No abstract

No abstract

Biomass provides a renewable pathway to support current and future energy needs for liquid transportation fuels, and is also being investigated as a low net carbon feedstock for electricity generation. To leverage this renewable source of energy requires the development and utilization of biomass resources beyond the current production levels. One source of renewable biomass energy feedstock is agricultural residues. However, a recent study[1] identified six factors that limit sustainable agricultural residue removal, and it stated that a comprehensive assessment of sustainable residue removal limits must consider each of the six factors. These factors are: (1) soil organic carbon, (2) wind and water erosion, (3) plant nutrient balances, (4) soil water and temperature dynamics, (5) soil compaction, and (6) off-site environmental impacts. Each of these factors is described by a set of disparate and heterogeneous models that are not currently integrated together. In addition each of the models has been validated, developed, and is currently maintained by a subject area expert separate from the other models. Recoding the complete set of models into a single monolithic software structure is impractical due to the time needed to develop and validate the completed set of models. Instead an extensible software framework is needed that can integrate the model set together enabling analysis and optimization of agricultural residue harvest for energy usage. This paper presents an integrated modeling strategy that incorporates these model sets together with the needed GIS information within a single integrated computational engineering framework. This integrated computational engineering framework has been implemented to facilitate high fidelity spatial assessments of biomass resource management. A case study demonstrating initial implementations of the resulting interactive analysis and optimization framework is presented. The case study demonstrates how multiple constraints can be simultaneously considered as a part of assessing sustainable agricultural residue removal potential.

Abstract. Nitrogen application is a standard practice for maximizing productivity of an agronomic system. The challenge is that many commercial scale agricultural systems are inefficient in utilizing the nitrogen that is applied. Therefore, understanding the impact of land management practices on nitrogen use inefficiencies within the agroecosystem is critical. This paper presents an integrated model that quantifies the impact of various land management practices on specific agroecosystem units. This integrated model is composed of the Wind Erosion Prediction System (WEPS), the Revised Universal Soil Loss Equation, Version 2 (RUSLE2), the Soil Condition Index (SCI), and the daily CENTURY model, DAYCENT. The integrated model was used to determine the impact of land management strategies on greenhouse gas emissions and nitrate leaching in a 60.5 ha field in Webster County, Iowa, USA. It was found that nitrogen use efficiency can vary significantly across a field and that integrated land management strategies can reduce overall nitrogen losses.

The objective of this design report is to provide an assessment of current technologies used for production, dewatering, and converting microalgae cultivated in open-pond systems to biofuel. The original draft design was created in 2011 and has subsequently been brought into agreement with the DOE harmonized model. The design report extends beyond this harmonized model to discuss some of the challenges with assessing algal production systems, including the ability to (1) quickly assess alternative algal production system designs, (2) assess spatial and temporal variability, and (3) perform large-scale assessments considering multiple scenarios for thousands of potential sites. The Algae Logistics Model (ALM) was developed to address each of these limitations of current modeling efforts to enable assessment of the economic feasibility of algal production systems across the United States. The (ALM) enables (1) dynamic assessments using spatiotemporal conditions, (2) exploration of algal production system design configurations, (3) investigation of algal production system operating assumptions, and (4) trade-off assessments with technology decisions and operating assumptions. The report discusses results from the ALM, which is used to assess the baseline design determined by harmonization efforts between U.S. DOE national laboratories. Productivity and resource assessment data is provided by coupling the ALM with the Biomass Assessment Tool developed at PNNL. This high-fidelity data is dynamically passed to the ALM and used to help better understand the impacts of spatial and temporal constraints on algal production systems by providing a cost for producing extracted algal lipids annually for each potential site. Expected OutcomeThe expected outcome of the design report is to provide an update on current technologies and methods for cultivating, dewatering, and converting microalgae into biofuel. In addition, assessments of these technologies within an algal production system are performed using the ALM with data provided by the Biomass Assessment Tool. This computational modeling approach enables the ability to seamlessly integrate technologies being built across the BETO research platform and the broader research community while using high-fidelity data from each potential site to explore design configurations and Technical Memorandum2 of 35 operational assumptions that make biofuels produced from microalgae a viable option. The harmonized baseline design determined by the national laboratories serves as a starting point for exploring alternative algal production system designs and operation. ProgressThe previous algae design report was updated to discuss numerous technologies for cultivating, dewatering, and converting microalgae to biofuels. The harmonized baseline design for large-scale open-pond microalgae production systems is assessed using a computational approach that enables coupling of disparate datasets and models. Baseline performance and costs for algal production systems are characterized in terms o...