Hydrologic monitoring of an integrated low impact development (LID) underdrained stormwater management system

In 2005, Wetland Studies and Solutions, Inc. (WSSI) installed an extensive Low Impact Development (LID) stormwater management system on their new office site in Gainesville, Virginia. The 4-acre site is serviced by a network of LID components: permeable pavements (two proprietary and one gravel type), bioretention cell / rain garden, green roof, vegetated swale, rainwater harvesting and drip irrigation, and slow-release underground detention. The site consists of heavy clay soils, and the LID components are mostly integrated by a series of underdrain pipes. A comprehensive monitoring system has been designed and installed to measure hydrologic performance throughout the LID, underdrained network. The monitoring system measures flows into and out of each LID component independently while concurrently monitoring rainfall events. A sensitivity analysis and laboratory calibration has been performed on the flow measurement system. Field data has been evaluated to determine the hydrologic performance of the LID features. Finally, hydrologic models amenable to compact, underdrained LID sites have been reviewed and recommended for future modeling and design.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Extracellular matrix of the charophycean green algae

A comprehensive knowledge of cell wallstructure and function throughout the plant kingdom is essential to understanding cell wall evolution. The fundamental understanding of the charophycean green algal cell wall is broadening. The similarities and differences that exist between land plant and algal cell walls provide opportunities to understand plant evolution. A variety of polymers previously associated with higher plants were discovered in the charophycean green algae (CGA), including homogalacturonans, cross-linking glycans, arabinogalactan protein, β-glucans, and cellulose. The cellulose content of CGA cell walls ranged from 6% to 43%, with the higher valuescomparable to that found in the primary cell wall of land plants (20-30%). (1,3)β-glucans were found in the unicellular Chlorokybus atmophyticus, Penium margaritaceum, and Cosmarium turpini, the unbranched filamentous Klebsormidium flaccidum, and the multicellular Chara corallina. The discovery of homogalacturonan in Penium margaritaceum representsthe first confirmation of land plant-type pectinsin desmids and the second rigorous characterization of a pectin polymer from the charophycean algae. Homogalacturonan was also indicated from the basal species Chlorokybus atmophyticus and Klebsormidium flaccidum. There is evidence of branched pectins in Cosmarium turpini and linkage analysis suggests the presence of type I rhamnogalacturonan (RGI). Cross-linking β-glucans are associated with cellulose microfibrils during land plant cell growth, and were found in the cell wall of CGA. The evidence of mixed-linkage glucan (MLG) in the 11 charophytesis both suprising and significant given that MLG was once thought to be specific to some grasses. The organization and structure of Cosmarium turpini and Chara corallina MLG was found to be similar to that of Equisetumspp., whereas the basal species of the CGA, Chlorokybus atmophyticus and Klebsormidium flaccidum, have unique organization of alternating of 3- and 4-linkages. The significance of this result on the evolution of the MLG synthetic pathway has yet to be determined. The extracellular matrix (ECM) of Chlorokybus atmophyticus, Klebsormidium flaccidum, and Spirogyra spp. exhibits significant biochemical diversity, ranging from distinct “land plant” polymers to polysaccharides unique to these algae. The neutral sugar composition of Chlorokybus atmophyticus hot water extract and Spirogyra extracellular polymeric substance (EPS), combined with antibody labeling results, revealed the distinct possibility of an arabinogalactan protein in these organisms. Polysaccharide analysis of Zygnematales (desmid) EPS, indicated a probable range of different EPS backbones and substitution patterns upon the core portions of the molecules. Desmid EPS is predominately composed of a complex matrix of branched, uronic acid containing polysaccharides with ester sulfate substitutions and, as such, has an almost infinite capacity for various hydrogen bonding, hydrophobic interaction and ionic cross-bridging motifs, which characterize their unique function in biofilms. My observations support the hypothesis that members of the CGA represent the phylogenetic line that gave rise to vascular plants and that the primary cell wall of vascular plants many have evolved directly from structures typical of the cell wall of filamentous green algae found in the charophycean green algae.

LIFE CYCLE ASSESSMENT OF BIOFUEL PRODUCED FROM ALGAE

Algae are considered a promising source of biofuels in the future. However, the environmental impact of algae-based fuel has high variability in previous LCA studies due to lack of accurate data from researchers and industry. The National Alliance for Advanced Biofuels and Bioproducts (NAABB) project was designed to produce and evaluate new technologies that can be implemented by the algal biofuel industry and establish the overall process sustainability. The MTU research group within NAABB worked on the environmental sustainability part of the consortium with UOP-Honeywell and with the University of Arizona (Dr. Paul Blowers). Several life cycle analysis (LCA) models were developed within the GREET Model and SimaPro 7.3 software to quantitatively assess the environment viability and sustainability of algal fuel processes. The baseline GREET Harmonized algae life cycle was expanded and replicated in SimaPro software, important differences in emission factors between GREET/E-Grid database and SimaPro/Ecoinvent database were compared, and adjustments were made to the SimaPro analyses. The results indicated that in most cases SimaPro has a higher emission penalty for inputs of electricity, chemicals, and other materials to the algae biofuels life cycle. A system-wide model of algae life cycle was made starting with preliminary data from the literature, and then progressed to detailed analyses based on inputs from all NAABB research areas, and finally several important scenarios in the algae life cycle were investigated as variations to the baseline scenario. Scenarios include conversion to jet fuel instead of biodiesel or renewable diesel, impacts of infrastructure for algae cultivation, co-product allocation methodology, and different usage of lipid-extracted algae (LEA). The infrastructure impact of algae cultivation is minimal compared to the overall life cycle. However, in the scenarios investigating LEA usage for animal feed instead of internal recycling for energy use and nutrient recovery the results reflect the high potential variability in LCA results. Calculated life cycle GHG values for biofuel production scenarios where LEA is used as animal feed ranged from a 55% reduction to 127% increase compared to the GREET baseline scenario depending on the choice of feed meal. Different allocation methods also affect LCA results significantly. Four novel harvesting technologies and two extraction technologies provided by the NAABB internal report have been analysis using SimaPro LCA software. The results indicated that a combination of acoustic extraction and acoustic harvesting technologies show the most promising result of all combinations to optimize the extraction of algae oil from algae. These scenario evaluations provide important insights for consideration when planning for the future of an algae-based biofuel industry.