applied in the food and pharmaceutical industries. Some compounds in microalgae have been proven to have antihypertensive effects. Among these natural compounds, peptides from microalgae are promising angiotensin-converting enzyme (ACE) inhibitors because an increasing number of peptides show hypertensive effects and ACE inhibitory-like activity. In addition to acting as ACE inhibitors for the treatment of hypertension, these peptides have other probiotic properties, such as antioxidant and anti-inflammatory properties, that are important for the prevention and treatment of hypertension. Numerous studies have revealed the important bioactivities of ACE inhibitors and their mechanisms. This review discusses the antihypertensive effects, structure-activity relationships, molecular docking studies, interaction mechanisms, and other probiotic properties of microalgal ACE inhibitory peptides according to the current research related to microalgae as potential antihypertensive drugs. Possible research directions are proposed. This review contributes to a more comprehensive understanding of microalgal antihypertensive peptides.

Optimisation of microalgal cultivation via nutrient-enhanced strategies: the biorefinery paradigm

BACKGROUND

The production of microalgal biofuels, despite their sustainable and renowned potential, is not yet cost-effective compared to current conventional fuel technologies. However, the biorefinery concept increases the prospects of microalgal biomass as an economically viable feedstock suitable for the co-production of multiple biofuels along with value-added chemicals. To integrate biofuels production within the framework of a microalgae biorefinery, it is not only necessary to exploit multi-product platforms, but also to identify optimal microalgal cultivation strategies maximising the microalgal metabolites from which biofuels are obtained: starch and lipids. Whilst nutrient limitation is widely known for increasing starch and lipid formation, this cultivation strategy can greatly reduce microalgal growth. This work presents an optimisation framework combining predictive modelling and experimental methodologies to effectively simulate and predict microalgal growth dynamics and identify optimal cultivation strategies.

RESULTS

Microalgal cultivation strategies for maximised starch and lipid formation were successfully established by developing a multi-parametric kinetic model suitable for the prediction of mixotrophic microalgal growth dynamics co-limited by nitrogen and phosphorus. The model's high predictive capacity was experimentally validated against various datasets obtained from laboratory-scale cultures of Chlamydomonas reinhardtii CCAP 11/32C subject to different initial nutrient regimes. The identified model-based optimal cultivation strategies were further validated experimentally and yielded significant increases in starch (+ 270%) and lipid (+ 74%) production against a non-optimised strategy.

CONCLUSIONS

The optimised microalgal cultivation scenarios for maximised starch and lipids, as identified by the kinetic model presented here, highlight the benefits of exploiting modelling frameworks as optimisation tools that facilitate the development and commercialisation of microalgae-to-fuel technologies.