Development of an optimization model for biofuel facility size and location and a simulation model for design of a biofuel supply chain

To mitigate greenhouse gas (GHG) emissions and reduce U.S. dependence on imported oil, the United States (U.S.) is pursuing several options to create biofuels from renewable woody biomass (hereafter referred to as “biomass”). Because of the distributed nature of biomass feedstock, the cost and complexity of biomass recovery operations has significant challenges that hinder increased biomass utilization for energy production. To facilitate the exploration of a wide variety of conditions that promise profitable biomass utilization and tapping unused forest residues, it is proposed to develop biofuel supply chain models based on optimization and simulation approaches. The biofuel supply chain is structured around four components: biofuel facility locations and sizes, biomass harvesting/forwarding, transportation, and storage. A Geographic Information System (GIS) based approach is proposed as a first step for selecting potential facility locations for biofuel production from forest biomass based on a set of evaluation criteria, such as accessibility to biomass, railway/road transportation network, water body and workforce. The development of optimization and simulation models is also proposed. The results of the models will be used to determine (1) the number, location, and size of the biofuel facilities, and (2) the amounts of biomass to be transported between the harvesting areas and the biofuel facilities over a 20-year timeframe. The multi-criteria objective is to minimize the weighted sum of the delivered feedstock cost, energy consumption, and GHG emissions simultaneously. Finally, a series of sensitivity analyses will be conducted to identify the sensitivity of the decisions, such as the optimal site selected for the biofuel facility, to changes in influential parameters, such as biomass availability and transportation fuel price. Intellectual Merit The proposed research will facilitate the exploration of a wide variety of conditions that promise profitable biomass utilization in the renewable biofuel industry. The GIS-based facility location analysis considers a series of factors which have not been considered simultaneously in previous research. Location analysis is critical to the financial success of producing biofuel. The modeling of woody biomass supply chains using both optimization and simulation, combing with the GIS-based approach as a precursor, have not been done to date. The optimization and simulation models can help to ensure the economic and environmental viability and sustainability of the entire biofuel supply chain at both the strategic design level and the operational planning level. Broader Impacts The proposed models for biorefineries can be applied to other types of manufacturing or processing operations using biomass. This is because the biomass feedstock supply chain is similar, if not the same, for biorefineries, biomass fired or co-fired power plants, or torrefaction/pelletization operations. Additionally, the research results of this research will continue to be disseminated internationally through publications in journals, such as Biomass and Bioenergy, and Renewable Energy, and presentations at conferences, such as the 2011 Industrial Engineering Research Conference. For example, part of the research work related to biofuel facility identification has been published: Zhang, Johnson and Sutherland [2011] (see Appendix A). There will also be opportunities for the Michigan Tech campus community to learn about the research through the Sustainable Future Institute

Countering Cybersecurity Vulnerabilities in the Power System

Security vulnerabilities in software pose an important threat to power grid security, which can be exploited by attackers if not properly addressed. Every month, many vulnerabilities are discovered and all the vulnerabilities must be remediated in a timely manner to reduce the chance of being exploited by attackers. In current practice, security operators have to manually analyze each vulnerability present in their assets and determine the remediation actions in a short time period, which involves a tremendous amount of human resources for electric utilities. To solve this problem, we propose a machine learning-based automation framework to automate vulnerability analysis and determine the remediation actions for electric utilities. Then the determined remediation actions will be applied to the system to remediate vulnerabilities. However, not all vulnerabilities can be remediated quickly due to limited resources and the remediation action applying order will significantly affect the system\u27s risk level. Thus it is important to schedule which vulnerabilities should be remediated first. We will model this as a scheduling optimization problem to schedule the remediation action applying order to minimize the total risk by utilizing vulnerabilities\u27 impact and their probabilities of being exploited. Besides, an electric utility also needs to know whether vulnerabilities have already been exploited specifically in their own power system. If a vulnerability is exploited, it has to be addressed immediately. Thus, it is important to identify whether some vulnerabilities have been taken advantage of by attackers to launch attacks. Different vulnerabilities may require different identification methods. In this dissertation, we explore identifying exploited vulnerabilities by detecting and localizing false data injection attacks and give a case study in the Automatic Generation Control (AGC) system, which is a key control system to keep the power system\u27s balance. However, malicious measurements can be injected to exploited devices to mislead AGC to make false power generation adjustment which will harm power system operations. We propose Long Short Term Memory (LSTM) Neural Network-based methods and a Fourier Transform-based method to detect and localize such false data injection attacks. Detection and localization of such attacks could provide further information to better prioritize vulnerability remediation actions

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.Comment: To appear at IMC 201

Mechanisms Involved in Symptom Modulation by Turnip Crinkle Virus and its Associated Subviral RNAs

Turnip crinkle virus (TCV) (family Tombusviridae, genus Carmovirus) is a positive- strand RNA virus. SatC, a satellite RNA associated with TCV, intensifies symptoms of TCV on all symptomatic hosts. Arabidopsis protoplast assays indicated that TCV virion levels are substantially reduced by the presence of satC or when two amino acids are inserted at the N-terminus of the coat protein (CP), resulting in similarly enhanced symptoms. Since the TCV CP is an RNA silencing suppressor, increased levels of the resultant free CP could augment silencing suppression resulting in enhanced colonization of the plant. Cloning and sequencing of virus-derived small RNAs (vsRNAs) accumulating in Arabidopsis plants infected with TCV with or without satC showed that the majority of vsRNAs are ~21-nt, purine-rich sequences. One TCV vsRNA species, TvsRNA5, is complementary to 3' UTR sequences in transcripts of 12 Arabidopsis genes. Transcript levels of 3 of these genes were reduced 2.4- to 4-fold by TCV infection, but restored to normal levels when infected with TCV containing a deletion in the TvsRNA5 sequence. This deletion did not affect levels of virus, but resulted in symptom attenuation in infected plants. These results suggest that at least some vsRNAs are specifically altering expression of host genes leading to phenotypic changes in host plants. In addition, a technique has been established for detection of viral RNAs in whole plants, which makes use of the binding of the CP of MS2 bacteriophage (CP MS2 ) to a 19 base hairpin (hp). Protoplast co-transfection of TCV containing the hairpin and a fusion protein construct consisting of CP MS2, GFP and a nuclear localization signal (NLS) relocated GFP from the nucleus to the cytoplasm, indicating the presence of virus. TCV movement was also tracked by observing cytoplasmic GFP fluorescence in infected transgenic plants expressing the fusion protein. This technique should be amenable for detection of any virus with a transformable host

Life-cycle energy and GHG emissions of forest biomass harvest and transport for biofuel production in Michigan

High dependence on imported oil has increased U.S. strategic vulnerability and prompted more research in the area of renewable energy production. Ethanol production from renewable woody biomass, which could be a substitute for gasoline, has seen increased interest. This study analysed energy use and greenhouse gas emission impacts on the forest biomass supply chain activities within the State of Michigan. A life-cycle assessment of harvesting and transportation stages was completed utilizing peer-reviewed literature. Results for forest-delivered ethanol were compared with those for petroleum gasoline using data specific to the U.S. The analysis from a woody biomass feedstock supply perspective uncovered that ethanol production is more environmentally friendly (about 62% less greenhouse gas emissions) compared with petroleum based fossil fuel production. Sensitivity analysis was conducted with key inputs associated with harvesting and transportation operations. The results showed that research focused on improving biomass recovery efficiency and truck fuel economy further reduced GHG emissions and energy consumption

Distribution and Abundance of Archaea in South China Sea Sponge Holoxea sp. and the Presence of Ammonia-Oxidizing Archaea in Sponge Cells

Compared with bacterial symbionts, little is known about archaea in sponges especially about their spatial distribution and abundance. Understanding the distribution and abundance of ammonia-oxidizing archaea will help greatly in elucidating the potential function of symbionts in nitrogen cycling in sponges. In this study, gene libraries of 16S rRNA gene and ammonia monooxygenase subunit A (amoA) genes and quantitative real-time PCR were used to study the spatial distribution and abundance of archaea in the South China Sea sponge Holoxea sp. As a result, Holoxea sp. specific AOA, mainly group C1a (marine group I: Crenarchaeota) were identified. The presence of ammonia-oxidizing crenarchaea was observed for the first time within sponge cells. This study suggested a close relationship between sponge host and its archaeal symbionts as well as the archaeal potential contribution to sponge host in the ammonia-oxidizing process of nitrification

Measuring the regional availability of forest biomass for biofuels and the potential of GHG reduction

Forest biomass is an important resource for producing bioenergy and reducing greenhouse gas (GHG) emissions. The State of Michigan in the United States (U.S.) is one region recognized for its high potential of supplying forest biomass; however, the long-term availability of timber harvests and the associated harvest residues from this area has not been fully explored. In this study time trend analyses was employed for long term timber assessment and developed mathematical models for harvest residue estimation, as well as the implications of use for ethanol. The GHG savings potential of ethanol over gasoline was also modeled. The methods were applied in Michigan under scenarios of different harvest solutions, harvest types, transportation distances, conversion technologies, and higher heating values over a 50-year period. Our results indicate that the study region has the potential to supply 0.75–1.4 Megatonnes (Mt) dry timber annually and less than 0.05 Mt of dry residue produced from these harvests. This amount of forest biomass could generate 0.15–1.01 Mt of ethanol, which contains 0.68–17.32 GJ of energy. The substitution of ethanol for gasoline as transportation fuel has potential to reduce emissions by 0.043–1.09 Mt CO2eq annually. The developed method is generalizable in other similar regions of different countries for bioenergy related analyses