Optimization of microalgal productivity using an adaptive, non-linear model based strategy

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.

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.

Optimal bioprocess design through a gene regulatory network - Growth kinetic hybrid model: Towards replacing Monod kinetics

Currently, design and optimisation of biotechnological bioprocesses is performed either through exhaustive experimentation and/or with the use of empirical, unstructured growth kinetics models. Whereas, elaborate systems biology approaches have been recently explored, mixed-substrate utilisation is predominantly ignored despite its significance in enhancing bioprocess performance. Herein, bioprocess optimisation for an industrially-relevant bioremediation process involving a mixture of highly toxic substrates, m-xylene and toluene, was achieved through application of a novel experimental-modelling gene regulatory network - growth kinetic (GRN-GK) hybrid framework. The GRN model described the TOL and ortho-cleavage pathways in Pseudomonas putida mt-2 and captured the transcriptional kinetics expression patterns of the promoters. The GRN model informed the formulation of the growth kinetics model replacing the empirical and unstructured Monod kinetics. The GRN-GK framework's predictive capability and potential as a systematic optimal bioprocess design tool, was demonstrated by effectively predicting bioprocess performance, which was in agreement with experimental values, when compared to four commonly used models that deviated significantly from the experimental values. Significantly, a fed-batch biodegradation process was designed and optimised through the model-based control of TOL Pr promoter expression resulting in 61% and 60% enhanced pollutant removal and biomass formation, respectively, compared to the batch process. This provides strong evidence of model-based bioprocess optimisation at the gene level, rendering the GRN-GK framework as a novel and applicable approach to optimal bioprocess design. Finally, model analysis using global sensitivity analysis (GSA) suggests an alternative, systematic approach for model-driven strain modification for synthetic biology and metabolic engineering applications.

Optimization of CO₂ fixation by Chlorella kessleri cultivated in a closed raceway photo-bioreactor

The aim of this study is to optimize biological fixation of CO2 using Chlorella kessleri cultivated in oil sands process water (OSPW). A lab-scale closed raceway photobioreactor was designed and assembled for cultivation of C. kessleri in OSPW. A fed-batch model describing the dynamics of microalgae growth and CO2, phosphate and ammonium uptake rate was developed based on batch kinetics identified in our previous study, and was successfully validated against experimental data. A model-based optimization method was used to calculate the optimal feeding strategies for CO2, phosphate and light intensity which resulted in a 1.5-fold increase in the final biomass concentration and a 2-fold increase in the average CO2 uptake rate in 240 h (10 days) compared to the initial fed-batch experiment over 432 h (18 days). Finally, scale-up to large-scale continuous operation was considered, and the optimal hydraulic retention time (HRT) and feeding strategy for maximum productivity were estimated.

A simple and rapid protocol for measuring neutral lipids in algal cells using fluorescence

Algae are considered excellent candidates for renewable fuel sources due to their natural lipid storage capabilities. Robust monitoring of algal fermentation processes and screening for new oil-rich strains requires a fast and reliable protocol for determination of intracellular lipid content. Current practices rely largely on gravimetric methods to determine oil content, techniques developed decades ago that are time consuming and require large sample volumes. In this paper, Nile Red, a fluorescent dye that has been used to identify the presence of lipid bodies in numerous types of organisms, is incorporated into a simple, fast, and reliable protocol for measuring the neutral lipid content of Auxenochlorella protothecoides, a green alga. The method uses ethanol, a relatively mild solvent, to permeabilize the cell membrane before staining and a 96 well micro-plate to increase sample capacity during fluorescence intensity measurements. It has been designed with the specific application of monitoring bioprocess performance. Previously dried samples or live samples from a growing culture can be used in the assay.