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.

STATE OF CHARGE BALANCING DROOP CONTROL

This thesis presents a load sharing method applied in a distributed micro grid system. The goal of this method is to balance the state-of-charge (SoC) of each parallel connected battery and make it possible to detect the average SoC of the system by measuring bus voltage for all connected modules. In this method the reference voltage for each battery converter is adjusted by adding a proportional SoC factor. Under such setting the battery with a higher SoC will output more power, whereas the one with lower SoC gives out less. Therefore the higher SoC battery will use its energy faster than the lower ones, and eventually the SoC and output power of each battery will converge. And because the reference voltage is related to SoC status, the information of the average SoC in this system could be shared for all modules by measuring bus voltage. The SoC balancing speed is related to the SoC droop factors. This SoC-based load sharing control system is analyzed in feasibility and stability. Simulations in MATLAB/Simulink are presented, which indicate that this control scheme could balance the battery SoCs as predicted. The observation of SoC sharing through bus voltage was validated in both software simulation and hardware experiments. It could be of use to non-communicated distributed power system in load shedding and power planning.

Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.