THE USE OF LIFE-CYCLE ANALYSIS TO REDUCE THE ENVIRONMENTAL IMPACT OF MATERIALS IN MANUFACTURING

This thesis is composed of three life-cycle analysis (LCA) studies of manufacturing to determine cumulative energy demand (CED) and greenhouse gas emissions (GHG). The methods proposed could reduce the environmental impact by reducing the CED in three manufacturing processes. First, industrial symbiosis is proposed and a LCA is performed on both conventional 1 GW-scaled hydrogenated amorphous silicon (a-Si:H)-based single junction and a-Si:H/microcrystalline-Si:H tandem cell solar PV manufacturing plants and such plants coupled to silane recycling plants. Using a recycling process that results in a silane loss of only 17 versus 85 percent, this results in a CED savings of 81,700 GJ and 290,000 GJ per year for single and tandem junction plants, respectively. This recycling process reduces the cost of raw silane by 68 percent, or approximately $22.6 and $79 million per year for a single and tandem 1 GW PV production facility, respectively. The results show environmental benefits of silane recycling centered around a-Si:H-based PV manufacturing plants. Second, an open-source self-replicating rapid prototype or 3-D printer, the RepRap, has the potential to reduce the environmental impact of manufacturing of polymer-based products, using distributed manufacturing paradigm, which is further minimized by the use of PV and improvements in PV manufacturing. Using 3-D printers for manufacturing provides the ability to ultra-customize products and to change fill composition, which increases material efficiency. An LCA was performed on three polymer-based products to determine the CED and GHG from conventional large-scale production and are compared to experimental measurements on a RepRap producing identical products with ABS and PLA. The results of this LCA study indicate that the CED of manufacturing polymer products can possibly be reduced using distributed manufacturing with existing 3-D printers under 89% fill and reduced even further with a solar photovoltaic system. The results indicate that the ability of RepRaps to vary fill has the potential to diminish environmental impact on many products. Third, one additional way to improve the environmental performance of this distributed manufacturing system is to create the polymer filament feedstock for 3-D printers using post-consumer plastic bottles. An LCA was performed on the recycling of high density polyethylene (HDPE) using the RecycleBot. The results of the LCA showed that distributed recycling has a lower CED than the best-case scenario used for centralized recycling. If this process is applied to the HDPE currently recycled in the U.S., more than 100 million MJ of energy could be conserved per annum along with significant reductions in GHG. This presents a novel path to a future of distributed manufacturing suited for both the developed and developing world with reduced environmental impact. From improving manufacturing in the photovoltaic industry with the use of recycling to recycling and manufacturing plastic products within our own homes, each step reduces the impact on the environment. The three coupled projects presented here show a clear potential to reduce the environmental impact of manufacturing and other processes by implementing complimenting systems, which have environmental benefits of their own in order to achieve a compounding effect of reduced CED and GHG.

DESIGN OF EFFICIENT IN-NETWORK DATA PROCESSING AND DISSEMINATION FOR VANETS

By providing vehicle-to-vehicle and vehicle-to-infrastructure wireless communications, vehicular ad hoc networks (VANETs), also known as the “networks on wheels”, can greatly enhance traffic safety, traffic efficiency and driving experience for intelligent transportation system (ITS). However, the unique features of VANETs, such as high mobility and uneven distribution of vehicular nodes, impose critical challenges of high efficiency and reliability for the implementation of VANETs. This dissertation is motivated by the great application potentials of VANETs in the design of efficient in-network data processing and dissemination. Considering the significance of message aggregation, data dissemination and data collection, this dissertation research targets at enhancing the traffic safety and traffic efficiency, as well as developing novel commercial applications, based on VANETs, following four aspects: 1) accurate and efficient message aggregation to detect on-road safety relevant events, 2) reliable data dissemination to reliably notify remote vehicles, 3) efficient and reliable spatial data collection from vehicular sensors, and 4) novel promising applications to exploit the commercial potentials of VANETs. Specifically, to enable cooperative detection of safety relevant events on the roads, the structure-less message aggregation (SLMA) scheme is proposed to improve communication efficiency and message accuracy. The scheme of relative position based message dissemination (RPB-MD) is proposed to reliably and efficiently disseminate messages to all intended vehicles in the zone-of-relevance in varying traffic density. Due to numerous vehicular sensor data available based on VANETs, the scheme of compressive sampling based data collection (CS-DC) is proposed to efficiently collect the spatial relevance data in a large scale, especially in the dense traffic. In addition, with novel and efficient solutions proposed for the application specific issues of data dissemination and data collection, several appealing value-added applications for VANETs are developed to exploit the commercial potentials of VANETs, namely general purpose automatic survey (GPAS), VANET-based ambient ad dissemination (VAAD) and VANET based vehicle performance monitoring and analysis (VehicleView). Thus, by improving the efficiency and reliability in in-network data processing and dissemination, including message aggregation, data dissemination and data collection, together with the development of novel promising applications, this dissertation will help push VANETs further to the stage of massive deployment.

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.

Predictive control of hybrid vehicle powertrain for intelligent energy management

This thesis studies the minimization of the fuel consumption for a Hybrid Electric Vehicle (HEV) using Model Predictive Control (MPC). The presented MPC – based controller calculates an optimal sequence of control inputs to a hybrid vehicle using the measured plant outputs, the current dynamic states, a system model, system constraints, and an optimization cost function. The MPC controller is developed using Matlab MPC control toolbox. To evaluate the performance of the presented controller, a power-split hybrid vehicle, 2004 Toyota Prius, is selected. The vehicle uses a planetary gear set to combine three power components, an engine, a motor, and a generator, and transfer energy from these components to the vehicle wheels. The planetary gear model is developed based on the Willis’s formula. The dynamic models of the engine, the motor, and the generator, are derived based on their dynamics at the planetary gear. The MPC controller for HEV energy management is validated in the MATLAB/Simulink environment. Both the step response performance (a 0 – 60 mph step input) and the driving cycle tracking performance are evaluated. Two standard driving cycles, Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET), are used in the evaluation tests. For the UDDS and HWFET driving cycles, the simulation results, the fuel consumption and the battery state of charge, using the MPC controller are compared with the simulation results using the original vehicle model in Autonomie. The MPC approach shows the feasibility to improve vehicle performance and minimize fuel consumption.

Ethanol Production from Hardboard Manufacturing Process Wastewater: Methods for Improving Product Yields from Hemicellulose Hydrolysates

Waste effluents from the forest products industry are sources of lignocellulosic biomass that can be converted to ethanol by yeast after pretreatment. However, the challenge of improving ethanol yields from a mixed pentose and hexose fermentation of a potentially inhibitory hydrolysate still remains. Hardboard manufacturing process wastewater (HPW) was evaluated at a potential feedstream for lignocellulosic ethanol production by native xylose-fermenting yeast. After screening of xylose-fermenting yeasts, Scheffersomyces stipitis CBS 6054 was selected as the ideal organism for conversion of the HPW hydrolysate material. The individual and synergistic effects of inhibitory compounds present in the hydrolysate were evaluated using response surface methodology. It was concluded that organic acids have an additive negative effect on fermentations. Fermentation conditions were also optimized in terms of aeration and pH. Methods for improving productivity and achieving higher ethanol yields were investigated. Adaptation to the conditions present in the hydrolysate through repeated cell sub-culturing was used. The objectives of this present study were to adapt S. stipitis CBS6054 to a dilute-acid pretreated lignocellulosic containing waste stream; compare the physiological, metabolic, and proteomic profiles of the adapted strain to its parent; quantify changes in protein expression/regulation, metabolite abundance, and enzyme activity; and determine the biochemical and molecular mechanism of adaptation. The adapted culture showed improvement in both substrate utilization and ethanol yields compared to the unadapted parent strain. The adapted strain also represented a growth phenotype compared to its unadapted parent based on its physiological and proteomic profiles. Several potential targets that could be responsible for strain improvement were identified. These targets could have implications for metabolic engineering of strains for improved ethanol production from lignocellulosic feedstocks. Although this work focuses specifically on the conversion of HPW to ethanol, the methods developed can be used for any feedstock/product systems that employ a microbial conversion step. The benefit of this research is that the organisms will the optimized for a company's specific system.

Development of Vapor Deposited Silica Sol-Gel Particles for a Bioactive Materials System to Direct Osteoblast Behavior

Tissue engineering and regenerative medicine have emerged in an effort to generate replacement tissues capable of restoring native tissue structure and function, but because of the complexity of biologic system, this has proven to be much harder than originally anticipated. Silica based bioactive glasses are popular as biomaterials because of their ability to enhance osteogenesis and angiogenesis. Sol-gel processing methods are popular in generating these materials because it offers: 1) mild processing conditions; 2) easily controlled structure and composition; 3) the ability to incorporate biological molecules; and 4) inherent biocompatibility. The goal of this work was to develop a bioactive vaporization system for the deposition of silica sol-gel particles as a means to modify the material properties of a substrate at the nano- and micro- level to better mimic the instructive conditions of native bone tissue, promoting appropriate osteoblast attachment, proliferation, and differentiation as a means for supporting bone tissue regeneration. The size distribution, morphology and degradation behavior of the vapor deposited sol-gel particles developed here were found to be dependent upon formulation (H2O:TMOS, pH, Ca/P incorporation) and manufacturing (substrate surface character, deposition time). Additionally, deposition of these particles onto substrates can be used to modify overall substrate properties including hydrophobicity, roughness, and topography. Deposition of Ca/P sol particles induced apatite-like mineral formation on both two- and three-dimensional materials when exposed to body fluids. Gene expression analysis suggests that Ca/P sol particles induce upregulation osteoblast gene expression (Runx2, OPN, OCN) in preosteoblasts during early culture time points. Upon further modification-specifically increasing particle stability-these Ca/P sol particles possess the potential to serve as a simple and unique means to modify biomaterial surface properties as a means to direct osteoblast differentiation.