Search

Abstract: Biofuels are being considered as an alternative source of energy produced from fossil fuels. Among various biological resources, microalgae have captured lots of attention in recent years due to their potential use as renewable energy source for biofuel production since they offer high growth rate, high lipid content and potential for carbon dioxide (CO2) capture. Cultivation of microalgae with other microorganisms in terms of promoting biomass production and other associated compounds is increasing compared to pure culture of algae. Studies on mixed culture of algae and bacteria have demonstrated the significant role of bacteria in enhancing algal growth and valuable products based on mutualistic relationship. One of the systems that is inherent in providing such medium for the interaction between algae and bacteria is high rate algal ponds (HRAPs) for wastewater treatment, with the potential for cost-effective production of biofuel. However, little attention has been devoted to study the nature of interactions from a modeling perspective. In this work, a dynamic mathematical model is presented to investigate the behavior of algal-bacterial consortium in an open pond. Wastewater serves as feed, providing substrate for bacteria and essential nutrients for the growth of algae. CO2 is supplied into the pond as additional source of carbon for algae to grow faster and CO2 footprint mitigation. To study the dynamic behavior of this system, the model was constituted of mass balance equations for each biological and chemical component. Gas-liquid mass transfer of CO2 and oxygen between the atmosphere and the pond, mass transfer of the additional supplied CO2 gas, and the effect of light intensity on algal growth were considered in the equations. The model was validated against multiple sets of experimental data in the literature and a good agreement for continuous and batch cultures was obtained. The lipid production model was incorporated into the model structure, providing reasonable predictions of the accumulated lipid in the algae for the potential generation of biofuels. The developed process model was optimized under different operating conditions to predict the optimal paths for the combined purposes of wastewater treatment and algal growth to produce biofuel. The supplementation of CO2 with increasing the inlet concentration of nitrogen and feeding in a stepwise pattern promoted the algal growth and lipid formation. The proposed model can be used as a tool to estimate the performance of practical algal ponds according to the desired functionality.

Recent growth in applications related to the Internet of Things, has introduced new challenges to the design of integrated circuits. Accuracy of sensors, power consumption, speed, and sensitivity to internal and external noise sources are some of the challenges addressed in this dissertation. A delta-sigma circuit architecture is designed to measure resistance with an accuracy of -/+ 1% over a range of four orders of magnitude. This performance is verified through both analog and mixed-signal simulation and chip measurement. A novel compensation technique is developed and simulated to reduce the supply sensitivity of an LC oscillator. An improvement of 85% is verified based on simulations of jitter and frequency variation. A new architecture is proposed for design of oscillators, based on a double-ladder periodic structure, whose behavior is less sensitive to the output termination resistance as compared to conventional oscillators. The advantages of the designed oscillator is verified compared to conventional LC and single-ladder structure oscillators. The design is optimized based on phase noise and power consumption.

In this thesis, mechanisms that cause LC Oscillator frequency sensitivity to supply noise is analyzed. It is proven that variations in both the common-mode and differential-mode components can increase the jitter in the presence of supply variations due to non-linearity of the capacitors that are in the oscillator. A novel compensation technique that reduces this sensitivity is presented. Simulations show that this technique can reduce the periodic jitter due to supply sensitivity by more than 80%.

This dissertation is a numerical modeling study based on the findings of the two installed Permeable Interlocking Concrete Pavements (PICPs) in Louisville, KY and twenty one laboratory models. A new model derived to more accurately predict the captured surface runoff volume by the PICPs using Artificial Neural Networks (ANNs). The proposed model relates rainfall parameters and site characteristics to the runoff volume captured by the permeable pavements. The database used for developing the prediction models is obtained from the collected data of the monitored permeable pavements. The performance of the ANN-based models are analyzed and the results demonstrate that the model results compare satisfactorily with measured values. A parametric study is completed to determine the sensitivity of a variety of parameters on the captured runoff volume. The results indicate that the developed model is capable of estimating the captured runoff by the permeable pavements for different rain events and site characteristics. The ANN model considers all significant contributing factors and provides a more precise volume prediction than the linear model. Clogging, which is mainly caused by sediment deposition, is the other important factor that result in performance failure of PICPs. Measuring Volumetric Water Content (VWC) by Time Domain Reflectometers (TDRs) is an automated method to track the speed of clogging. Monitoring peak VWC during rain events has been used as an indication of clogging progression over the PICP. Five ANN models are developed from the recorded VWC in order to compute the peak VWC from the rainfall parameters and maintenance treatment. A comprehensive set of data including various rain events characteristics obtained from the rain gauge and the conducted maintenance on the PICP are used for training and testing the neural network models. The performances of the ANN models are assessed and the results demonstrate satisfactory model accuracy when compared to the measured values. A parametric study was completed and the results indicate that the models are capable of estimating the peak VWC of the permeable pavements for different locations. The models consider all the contribution factors and provide more precise prediction values than the linear model. Peak 5 minute intensity, the previous rainfall depth, and the cumulative rainfall depth from the installation are the most critical parameters with respect to the hydrologic performance of the PICP. Finally, twenty one model configurations with different combinations of slope, gap size, and joint filling material were built to study clogging progression and permeable pavement performance. In this study, a neural network model was used to predict the clogging progression rate with critical PICP characteristics. The results indicate that the model is accurately predicting the extent of clogging along the length of permeable pavement. Sensitivity analyses are completed and the results suggest surface slope and location as the most influential parameters on the clogging length. Moreover, the prediction model for infiltration edge progression is presented to estimate the rainfall depth with 99% accuracy on testing datasets. By predicting the precise cumulative rainfall depth based on the infiltration edge distance and the PICP specifications, the hydrologic operation for each configuration and at any rainfall depth is accessible. The results demonstrate that surface slope and gap size present the highest influence on the infiltration edge progression. By better understanding the effects of pavements’ specification and site characteristics and selecting the most efficient pavement configuration, improved future design and more effective maintenance operations can be achieved.