Sandia National Laboratories and General Motors Global Energy Systems team conducted a joint biofuels systems analysis project from March to November 2008. The purpose of this study was to assess the feasibility, implications, limitations, and enablers of large-scale production of biofuels. 90 billion gallons of ethanol (the energy equivalent of approximately 60 billion gallons of gasoline) per year by 2030 was chosen as the book-end target to understand an aggressive deployment. Since previous studies have addressed the potential of biomass but not the supply chain rollout needed to achieve large production targets, the focus of this study was on a comprehensive systems understanding the evolution of the full supply chain and key interdependencies over time. The supply chain components examined in this study included agricultural land use changes, production of biomass feedstocks, storage and transportation of these feedstocks, construction of conversion plants, conversion of feedstocks to ethanol at these plants, transportation of ethanol and blending with gasoline, and distribution to retail outlets. To support this analysis, we developed a 'Seed to Station' system dynamics model (Biofuels Deployment Model - BDM) to explore the feasibility of meeting specified ethanol production targets. The focus of this report is water and its linkage to broad scale biofuel deployment.
This study assesses the energy and water use of saltcedar (or tamarisk) as biomass for biofuel production in a hypothetical sub-region in New Mexico. The baseline scenario consists of a rural stretch of the Middle Rio Grande River with 25% coverage of mature saltcedar that is removed and converted to biofuels. A manufacturing system life cycle consisting of harvesting, transportation, pyrolysis, and purification is constructed for calculating energy and water balances. On a dry short ton woody biomass basis, the total energy input is approximately 8.21 mmBTU/st. There is potential for 18.82 mmBTU/st of energy output from the baseline system. Of the extractable energy, approximately 61.1% consists of bio-oil, 20.3% bio-char, and 18.6% biogas. Water consumptive use by removal of tamarisk will not impact the existing rate of evapotranspiration. However, approximately 195 gal of water is needed per short ton of woody biomass for the conversion of biomass to biocrude, three-quarters of which is cooling water that can be recovered and recycled. The impact of salt presence is briefly assessed. Not accounted for in the baseline are high concentrations of Calcium, Sodium, and Sulfur ions in saltcedar woody biomass that can potentially shift the relative quantities of bio-char and bio-oil. This can be alleviated by a pre-wash step prior to the conversion step. More study is needed to account for the impact of salt presence on the overall energy and water balance.
Carbon capture and sequestration (CCS) is an option to mitigate impacts of atmospheric carbon emission. Numerous factors are important in determining the overall effectiveness of long-term geologic storage of carbon, including leakage rates, volume of storage available, and system costs. Recent efforts have been made to apply an existing probabilistic performance assessment (PA) methodology developed for deep nuclear waste geologic repositories to evaluate the effectiveness of subsurface carbon storage (Viswanathan et al., 2008; Stauffer et al., 2009). However, to address the most pressing management, regulatory, and scientific concerns with subsurface carbon storage (CS), the existing PA methodology and tools must be enhanced and upgraded. For example, in the evaluation of a nuclear waste repository, a PA model is essentially a forward model that samples input parameters and runs multiple realizations to estimate future consequences and determine important parameters driving the system performance. In the CS evaluation, however, a PA model must be able to run both forward and inverse calculations to support optimization of CO{sub 2} injection and real-time site monitoring as an integral part of the system design and operation. The monitoring data must be continually fused into the PA model through model inversion and parameter estimation. Model calculations will in turn guide the design of optimal monitoring and carbon-injection strategies (e.g., in terms of monitoring techniques, locations, and time intervals). Under the support of Laboratory-Directed Research & Development (LDRD), a late-start LDRD project was initiated in June of Fiscal Year 2010 to explore the concept of an enhanced performance assessment system (EPAS) for carbon sequestration and storage. In spite of the tight time constraints, significant progress has been made on the project: (1) Following the general PA methodology, a preliminary Feature, Event, and Process (FEP) analysis was performed for a hypothetical CS system. Through this FEP analysis, relevant scenarios for CO{sub 2} release were defined. (2) A prototype of EPAS was developed by wrapping an existing multi-phase, multi-component reservoir simulator (TOUGH2) with an uncertainty quantification and optimization code (DAKOTA). (3) For demonstration, a probabilistic PA analysis was successfully performed for a hypothetical CS system based on an existing project in a brine-bearing sandstone. The work lays the foundation for the development of a new generation of PA tools for effective management of CS activities. At a top-level, the work supports energy security and climate change/adaptation by furthering the capability to effectively manage proposed carbon capture and sequestration activities (both research and development as well as operational), and it greatly enhances the technical capability to address this national problem. The next phase of the work will include (1) full capability demonstration of the EPAS, especially for data fusion, carbon storage system optimization, and process optimization of CO{sub 2} injection, and (2) application of the EPAS to actual carbon storage systems.
Analysis of chemical supply chains is an inherently complex task, given the dependence of these supply chains on multiple infrastructure systems (e.g. transportation and energy). This effort requires data and information at various levels of resolution, ranging from network-level distribution systems to individual chemical reactions. The U.S. Department of Homeland Security (DHS) has tasked the National Infrastructure Simulation and Analysis Center (NISAC) with developing a chemical infrastructure analytical capability to assess interdependencies and complexities of the nation's critical infrastructure, including the chemical sector. To address this need, the Sandia National Laboratories (Sandia)1 component of NISAC has integrated its existing simulation and infrastructure analysis capabilities with various chemical industry datasets to create a capability to analyze and estimate the supply chain and economic impacts resulting from large-scale disruptions to the chemical sector. This development effort is ongoing and is currently being funded by the DHS's Science and Technology Directorate. This paper describes the methodology being used to create the capability and the types of data necessary to exercise the capability, and it presents an example analysis focusing on the ethylene portion of the chemical supply chain.
Chlorinated hydrocarbons represent the precursors for products ranging from polyvinyl chloride (PVC) and refrigerants to pharmaceuticals. Natural or manmade disruptions that affect the availability of these products nationally have the potential to affect a wide range of markets, from healthcare to construction. Analysis of chemical supply chains is an inherently complex task, given the dependence of these supply chains on multiple infrastructure systems (e.g. transportation and energy). This effort requires data and information at various levels of resolution, ranging from network-level distribution systems to individual chemical reactions. The U.S. Department of Homeland Security (DHS) has tasked the National Infrastructure Simulation and Analysis Center (NISAC) with developing a chemical infrastructure analytical capability to assess interdependencies and complexities of the nation's critical infrastructure, including the chemical sector. To address this need, the Sandia National Laboratories (Sandia) component of NISAC has integrated its existing simulation and infrastructure analysis capabilities with various chemical industry datasets to create a capability to analyze and estimate the supply chain economic impacts resulting from large-scale disruptions to the chemical sector. This development effort is ongoing and is currently being funded by the DHS's Science and Technology Directorate. This paper describes the methodology being used to create the capability and the types of data necessary to exercise the capability, and it presents an example analysis focusing on the chlorinated hydrocarbon portion of the chemical supply chain.
Biofouling, the unwanted growth of biofilms on a surface, of water-treatment membranes negatively impacts in desalination and water treatment. With biofouling there is a decrease in permeate production, degradation of permeate water quality, and an increase in energy expenditure due to increased cross-flow pressure needed. To date, a universal successful and cost-effect method for controlling biofouling has not been implemented. The overall goal of the work described in this report was to use high-performance computing to direct polymer, material, and biological research to create the next generation of water-treatment membranes. Both physical (micromixers - UV-curable epoxy traces printed on the surface of a water-treatment membrane that promote chaotic mixing) and chemical (quaternary ammonium groups) modifications of the membranes for the purpose of increasing resistance to biofouling were evaluated. Creation of low-cost, efficient water-treatment membranes helps assure the availability of fresh water for human use, a growing need in both the U. S. and the world.
The objective of this project is to analyze the potential for hydrogen co-production within high-temperature stationary fuel cell systems (H2-FCS) and identify novel designs with minimum CO2 and cost. Specific objectives are to (1) develop novel H2-FCS designs that release low greenhouse gas emissions; and (2) develop novel H2-FCS designs with low hydrogen production cost.
The goal of this study is to model the electrical response of gold plated copper electrical contacts exposed to a mixed flowing gas stream consisting of air containing 10 ppb H{sub 2}S at 30 C and a relative humidity of 70%. This environment accelerates the attack normally observed in a light industrial environment (essentially a simplified version of the Battelle Class 2 environment). Corrosion rates were quantified by measuring the corrosion site density, size distribution, and the macroscopic electrical resistance of the aged surface as a function of exposure time. A pore corrosion numerical model was used to predict both the growth of copper sulfide corrosion product which blooms through defects in the gold layer and the resulting electrical contact resistance of the aged surface. Assumptions about the distribution of defects in the noble metal plating and the mechanism for how corrosion blooms affect electrical contact resistance were needed to complete the numerical model. Comparisons are made to the experimentally observed number density of corrosion sites, the size distribution of corrosion product blooms, and the cumulative probability distribution of the electrical contact resistance. Experimentally, the bloom site density increases as a function of time, whereas the bloom size distribution remains relatively independent of time. These two effects are included in the numerical model by adding a corrosion initiation probability proportional to the surface area along with a probability for bloom-growth extinction proportional to the corrosion product bloom volume. The cumulative probability distribution of electrical resistance becomes skewed as exposure time increases. While the electrical contact resistance increases as a function of time for a fraction of the bloom population, the median value remains relatively unchanged. In order to model this behavior, the resistance calculated for large blooms has been weighted more heavily.
The goal of this LDRD involves development of a system dynamics model to understand the interdependencies between water resource availability and water needs for production of biofuels. Specifically, this model focuses on availability and feasibility of non-traditional water sources from dairy wastewater, produced water from crude oil production and from coal-bed methane gas extraction for the production of algal-based biofuel. The conceptual simulation framework and historical data are based on two locales within New Mexico, the San Juan basin in the northwest and the Permian basin in the southeast, where oil and gas drilling have increased considerably in the last ten years. The overall water balance ignores both transportation options and water chemistry and is broken down by county level. The resulting model contains an algal growth module, a dairy module, an oil production module, and a gas production module. A user interface is also created for controlling the adjustable parameters in the model. Our preliminary investigation indicates a cyclical demand for non-fresh water due to the cyclical nature of algal biomass production and crop evapotranspiration. The wastewater from the dairy industry is not a feasible non-fresh water source because the agricultural water demand for cow's dry feed far exceeds the amount generated at the dairy. The uncertainty associated with the water demand for cow's dry matter intake is the greatest in this model. The oil- and gas-produced water, ignoring the quality, provides ample supply for water demand in algal biomass production. There remains work to address technical challenges associated with coupling the appropriate non-fresh water source to the local demand.