Modelling of microalgal growth and lipid production in Dunaliella tertiolecta using nitrogen-phosphorus-potassium fertilizer medium in sintered disk chromatographic glass bubble column

Chlorella vulgaris cultivation in simulated wastewater for the biomass production, nutrients removal and CO2 fixation simultaneously

Chlorella vulgaris (C. vulgaris) was promising microalgae to simultaneously achieve biomass production, carbon dioxide (CO₂) fixation, nutrients removal and proteins production especially under different conditions of CO₂ gas and wastewaters. Results presented that maximal specific growth rate of C. vulgaris was 0.21-0.35 d^-1 and 0.33-0.43 d^-1 at 0.038% and 10% CO₂ respectively, and corresponding maximal CO₂ fixation rate was attended with 4.51-14.26 and 56.26-85.72 mg CO₂·L^-1·d^-1. C. vulgaris showed good wastewater removal efficiency of nitrogen and phosphorus at 10% CO₂ with 96.12%-99.61% removal rates. Nitrogen fixation amount achieved 41.86 mg L^-1 when the initial NH₄Cl concentration was set at 60 mg L^-1 at 10% CO₂. Improved total protein (25.01-365.49 mg) and amino acids (24.56-196.44 mg) contents of C. vulgaris biomass was observed with the increasing of added CO₂ and ammonium concentrations. Moreover, the developed kinetic function of C. vulgaris growth depends on both phosphorus quota and nitrogen quota with correlation coefficient (R²) ranged from 0.68 to 0.97. Computed maximal consumed nutrients concentrations (ΔC_max) based on Logistic function are positively related to initial NH₄⁺-N concentrations, which indicated that adding ammonium could stimulate the utilization of both phosphorus and nitrogen.

Experimental Study of Substrate Limitation and Light Acclimation in Cultures of the Microalgae Scenedesmus obliquus—Parameter Identification and Model Predictive Control

In this study, the parameters of a dynamic model of cultures of the microalgae Scenedesmus obliquus are estimated from datasets collected in batch photobioreactors operated with various initial conditions and light illumination conditions. Measurements of biomass, nitrogen quota, bulk substrate concentration, as well as chlorophyll concentration are achieved, which allow the determination of parameters with satisfactory confidence intervals and model cross-validation against independent data. The dynamic model is then used as a predictor in a nonlinear model predictive control strategy where the dilution rate and the incident light intensity are simultaneously manipulated in order to optimize the cumulated algal biomass production.

Experimental assessment and mathematical modelling of the growth of Chlorella vulgaris under photoautotrophic, heterotrophic and mixotrophic conditions

Microalgae show great potential for wastewater treatment and nutrient recovery. However, microalgae cultivation and harvesting are affected by the low biomass concentrations which are inherent to the photoautotrophic growth process. Mixotrophic growth can be a solution as it increases microalgae biomass concentration independently from the incident light intensity. In this work, a combined respirometric-titrimetric unit was used to assess the microalgae kinetics during such mixotrophic growth conditions for Chlorella vulgaris. Based on the experimental results, a microalgae model was extended in order to gain more insight in the delicate balance between photoautotrophic and heterotrophic growth. The results suggest that during heterotrophic growth with light in absence of external inorganic carbon sources (i.e. photoheterotrophic growth), all CO₂ produced by the heterotrophic pathway is internally recycled for photoautotrophic growth. Moreover, it was shown that photoautotrophic growth is the preferential growth mechanism under mixotrophic cultivation conditions (i.e. light + inorganic carbon + organic carbon), but that high oxygen concentrations activate the heterotrophic growth pathway to avoid photorespiration. The extended microalgae model supports these findings, with good model performance for all conducted experiments.

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

Microalgae are considered a promising feedstock for biorefineries given that their chemical composition - rich in carbohydrate and lipid - can be directed towards the co-production of various value-added fuels and chemicals. Production of microalgal biomass for biorefinery purposes requires the identification and establishment of optimal cultivation systems, a crucial yet complicated task due to the numerous factors (e.g. media composition, light, temperature) that simultaneously regulate biomass growth and intracellular composition. Modelling these biological processes, taking into account a single or multiple growth-limiting factors, offers a valuable tool to simulate, design and optimise the dynamics of microalgae cultivation. This review provides an overview of existing models developed to describe microalgal growth processes at the macroscopic scale (also termed black-box models) and discusses their formulation in detail. The black-box kinetic modelling frameworks are compiled into single-factor (6 formulations) and multiple-factor (32 formulations - further divided into non-interactive, additive, and interactive) growth kinetic models, as reported in more than 80 studies, for the prediction of biomass growth as a function of major operational factors such as media composition (e.g. nutrient concentration) and environmental factors (e.g. transient light and temperature). In addition, the review focuses on those models that further account for the production dynamics of two microalgal intracellular products with renowned potential as biorefinery substrates: carbohydrate and lipid molecules. Models of microalgal cultivation dynamics offer a robust engineering tool to understand the natural yet complex responses of microalgae to their growing environment and can help - if used appropriately - to optimise microalgae cultivation and increase the economic viability and sustainability of microalgal systems.

Recent developments in synthetic biology and metabolic engineering in microalgae towards biofuel production

In the wake of the uprising global energy crisis, microalgae have emerged as an alternate feedstock for biofuel production. In addition, microalgae bear immense potential as bio-cell factories in terms of producing key chemicals, recombinant proteins, enzymes, lipid, hydrogen and alcohol. Abstraction of such high-value products (algal biorefinery approach) facilitates to make microalgae-based renewable energy an economically viable option. Synthetic biology is an emerging field that harmoniously blends science and engineering to help design and construct novel biological systems, with an aim to achieve rationally formulated objectives. However, resources and tools used for such nuclear manipulation, construction of synthetic gene network and genome-scale reconstruction of microalgae are limited. Herein, we present recent developments in the upcoming field of microalgae employed as a model system for synthetic biology applications and highlight the importance of genome-scale reconstruction models and kinetic models, to maximize the metabolic output by understanding the intricacies of algal growth. This review also examines the role played by microalgae as biorefineries, microalgal culture conditions and various operating parameters that need to be optimized to yield biofuel that can be economically competitive with fossil fuels.

Biokinetic model-based multi-objective optimization of Dunaliella tertiolecta cultivation using elitist non-dominated sorting genetic algorithm with inheritance

Algal model based multi-objective optimization using elitist non-dominated sorting genetic algorithm with inheritance was carried out for batch cultivation of Dunaliella tertiolecta using NPK-fertilizer. Optimization problems involving two- and three-objective functions were solved simultaneously. The objective functions are: maximization of algae-biomass and lipid productivity with minimization of cultivation time and cost. Time variant light intensity and temperature including NPK-fertilizer, NaCl and NaHCO₃ loadings are the important decision variables. Algal model involving Monod/Andrews adsorption kinetics and Droop model with internal nutrient cell quota was used for optimization studies. Sets of non-dominated (equally good) Pareto optimal solutions were obtained for the problems studied. It was observed that time variant optimal light intensity and temperature trajectories, including optimum NPK fertilizer, NaCl and NaHCO₃ concentration has significant influence to improve biomass and lipid productivity under minimum cultivation time and cost. Proposed optimization studies may be helpful to implement the control strategy in scale-up operation.