Jessica L. Drewry

Analysis and modeling of Nannochloropsis growth in lab, greenhouse, and raceway experiments

Efficient production of algal biofuels could reduce dependence on foreign oil by providing a domestic renewable energy source. Moreover, algae-based biofuels are attractive for their large oil yield potential despite decreased land use and natural resource (e.g., water and nutrients) requirements compared to terrestrial energy crops. Important factors controlling algal lipid productivity include temperature, nutrient availability, salinity, pH, and the light-to-biomass conversion rate. Computational approaches allow for inexpensive predictions of algae growth kinetics for various bioreactor sizes and geometries without the need for multiple, expensive measurement systems. Parametric studies of algal species include serial experiments that use off-line monitoring of growth and lipid levels. Such approaches are time consuming and usually incomplete, and studies on the effect of the interaction between various parameters on algal growth are currently lacking. However, these are the necessary precursors for computational models, which currently lack the data necessary to accurately simulate and predict algae growth. In this work, we conduct a lab-scale parametric study of the marine alga Nannochloropsis salina and apply the findings to our physics-based computational algae growth model. We then compare results from the model with experiments conducted in a greenhouse tank and an outdoor, open-channel raceway pond. Results show that the computational model effectively predicts algae growth in systems across varying scale and identifies the causes for reductions in algal productivities. Applying the model facilitates optimization of pond designs and improvements in strain selection.

A Computational Fluid Dynamics Model of Algal Growth in Open Raceways

Biofuels derived from algae are becoming an increasingly viable alternative to petroleum based fuels; however, research and development in the field must continue before biofuels can be produced in an economical and environmentally friendly manner. Unlike with photobioreactors, there is no generally accepted model for evaluating the growth of algae in open raceways because algal growth involves a large number of variables. Algal growth is directly dependent on the momentum, mass and heat transfer within the raceway as well as climatic variables; therefore, to achieve the most accurate model of growth possible, it is essential to implement a growth model within computational fluid dynamics (CFD) software. The aim of this study, then, is to evaluate and optimize raceway operations by accurately modeling the algae growth through user defined functions embedded within a CFD model. This comprehensive model of algal growth is necessary because the system can only be optimized by identifying the parameters that lead to maximum growth with minimal inputs.

Computational model of methane and ammonia emissions from dairy barns: Development and validation

Abstract The increased global demand for milk and other dairy products over the past decades is a cause for concern due to the potential for environmental impact. Ammonia produced by housed dairy cows can contribute to the formation of particulate matter and nitrous oxide which both contribute to the greenhouse effect. The methane produced by these cows also contributes to the greenhouse effect. Scientists and engineers face the challenge of developing methods to reduce the environmental impact of dairy production while not inhibiting the ability of producers to keep up with demand. Emission of methane and ammonia are highly dependent on feed composition, barn design and operation, manure management making this a challenging topic to study experimentally. Using computational models to simulate the generation and dispersion of gaseous species within dairy housing can facilitate the exploration of cost-effective gas mitigation strategies. Thus a steady-state computational fluid dynamics (CFD) model capable of simulating biologically based generation of methane, ammonia, and heat and their transport within the domain was developed and validated. The effect of buoyancy forces on the accuracy and stability of the solutions was explored. The model was validated with experimental data collected from emission chambers located at USDA-ARS Dairy Forage Research Center in Wisconsin, USA. Concentration of ammonia and methane, due to controlled injections from cylinders and biological generations from a dairy cow, were measured in the chambers using a FTIR gas analyzer. Results of the validated CFD model could be used to predict gaseous emissions under a range of environmental, design, and experimental treatment parameters.

Formation of Algae Growth Constitutive Relations for Improved Algae Modeling

This SAND report summarizes research conducted as a part of a two year Laboratory Directed Research and Development (LDRD) project to improve our abilities to model algal cultivation. Algae-based biofuels have generated much excitement due to their potentially large oil yield from relatively small land use and without interfering with the food or water supply. Algae mitigate atmospheric CO2 through metabolism. Efficient production of algal biofuels could reduce dependence on foreign oil by providing a domestic renewable energy source. Important factors controlling algal productivity include temperature, nutrient concentrations, salinity, pH, and the light-to-biomass conversion rate. Computational models allow for inexpensive predictions of algae growth kinetics in these non-ideal conditions for various bioreactor sizes and geometries without the need for multiple expensive measurement setups. However, these models need to be calibrated for each algal strain. In this work, we conduct a parametric study of key marine algae strains and apply the findings to a computational model.