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Waller, Peter

Publications and source records attributed to Waller, Peter.

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Association between algal productivity and phycosphere composition in an outdoor Chlorella sorokiniana reactor based on multiple longitudinal analyses

Summary Microalgae as a biofuel source are of great interest. Bacterial phycosphere inhabitants of algal cultures are hypothesized to contribute to productivity. In this study, the bacterial composition of the Chlorella sorokiniana phycosphere was determined over several production cycles in different growing seasons by 16S rRNA gene sequencing and identification. The diversity of the phycosphere increased with time during each individual reactor run, based on Faith’s phylogenetic diversity metric versus days post‐inoculation ( R = 0.66, P < 0.001). During summer months, Vampirovibrio chlorellavorus , an obligate predatory bacterium, was prevalent. Bacterial sequences assigned to the Rhizobiales, Betaproteobacteriales and Chitinophagales were positively associated with algal biomass productivity. Applications of the general biocide, benzalkonium chloride, to a subset of experiments intended to abate V. chlorellavorus appeared to temporarily suppress phycosphere bacterial growth, however, there was no relationship between those bacterial taxa suppressed by benzalkonium chloride and their association with algal productivity, based on multinomial model correlations. Algal health was approximated using a model‐based metric, or the ‘Health Index’ that indicated a robust, positive relationship between C. sorokiniana fitness and presence of members belonging to the Burholderiaceae and Allorhizobium–Neorhizobium–Pararhizobium–Rhizobium clade. Bacterial community composition was linked to the efficiency of microalgal biomass production and algal health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Simulation of shading and algal growth in experimental raceways

Many algae research groups use elevated experimental raceways to characterize algal biomass productivity. Side walls and a relatively large paddlewheel shade the culture and lower productivity, particularly in winter and with low culture depth. This paper presents a methodology to calculate shading for a given raceway shape: (1) develop a mesh of the inner surface geometry of the raceway; (2) offset the mesh horizontally in the x and y directions on the water surface as a function of wall height, solar zenith angle, and solar azimuth angle; (3) calculate the shaded area for each projected mesh with the shoelace algorithm; (4) use regression to develop a response surface for shaded area as a function of horizontal x and y offset. For each time step, the shaded area is calculated with the regression equation based on the x and y offset at that time step. The shading model was added to the Huesemann Algae Biomass Growth (HABG) model, which predicts algae growth rate as a function of water temperature and light intensity in algae culture layers. The shading model was evaluated with biomass concentrations from three Regional Algal Feedstock Testbed (RAFT) experiments with three different species of algae. The shading algorithm reduced the HABG estimate of productivity and improved the agreement with observed productivities. In the model, productivity was sensitive to the assumed light distribution below the water surface. Averaging light in each layer resulted in higher calculated growth rate than calculating growth separately in shaded and unshaded areas. Shaded area was sensitive to raceway orientation, water depth, and time of year. A north-south raceway orientation had less average shading in winter but more average shading in summer than an east-west orientation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Application of deoxygenation-aeration cycling to control the predatory bacterium Vampirovibrio chlorellavorus in Chlorella sorokiniana cultures

A previously untested approach was evaluated to enable management of the predatory bacterium, Vampirovibrio chlorellavorus, a pathogen of Chlorella sorokiniana, in suspension cultures grown in a laboratory test reactor. Because V. chlorellavorus is an obligate aerobic bacterium, whereas C. sorokiniana grows under aerobic and anaerobic conditions, deoxygenation of the culture was expected to be detrimental to the pathogen, but not to the algal host. The effect of deoxygenation on the uninfected (healthy) C. sorokiniana suspension cells, compared to the C. sorokiniana-V. chlorellavorus co-culture, was studied in relation to biomass, dissolved oxygen, ratio of C. sorokiniana to V. chlorellavorus DNA, and visual and light microscopic observations. Preliminary experiments were conducted to test the effects of different deoxygenation-aeration cycling regimes on performance of V. chlorellavorus-free C. sorokiniana cultures. To an aerobic culture, pure nitrogen gas was introduced to create anoxic conditions, followed by the injection of ambient air to re-establish an aerobic environment. Under this repeated cycling regime, C. sorokiniana was shown to tolerate the anoxic conditions for extended timespans that ranged from 2 to 8 h over a 5-day test period. The analogous aerobic-anoxic cycling with the C. sorokiniana-V. chlorellavorus co-cultures resulted in ‘near-normal’ growth cycle and harvestable biomass, whereas the continuously-aerated (aerobic) co-cultures that were grown without the deoxygenation step in the cycle collapsed in 3 days. Visual and light microscopic observations revealed intact C. sorokiniana cells were present in the deoxygenated cultures, compared to the aerobically-grown, brown-colored algal cultures consisting of collapsed cells. Quantitative polymerase chain reaction analysis showed continuous increases in the ratio of V. chlorellavorus (16S rDNA) to C. sorokiniana (18S rDNA) DNA in the aerated co-cultures, with greater increases during dark periods, while the pathogen-to-host DNA ratio in the deoxygenated co-cultures was relatively low and algal cells did not collapse, as would be expected following pathogen attack.

Algae, Anoxic, Bomass, Infection, Nitrogen, Quanti↗
DOE OSTI2019

Model of temperature, evaporation, and productivity in elevated experimental algae raceways and comparison with commercial raceways

Elevated Experimental Raceways (EERs) have been used at many research sites in the United States to evaluate algal biomass productivity. This paper develops temperature and evaporation models for EERs and then evaluates whether the temperature profile and resultant productivity in an EER is representative of large-scale commercial raceways. Open water surface evaporation and temperature models with shading algorithms were programmed in Python and calibrated with temperature and evaporation data from the EERs in the Regional Algal Feedstock Testbed (RAFT) experiments at the University of Arizona. The final calibrated model for EERs was named the EERTEM (Elevated Experimental Raceway Temperature and Evaporation Model). The energy balance algorithms in the Biomass Assessment Tool (BAT model) were also added to the Python code and used to develop temperature simulations of standard commercial paddlewheel raceways. A comparison of BAT and EERTEM simulations indicated that EER temperature fluctuations are not representative of in ground commercial paddlewheel raceways, primarily due to the buffering effect of soil heat flux in commercial raceways. With the different temperature profiles, biomass productivities of three algae species in the EERs were compared to productivities in commercial raceways. Differences in productivity between EERTEM and BAT model temperature profiles were observed when the maximum daytime temperature of one raceway was in the optimal growth range but the temperature of the other raceway exceeded or was below the optimal growth range.

Khawam, George↗
DOE OSTI2019

Regional Algal Feedstock Testebed (RAFT) Final Report

The RAFT project completed detailed characterization of 12 twelve potential production strains and extensively tested 3 the three best strains in long term seasonal cultivation trials. A total of x 272 cultivation experiments were completed at the three outdoor testbed locations as part of long term cultivation trials conducted over a three year period. The cultivation experiments tested various harvest strategies, crop protection strategies, and seasonal effects along with strain rotation strategies. A sensor, that continuously monitors cell density, was developed and a patent application was submitted. Models were develop and validated for predicting strain performance, determining biomass productivity, evaluating cultivation system design and operations, and evaluating large scale production scenarios. A detailed data base was established for the long term cultivation trials with discrete data from the x 272 testbed cultivation experiments. This data base will be maintained at the University of Arizona https://raft.arizona.edu/cultivation-data/ for public access and continued academic evaluations.

09 BIOMASS FUELS↗
DOE OSTI2018

Incorporation of salinity, nitrogen, and shading stress factors into the Huesemann Algae Biomass Growth model

The Huesemann Algae Biomass Growth (HABG) model predicts microalgae biomass growth in outdoor raceways as a function of water temperature and incident light intensity. The model over-predicted growth in several Regional Algal Feedstock Testbed (RAFT) experiments. Inspection of the culture conditions suggested that salinity stress, nutrient limitation, and shading reduced growth, which were not included in the original model. Laboratory experiments were carried out to measure growth response of S. obliquus to high salinity and limited nitrogen. In addition, the shading effect was estimated based on raceway geometry, solar angle, albedo and measured light intensity data. In this study, the improvement of model performance is presented step by step as each growth limiting factor is incorporated into the model. The new salinity, shanding, and nutrient stress factors remove most of the over-prediction of the original model. Comparison of the model with observed data indicated that ignoring the lag phase also resulted in overprediction. Delaying the start of the simulation until the end of the lag phase and inclusion of stress factors lowered model inaccuracy from 90% over-prediction to within ±10% in six out of seven batches.

09 BIOMASS FUELS↗
DOE OSTI2016

Estimating the Maximum Achievable Productivity in Outdoor Ponds: Microalgae Biomass Growth Modeling and Climate Simulated Culturing

To address the challenge of determining the maximum achievable monthly, seasonal, and annual biomass productivity for selected promising strains in outdoor ponds at any geographic location of interest, we developed the following four step integrated strategy. First, the strain is characterized in terms of its specific growth rate response to temperature and light, including darkness, as well as its light attenuation characteristics. Second, these species-specific parameters serve as inputs to PNNL’s biomass growth model that, in conjunction with the sunlight and pond water temperature predictions provided by PNNL’s Biomass Assessment Tool, is used to predict the respective biomass productivities for tens of thousands of hypothetical pond locations in the United States. Third, monthly and annual biomass productivity maps are created to identify the geographic location(s) of optimum biomass productivity and to generate light and water temperature “scripts” (time series) for hypothetical ponds at the best site(s). Finally, the maximum achievable biomass productivity for the given strain is confirmed by conducting culture experiments in PNNL’s indoor LED-lighted and temperature-controlled ponds under climate-simulated conditions, using the previously determined light and temperature scripts. This integrated strategy provides an efficient and low risk approach to screen promising strains for their potential to exhibit high biomass productivities, and to validate their model-predicted performance in climate-simulation ponds before transitioning to cultivation in outdoor raceways.

biomass assessment tool↗
DOE OSTI2014

ARID Raceway Temperature Model Evaluation

The ARID (Algae Raceway Integrated Design) raceway temperature model, written in Visual Basic for Applications, calculates algae culture temperature in ARID and conventional raceways as a function of meteorological data from agricultural weather stations. The model is an atmospheric boundary layer model and uses energy balance terms from the Penman-Monteith equation. The original model used evapotranspiration, as calculated by the weather station, and assumed an average daily albedo. In this research, equations for albedo and for direct calculation of evaporation from atmospheric parameters were added to the ARID model. Albedo was calculated with the Fresnel equations. The Penman-Monteith model was adjusted for algae raceway evaporation rather than reference evapotranspiration. The new model components were calibrated with data from a 21-day algae growth experiment in the ARID raceway during winter 2011. The model was also adapted to model variable circulation times and heat transfer through a canal cover. In summary, this research improved and calibrated the ARID temperature model for future use.

09 BIOMASS FUELS↗