Sustainability tradeoffs within photoautotrophic cultivation systems: integrating physical and lifecycle modeling for design and optimization

2018 Summer.Includes bibliographical references.Photoautotroph-based biofuels are considered one of the most promising renewable resources to meet the global energy requirements for transportation systems. Long-term research and development has resulted in demonstrations of microalgae areal oil productivities that are higher than crop-based biofuels, about 10 times that of palm oil and about 130 times that of soybean. Cyanobacteria is reported to have ~4 times the areal productivity of microalgae on an equivalent energy basis. Downstream of this cultivation process, the cyanobacteria biomass and bioproducts can be supplied to biorefineries producing feed, biomaterials, biosynthetic chemicals, and biofuels. As such, cyanobacteria, and microalgae-based systems can be a significant contributor to more sustainable energy and production systems. This research presents novel means to be able to analyze, integrate, assess, and design sustainable photoautotrophic biofuel and bioproduct systems, as defined using lifecycle assessment methods (LCA). As part of a broad collaboration between industry, academia, and the national laboratories, I have developed models and experiments to quantify tradeoffs among the scalability, sustainability, and technical feasibility of cyanobacteria biorefineries and microalgae cultivation systems. A central hypothesis to this research is that the lifecycle energy costs and benefits, the cultivation productivity, and the scalability of any given organism or technology is governed by the fluid mechanics of the photobioreactor systems. The fluid characteristics of both open raceway ponds and flat photobioreactors, are characterized through industrial-scale experiment and modeling. Turbulent mixing is studied by applying Acoustic Doppler Velocimetry (ADV), Particle Image Velocimetry (PIV), and computational fluid dynamics (CFD) characterization tools. The implications of these fluid conditions on photoautotrophic organisms are studied through cultivation and modeling of the cyanobacteria, Synechocystis sp. PCC6803. Growth-stage models of this cyanobacteria include functions dependent on incident radiation, temperature, nutrient availability, dark and photo-respiration. By developing an integrated approach to laboratory experimentation and industrial-scale growth experiments, we have validated models to quantify the scalability and sustainability of these novel biosystems. These capabilities are utilized to perform long-term and industrially-relevant assessments of the costs and benefits of these promising technologies, and will serve to inform the biological engineering research and development of new organisms

A 2D model for hydrodynamics and biology coupling applied to algae growth simulations

Cultivating oleaginous microalgae in specific culturing devices such as raceways is seen as a future way to produce biofuel. The complexity of this process coupling non linear biological activity to hydrodynamics makes the optimization problem very delicate. The large amount of parameters to be taken into account paves the way for a useful mathematical modeling. Due to the heterogeneity of raceways along the depth dimension regarding temperature, light intensity or nutrients availability, we adopt a multilayer approach for hydrodynamics and biology. For free surface hydrodynamics, we use a multilayer Saint-Venant model that allows mass exchanges, forced by a simplified representation of the paddlewheel. Then, starting from an improved Droop model that includes light effect on algae growth, we derive a similar multilayer system for the biological part. A kinetic interpretation of the whole system results in an efficient numerical scheme. We show through numerical simulations in two dimensions that our approach is capable of discriminating between situations of mixed water or calm and heterogeneous pond. Moreover, we exhibit that a posteriori treatment of our velocity fields can provide lagrangian trajectories which are of great interest to assess the actual light pattern perceived by the algal cells and therefore understand its impact on the photosynthesis process.Comment: 27 pages, 11 figure

Advanced CFD model of multiphase photobioreactors for microalgal derived biomass production

Development of more efficient algal photobioreactors (PBRs) is driven by increasing interest in algaculture for the production of fuels, chemicals, food, animal feed, and medicine, as well as carbon capture. While at present, the cost and microalgae production capacity are one of its restrictions when competition with other biodiesel feedstock. The objective of the present work is to develop and validate better computational models to investigate the interplay between fluid hydrodynamics, radiation transport and algae growth, which is crucial to determine the performance and scalability of algae photobioreactors. First, a detailed review of the pertinent information required for developing a comprehensive computation model for photobioreactors was conducted. The current status of the submodels, including hydrodynamics and mass transfer multiphase CFD models, radiation transport models, microalgae growth rate models, and coupling method for developing a comprehensive model for PBRs was outlined. Second, an Eulerian two-fluid model for gas-liquid Taylor-Couette flow was proposed and validated. The CFD was based on the RANS approach with constitutive closures for interphase forces and liquid turbulence. The model was validated by comparison with previously published experimental data. The mechanism of bubble radial non-uniformity distribution was discussed and the relative importance of various interphase forces was demonstrated. Third, the validated two fluid CFD model was employed to simulate the local values of the mass transfer coefficient based on the Higbie theory. A novel approach was proposed to estimate the mass transfer exposure time. This approach automatically selects the appropriate expression (either the penetration model or eddy cell model) based on local flow conditions. The simulation predictions agree well with experimental foundlings, which demonstrates that the adaptive mass transfer model has the ability to correctly description of both local and global mass transfer of oxygen in a semi-batch gas–liquid Taylor–Couette reactor. Forth, microalgae culture experiment was conducted to identify the limiting factor in the Taylor-Couette photobioreactor. The characteristic time scales for mixing, mass transfer and biomass growth was compared. It is found that algal growth rate in Taylor vortex reactors is not limited by fluid mixing or interphase mass transfer, and therefore the observed biomass productivity improvements are likely attributable to improved light utilization efficiency (high-frequency light/dark cycles). Fifth, a commonly used Lagrangian strategy for coupling the various factors influencing algal growth was employed whereby results from computational fluid dynamics and radiation transport simulations were used to compute numerous microorganism light exposure histories, and this information, in turn, was used to estimate the global biomass specific growth rate. The simulation predictions were compared with experimental measurements and the origin of weaknesses of the commonly used Lagrangian approach model was traced. Sixth, an alternative Eulerian computational approach for predicting photobioreactor performance was proposed, wherein a transport equation for algal growth kinetics is solved, thereby obviating the need to carry out thousands of particle tracking simulations. The simulation predictions were compared with experimental measurements and commonly used Lagrangian approach model

Municipal wastewater treatment with pond technology : historical review and future outlook

Facing an unprecedented population growth, it is difficult to overstress the assets for wastewater treatment of waste stabilization ponds (WSPs), i.e. high removal efficiency, simplicity, and low cost, which have been recognized by numerous scientists and operators. However, stricter discharge standards, changes in wastewater compounds, high emissions of greenhouse gases, and elevated land prices have led to their replacements in many places. This review aims at delivering a comprehensive overview of the historical development and current state of WSPs, and providing further insights to deal with their limitations in the future. The 21st century is witnessing changes in the way of approaching conventional problems in pond technology, in which WSPs should no longer be considered as a low treatment technology. Advanced models and technologies have been integrated for better design, control, and management. The roles of algae, which have been crucial as solar-powered aeration, will continue being a key solution. Yet, the separation of suspended algae to avoid deterioration of the effluent remains a major challenge in WSPs while in the case of high algal rate pond, further research is needed to maximize algal growth yield, select proper strains, and optimize harvesting methods to put algal biomass production in practice. Significant gaps need to be filled in understanding mechanisms of greenhouse gas emission, climate change mitigation, pond ecosystem services, and the fate and toxicity of emerging contaminants. From these insights, adaptation strategies are developed to deal with new opportunities and future challenges

Toward Carbon Neutrality: The Modeling and Implementation of an Algal Carbon Capture System

Carbon dioxide (CO2) emissions from anthropogenic sources are causing widespread ecological disruptions. The uptake of CO2 by aquatic photoautotrophs is one strategy for carbon capture to mitigate these emissions. The objectives of this thesis were to investigate carbonate chemistry and algal growth equations to improve MATLAB model predictive capability in a closed-reactor system and to develop, design, and evaluate a non-fossil fuel technology and strategy for operation of the Algal Carbon Capture System (ACCS). A dynamic growth model based on carbon-limited algal specific growth rate with Monod kinetics, considering CO2, bicarbonate (HCO3), and carbonate (CO32-) as substitutable substrates, provided the best estimates for algal biomass in closed-reactors. Total inorganic carbon (TIC), CO2, HCO3-, CO32-, pH, and alkalinity were also well predicted. This model improves upon those reviewed by incorporating kinetic rates of inorganic carbon species interconversion instead of the equilibrium assumption. Discrepancies in rate constants of the bicarbonate hydroxylation reaction indicate more exploration of these parameters is needed. Here is proposed the use of the geometric mean (2.25 108 M-1∙s-1) for the forward rate constant. Underprediction of algal biomass and improved response of CO2/HCO3-/CO32- substitutable model over the CO2/HCO3- substitutable may indicate an unknown biological pathway for the use of carbonate for growth. An airlift pump prototype was designed, built, implemented, and tested at the ACCS to create water flow in one raceway channel as a demonstration of the concept. The airlift operates solely on available solar power and provides at its outlet a water velocity of 12.5 cm/s, and an average channel velocity of 1.02 ± 0.15 cm/s as the surface kinetic energy is distributed throughout the channel depth

Deep learning based surrogate modeling and optimization for Microalgal biofuel production and photobioreactor design

Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modelling. However, this approach presents computational intractability and numerical instabilities when simulating large-scale systems, causing time-intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data-driven surrogate modelling framework which considerably reduces computing time from months to days by exploiting state-of-the-art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi-objective optimization was incorporated to generate a Pareto-frontier for decision-making, advancing its applications in complex biosystems modelling and optimization

Cultivating Spirulina maxima: Innovative Approaches

This chapter reports an annual production of Spirulina (Arthrospira) maxima in Ansan, South Korea (37.287°N, 126.833°E) with temperate four seasons climate for testing industrial application. Construction on pilot plant of semi-open raceway system (ORS) with each 20 ton culture volume has been established in early 2011 based on building information modeling (BIM). An optimized design of pilot culture system for microalgae scale-up culture in temperate area and details of culture was presented. In scale-up trials using two ORSs, the strain displayed satisfactory annual growth under batch condition. In an annual trial, average biomass concentration was recorded at 0.99 ± 0.16 g/L, which showed stable productivity in a year. Maximum concentration was estimated at 1.418 ± 0.09 g/L in August, while minimum production was estimated at 0.597 ± 0.05 g/L in October. Despite insufficient solar radiation and nutrients, ORS was favorable for S. maxima production. The technical strategies contribute to the annual production of S. maxima in this region: controlling the culture temperature, reducing production cost, and retrospective climatic data-based BIM construction of the greenhouse. Consequently, pilot production of S. maxima was feasible in Korean climates, a region previously thought to be outside its geographic limits