THE PERFORMANCE AND MODIFICATION OF RECYCLED ELECTRONIC WASTE PLASTICS FOR THE IMPROVEMENT OF ASPHALT PAVEMENT MATERIALS

Bulk electric waste plastics were recycled and reduced in size into plastic chips before pulverization or cryogenic grinding into powders. Two major types of electronic waste plastics were used in this investigation: acrylonitrile butadiene styrene (ABS) and high impact polystyrene (HIPS). This research investigation utilized two approaches for incorporating electronic waste plastics into asphalt pavement materials. The first approach was blending and integrating recycled and processed electronic waste powders directly into asphalt mixtures and binders; and the second approach was to chemically treat recycled and processed electronic waste powders with hydro-peroxide before blending into asphalt mixtures and binders. The chemical treatment of electronic waste (e-waste) powders was intended to strengthen molecular bonding between e-waste plastics and asphalt binders for improved low and high temperature performance. Superpave asphalt binder and mixture testing techniques were conducted to determine the rheological and mechanical performance of the e-waste modified asphalt binders and mixtures. This investigation included a limited emissions-performance assessment to compare electronic waste modified asphalt pavement mixture emissions using SimaPro and performance using MEPDG software. Carbon dioxide emissions for e-waste modified pavement mixtures were compared with conventional asphalt pavement mixtures using SimaPro. MEPDG analysis was used to determine rutting potential between the various e-waste modified pavement mixtures and the control asphalt mixture. The results from this investigation showed the following: treating the electronic waste plastics delayed the onset of tertiary flow for electronic waste mixtures, electronic waste mixtures showed some improvement in dynamic modulus results at low temperatures versus the control mixture, and tensile strength ratio values for treated e-waste asphalt mixtures were improved versus the control mixture.

Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce.