Optimizing microalgal production in raceway systems

The paradoxes hidden behind the Droop model highlighted by a metabolic approach

We propose metabolic models for the haptophyte microalgae Tisochrysis lutea with different possible organic carbon excretion mechanisms. These models-based on the DRUM (Dynamic Reduction of Unbalanced Metabolism) methodology-are calibrated with an experiment of nitrogen starvation under day/night cycles, and then validated with nitrogen-limited chemostat culture under continuous light. We show that models including exopolysaccharide excretion offer a better prediction capability. It also gives an alternative mechanistic interpretation to the Droop model for nitrogen limitation, which can be understood as an accumulation of carbon storage during nitrogen stress, rather than the common belief of a nitrogen pool driving growth. Excretion of organic carbon limits its accumulation, which leads to a maximal C/N ratio (corresponding to the minimum Droop N/C quota). Although others phenomena-including metabolic regulations and dissipation of energy-are possibly at stake, excretion appears as a key component in our metabolic model, that we propose to include in the Droop model.

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.

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.

Microalgae-bacteria consortia in high-rate ponds for treating urban wastewater: Elucidating the key state indicators under dynamic conditions

On-line performance indicators of a microalgae-bacteria consortium were screened out from different variables based on pH and dissolved oxygen on-line measurements via multivariate projection analysis, aiming at finding on-line key state indicators to easily monitor the process. To fulfil this objective, a pilot-scale high-rate pond for urban wastewater treatment was evaluated under highly variable conditions, i.e. during the start-up period. The system was started-up without seed of either bacterial or microalgal biomass. It took around 19 days to fully develop a microalgal community assimilating nutrients significantly. Slight increases in the biomass productivities in days 26-30 suggest that the minimum time for establishing a performant bacteria-microalgae consortium could be of around one month for non-inoculated systems. At this point, the process was fully functional, meeting the European discharge limits for protected areas. The results of the statistical analyses show that both the pH and the dissolved oxygen concentration represent accurately the biochemical processes taking place under the start-up of the process. Both pH and dissolved oxygen represented accurately also the performance of the high-rate algal pond, being affordable, easily-implemented, options for monitoring, control and optimization of industrial-scale processes.

Development of a Novel Kinetic Model for Cocoa Fermentation Applying the Evolutionary Optimization Approach

Traditional Mexican cocoa fermentation performed in batch was studied by applying kinetic modelling with experimental validation. Similar microbiological behaviour was observed up to 60 h, with a temperature increase at 72 h that remained constant (50 °C) until 156 h. Metabolite-production kinetics (ethanol and acetic acid) from degradable mucilage (glucose) was explored. Exploration involved applying different combinations of unstructured growth models, in order to consider the effect of temperature when predicting the concentration of metabolites in these microorganisms. Two methods were used to optimize model parameters: the Levenberg–Marquardt optimization approach and Genetic Algorithms (GAs). GAs which could be used to scale up the fermentation process indicated the applicability of this model for predicting fermentation quality. The maximum specific rate average for μmax and saturation constant (Ks) were 0.0961 h−1 and 1.4 mg/g m.s., respectively. The results obtained indicate the expediency of this technique for future application in the design and control of batch fermentation.

Optimal control for micro-algae on a raceway model

We apply numerical optimal control methods to an existing algae growth model with the aim to determine the best performance of the model under known conditions using a variety of decision variables. We transform the system of differential algebraic equations in the existing model to a system of ordinary differential equations which introduces dynamics for average light intensity and chlorophyll. In addition, we allow for variable nitrogen concentration of the inflow as well as variable initial nitrogen concentration of the raceway. Our main focus is on optimizing of the production of lipids. We calculate both open and closed loop optimal controllers and test their robustness. Finally, we also consider raceway depth as a decision variable. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:107-119, 2018.

Maximizing Productivity and Reducing Environmental Impacts of Full-Scale Algal Production through Optimization of Open Pond Depth and Hydraulic Retention Time

The ability to dynamically control algal raceway ponds to maximize biomass productivity and reduce environmental impacts (e.g., land and water use) with consideration of local constraints (e.g., water availability and climatic conditions) is an important consideration in algal biotechnology. This paper presents a novel optimization strategy that seeks to maximize growth (i.e., optimize land use), minimize respiration losses, and minimize water demand through regular adjustment of pond depth and hydraulic retention time (HRT) in response to seasonal changes. To evaluate the efficiency of this strategy, algal productivity and water demand were simulated in five different climatic regions. In comparison to the standard approach (constant and location-independent depth and HRT), dynamic control of depth and HRT was shown to increase productivity by 0.6-9.9% while decreasing water demand by 10-61% depending upon the location considered (corresponding to a decrease in the water footprint of 19-62%). Interestingly, when the fact that the water demand was limited to twice the local annual rainfall was added as a constraint, higher net productivities were predicted in temperate and tropical climates (15.7 and 16.7 g m(-2) day(-1), respectively) than in Mediterranean and subtropical climates (13.0 and 9.7 g m(-2) day(-1), respectively), while algal cultivation was not economically feasible in arid climates. Using dynamic control for a full-scale operation by adjusting for local climatic conditions and water constraints can notably affect algal productivity. It is clear that future assessments of algal cultivation feasibility should implement locally optimized dynamic process control.

Full-scale validation of a model of algal productivity

While modeling algal productivity outdoors is crucial to assess the economic and environmental performance of full-scale cultivation, most of the models hitherto developed for this purpose have not been validated under fully relevant conditions, especially with regard to temperature variations. The objective of this study was to independently validate a model of algal biomass productivity accounting for both light and temperature and constructed using parameters experimentally derived using short-term indoor experiments. To do this, the accuracy of a model developed for Chlorella vulgaris was assessed against data collected from photobioreactors operated outdoor (New Zealand) over different seasons, years, and operating conditions (temperature-control/no temperature-control, batch, and fed-batch regimes). The model accurately predicted experimental productivities under all conditions tested, yielding an overall accuracy of ±8.4% over 148 days of cultivation. For the purpose of assessing the feasibility of full-scale algal cultivation, the use of the productivity model was therefore shown to markedly reduce uncertainty in cost of biofuel production while also eliminating uncertainties in water demand, a critical element of environmental impact assessments. Simulations at five climatic locations demonstrated that temperature-control in outdoor photobioreactors would require tremendous amounts of energy without considerable increase of algal biomass. Prior assessments neglecting the impact of temperature variations on algal productivity in photobioreactors may therefore be erroneous.