An antibody that recognizes a chelated form of hexavalent uranium was used in the development of two different immunosensors for uranium detection. Specifically, these sensors were utilized for the analysis of groundwater samples collected during a 2007 field study of in situ bioremediation in a aquifer located at Rifle, CO. The antibody-based sensors provided data comparable to that obtained using Kinetic Phosphorescence Analysis (KPA). Thus, these novel instruments and associated reagents should provide field researchers and resource managers with valuable new tools for on-site data acquisition.

Networks provide functionality beyond just packet routing and delivery. Network functions such as firewalls, caches, WAN optimizers, etc. are crucial for scaling networks and in supporting new applications. While traditionally network functions were implemented using dedicated hardware middleboxes, recent efforts have resulted in them being implemented as software and deployed in virtualized environment . This move towards virtualized network function is commonly referred to as network function virtualization (NFV). While the NFV proposal has been enthusiastically accepted by carriers and enterprises, actual efforts to deploy NFV have not been as successful. In this thesis we argue that this is because the current deployment strategy which relies on operators to ensure that network functions are configured to correctly implement policies, and then deploys these network functions as virtual machines (or containers), connected by virtual switches are ill- suited to NFV workload.

In this dissertation we propose an alternative NFV framework based on the use of static techniques such as type checking and formal verification. Our NFV framework consists of NetBricks – a NFV runtime and programming environment, that uses type checking to provide isolation, and presents a novel dataflow based approach to writing high performance network functions; and VMN a verification tool that can automatically check whether a set of NFs correctly implement network policy. Finally, we also show that simplifying NF development and deployment enable new applications, both in the wide-area and within datacenters.

This dissertation presents techniques for the numerical modeling and control of building systems, with an emphasis on thermostatically controlled loads. The primary objective of this work is to address technical challenges related to the management of energy use in commercial and residential buildings. This work is motivated by the need to enhance the performance of building systems and by the potential for aggregated loads to perform load following and regulation ancillary services, thereby enabling the further adoption of intermittent renewable energy generation technologies. To increase the generalizability of the techniques, an emphasis is placed on recursive and adaptive methods which minimize the need for customization to specific buildings and applications.

The techniques presented in this dissertation can be divided into two general categories: modeling and control. Modeling techniques encompass the processing of data streams from sensors and the training of numerical models. These models enable us to predict the energy use of a building and of sub-systems, such as a heating, ventilation, and air conditioning (HVAC) unit. Specifically, we first present an ensemble learning method for the short-term forecasting of total electricity demand in buildings. As the deployment of intermittent renewable energy resources continues to rise, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. Second, we present a recursive parameter estimation technique for identifying a thermostatically controlled load (TCL) model that is non-linear in the parameters. For TCLs to perform demand response services in real-time markets, online methods for parameter estimation are needed. Third, we develop a piecewise linear thermal model of a residential building and train the model using data collected from a custom-built thermostat. This model is capable of approximating unmodeled dynamics within a building by learning from sensor data.

Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following.

This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.

This dissertation develops an advanced battery management system where lithium-ion battery dynamics are governed by electrochemical principles, and estimation & control techniques are applied to enhance the performance and longevity. The rapid progress in electrified transportation, electronic devices, and energy storage system needs cutting-edge management system that can monitor and control the electric battery system safely. We address this topic in three phases: i) modeling, ii) estimation, and iii) controls respectively.

First, we provide an overview of battery fundamentals and develop mathematical models for electrochemical battery phenomena. Electrochemical modeling is an important tool for simulating the performance of a battery. With the electrochemical model as a simulator, one can demonstrate prototypes of battery management system without conducting expensive experimental protocols. Furthermore, designing reduced-order electrochemical models helps to develop real-time estimation/control algorithms for large-scale battery systems.

Second, we propose a systematic framework to identify battery cell using the electrochemical model. To accurately represent the cell chemistry, the model parameters must be correctly identified. Good fitting parameters take a long time to identify. Often, it requires disassembling the cell and performing specialized electrochemical characterization tests, a costly and difficult process. The proposed parameter estimation framework for an electrochemical model enables to estimate electrochemical parameters by using input-output data.

Lastly, we then develop a novel battery charging strategy for the identified electrochemical model while accounting for the electrochemical constraints associated to aging mechanism. The model-based optimal solutions reveal that the characteristic of optimal charging protocol is Bang-Ride control, which provides theoretical supports on the existing charging strategy. Furthermore, learning-based controller design via reinforcement learning is highlighted as an adaptive controller for battery charging strategy. The performance of this learning-based feedback controller is demonstrated with other charging strategies.