Pilot-scale open-channel raceways and flat-panel photobioreactors maintain well-mixed conditions under a wide range of mixing energy inputs.

Turbulent mixing in pilot-scale cultivation systems influences the productivity of photoautotrophic cultures. We studied turbulent mixing by applying particle image velocimetry and acoustic doppler velocimetry to pilot-scale, flat-panel photobioreactor, and open-channel raceway. Mixing energy inputs were varied from 0.1 to 2.1 W·m-3 . The experimental results were used to quantify turbulence and to validate computational fluid dynamics models, from which Lagrangian representations of the fluid motion in these reactors were derived. The results of this investigation demonstrated that differences in mixing energy input do not significantly impact the structure of turbulence and the light/dark cycling frequencies experienced by photoautotrophic cells within the reactors. The experimental and computational results of our research demonstrated that well-mixed conditions exist in pilot-scale, flat-panel photobioreactors and open-channel raceways, even for relatively low mixing energy inputs.

Probabilistic Lifecycle Assessment of Butanol Production from Corn Stover Using Different Pretreatment Methods.

The recalcitrant nature of lignocelluloses requires a pretreatment process before the fermentative butanol production. The commonly used pretreatment processes, such as steam explosion, sulfuric acid, ammonia fiber explosion, ionic liquid (IL), and biological, require different quantities and types of process chemicals, and produce different quality and quantity of fermentable sugars. This study determines life-cycle greenhouse gas emissions (GHG) these pretreatment methods by developing a system-level process model including corn stover feedstock supply system and the downstream butanol production process. This study further evaluates the uncertainty associated with energy use and GHG emissions for each stage of the entire butanol production chain and provide the future optimization opportunities. Probabilistic results of these analyses describe a distribution of GHG emissions with an average of 18.09-1056.12 gCO2e/MJ and a 95% certainty to be less than 33.3-1888.3 gCO2e /MJ. The highest GHG emissions of IL-pretreatment of 1056.12 gCO2e/MJ reaches to 89.8 gCO2e/MJ by switching IL-recovery from 80 to 99 wt %, which is the most influential parameter for IL-pretreatment. Additionally, credits from excess electricity, butanol yield, nitrogen replacement, and diesel fuel for transportation and harvesting were the most influential parameters. Based on the current state of technologies, apart from ionic liquid and biological pretreatments, other pretreatment processes have similar metrics of sustainability.

Techno-economic assessment for the production of algal fuels and value-added products: opportunities for high-protein microalgae conversion.

BACKGROUND: Microalgae possess numerous advantages for use as a feedstock in producing renewable fuels and products, with techno-economic analysis (TEA) frequently used to highlight the economic potential and technical challenges of utilizing this biomass in a biorefinery context. However, many historical TEA studies have focused on the conversion of biomass with elevated levels of carbohydrates and lipids and lower levels of protein, incurring substantial burdens on the ability to achieve high cultivation productivity rates relative to nutrient-replete, high-protein biomass. Given a strong dependence of algal biomass production costs on cultivation productivity, further TEA assessment is needed to understand the economic potential for utilizing potentially lower-cost but lower-quality, high-protein microalgae for biorefinery conversion. RESULTS: In this work, we conduct rigorous TEA modeling to assess the economic viability of two conceptual technology pathways for processing proteinaceous algae into a suite of fuels and products. One approach, termed mild oxidative treatment and upgrading (MOTU), makes use of a series of thermo-catalytic operations to upgrade solubilized proteins and carbohydrates to hydrocarbon fuels, while another alternative focuses on the biological conversion of those substrates to oxygenated fuels in the form of mixed alcohols (MA). Both pathways rely on the production of polyurethanes from unsaturated fatty acids and valorization of unconverted solids for use as a material for synthesizing bioplastics. The assessment found similar, albeit slightly higher fuel yields and lower costs for the MA pathway, translating to a residual solids selling price of $899/ton for MA versus $1033/ton for MOTU as would be required to support a $2.50/gallon gasoline equivalent (GGE) fuel selling price. A variation of the MA pathway including subsequent upgrading of the mixed alcohols to hydrocarbon fuels (MAU) reflected a required solids selling price of $975/ton. CONCLUSION: The slight advantages observed for the MA pathway are partially attributed to a boundary that stops at oxygenated fuels versus fungible drop-in hydrocarbon fuels through a more complex MOTU configuration, with more comparable results obtained for the MAU scenario. In either case, it was shown that an integrated algal biorefinery can be economical through optimal strategies to utilize and valorize all fractions of the biomass.

Application of attached algae flow-ways for coupling biomass production with the utilization of dilute non-point source nutrients in the Upper Laguna Madre, TX.

The purpose of this study is to determine the potential for an attached algae flow-way system to efficiently produce algal biomass in estuarine surface waters by utilizing dilute non-point source nutrients from local urban, industrial, and agricultural discharges into the Upper Laguna Madre, Corpus Christi, Texas. The study was conducted over the course of two years to establish seasonal base-line biomass productivity and composition for bioproducts applications, and to identify key environmental factors and flow-way cohorts impacting biomass production. For the entire cultivation period, continuous ash-free biomass production at 4 to 10 g/m2/day (corresponding to nutrient recovery at 300 to 500 mg of nitrogen/m2/day and 15 to 30 mg of phosphorus/m2/day) was successfully achieved without system restart. Upon start-up, a latency period was observed which indicates roles for species succession from relatively low productivity, high ash content pioneer periphytic culture composed primarily of benthic diatoms from the source waters to higher productivity, reduced ash content, and more resilient culture mainly composed of filamentous chlorophyta, Ulva lactuca. Principal Component Analysis (PCA) was used to identify environmental factors driving biomass production, and machine learning (ML) models were constructed to assess the predictive capability of the data set for system performance using the local multi-season environmental variations. Environmental datasets were segregated for ML training, validation, and testing using three methods: regression tree, ensemble regression, and Gaussian process regression (GPR). The predicted ash-free biomass productivity using ML models resulted in root-squared-mean-errors (RSME) from 1.78 to 1.86 g/m2/day, and R2 values from 0.67 to 0.75 using different methods. The greatest contributor to net productivity was total solar irradiation, followed by air temperature, salinity, and pH. The results of the study should be useful as a decision-making tool to application of attached algae flow-ways for biomass production while preventing algal blooms in the environment.

Microbial Heat and Organic Matter Loss in an Aerobic Corn Stover Storage Reactor: A Model Validation and Prediction Approach Using Lumped-Parameter Dynamical Formulation.

Corn stover dry matter loss effects variability for biofuel conversion facility and technology sustainability. This research seeks to understand the dynamic mechanisms of the thermal system, organic matter loss, and microbial heat generation in corn stover storage operations through system dynamics, a mathematical modeling approach, and response analysis to improve the system performance. This study considers epistemic uncertainties including cardinal temperatures of microbial respiratory activity, specific degradation rate, heat evolution per unit substrate degraded, and thermal conductivity in corn stover storage reactors. These uncertainties were managed through calibration, a process of improving the agreement between the computational and benchmark experimental results by adjusting the parameters of the model. Model calibration successfully predicted the temperature of the system as quantified by the mean absolute error, 0.6°C, relative to the experimental work. The model and experimental dry matter loss after 30 days of storage were 5.1% and 4.9 ± 0.28%. The model was further validated using additional experimental results to ensure that the model accurately represented the system. Model validation obtained a temperature mean absolute relative error of 0.9 ± 0.3°C and dry matter loss relative error of 3.1 ± 1.5%. This study presents a robust prediction of corn stover storage temperature and demonstrates that an understanding of carbon sources, microbial communities, and lag-phase evolution in bi-phasic growth are essential for the prediction of organic matter preservation in corn stover storage systems under environment's variation.

A geographical assessment of vegetation carbon stocks and greenhouse gas emissions on potential microalgae-based biofuel facilities in the United States.

The microalgae biofuels life cycle assessments (LCA) present in the literature have excluded the effects of direct land use change (DLUC) from facility construction under the assumption that DLUC effects are negligible. This study seeks to model the greenhouse gas (GHG) emissions of microalgae biofuels including DLUC by quantifying the CO2 equivalence of carbon released to the atmosphere through the construction of microalgae facilities. The locations and types of biomass and Soil Organic Carbon that are disturbed through microalgae cultivation facility construction are quantified using geographical models of microalgae productivity potential including consideration of land availability. The results of this study demonstrate that previous LCA of microalgae to biofuel processes have overestimated GHG benefits of microalgae-based biofuels production by failing to include the effect of DLUC. Previous estimations of microalgae biofuel production potential have correspondingly overestimated the volume of biofuels that can be produced in compliance with U.S. environmental goals.