Protecting valuable resources using optimal control theory and feedback strategies for plant disease management

Mathematical models of tree diseases often have little to say about how to manage established epidemics. Models often show that it is too late for successful disease eradication, but few study what management could still be beneficial. This study focusses on finding effective control strategies for managing sudden oak death, a tree disease caused by Phytophthora ramorum. Sudden oak death is a devastating disease spreading through forests in California and southwestern Oregon. The disease is well established and eradication is no longer possible. The ongoing spread of sudden oak death is threatening high value tree resources, including national parks, and culturally and ecologically important species like tanoak. In this thesis we show how the allocation of limited resources for controlling sudden oak death can be optimised to protect these valuable trees. We use simple, approximate models of sudden oak death dynamics, to which we apply the mathematical framework of optimal control theory. Applying the optimised controls from the approximate model to a complex, spatial simulation model, we demonstrate that the framework finds effective strategies for protecting tanoak, whilst also conserving biodiversity. When applied to the problem of protecting Redwood National Park, which is under threat from a nearby outbreak of sudden oak death, the framework finds spatial strategies that balance protective barriers with control at the epidemic wavefront. Because of the number of variables in the system, computational and numerical limitations restrict the control optimisation to relatively simple approximate models. We show how a lack of accuracy in the approximate model can be accounted for by using model predictive control, from control systems engineering: an approach coupling feedback with optimal control theory. Continued surveillance of the complex system, and re-optimisation of the control strategy, ensures that the result remains close to optimal, and leads to highly effective disease management. In this thesis we show how the machinery of optimal control theory can inform plant disease management, protecting valuable resources from sudden oak death. Incorporating feedback into the application of the resulting strategies ensures control remains effective over long timescales, and is robust to uncertainties and stochasticity in the system. Local management of sudden oak death is still possible, and our results show how this can be achieved

Abstracts of manuscripts submitted in 1990 for publication

This volume contans the abstracts of manuscripts submitted for publication during calendar year 1990 by the staff and students of the Woods Hole Oceanographic Institution. We identify the journal of those manuscripts which are in press or have been published. The volume is intended to be informative, but not a bibliography. The abstracts are listed by title in the Table of Contents and are grouped into one of our five deparments, Marine Policy Center, Coastal Research Center, or the student category. An author index is presented in the back to facilitate locating specific papers

Smart operation of transformers for sustainable electric vehicles integration and model predictive control for energy monitoring and management

The energy transmission and distribution systems existing today are stillsignificantly dependent on transformers,despite beingmore efficient and sustainable than those of decadesago. However, a large numberof power transformers alongwith other infrastructures have been in service for decades and are considered to be in their final ageing stage. Anymalfunction in the transformerscouldaffect the reliability of the entire electric network and alsohave greateconomic impact on the system.Concernsregardingurban air pollution, climate change, and the dependence on unstable and expensive supplies of fossil fuels have lead policy makers and researchers to explore alternatives to conventional fossil-fuelled internal combustion engine vehicles. One such alternative is the introduction of electric vehicles. A broad implementation of such mean of transportation could signify a drastic reduction in greenhouse gases emissions and could consequently form a compelling argument for the global efforts of meeting the emission reduction targets. In this thesis the topic of a high penetration of electric vehicles and their possible integration in insular networksis discussed. Subsequently, smart grid solutions with enabling technologies such as energy management systems and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector.However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles can introduce even more uncertaintyon the operation of these assets, which is an issue that needs special attention. Additionally, in order to improve the energy consumption efficiencyin a household, home energy management systems are alsoaddressed. A considerable number ofmethodologies developed are tested in severalcasestudies in order to answer the risen questions.Os sistemas de transmissão e distribuição de energia existentes hoje em dia sãosignificativamente dependentes dos transformadores, pese embora sejammais eficientes e sustentáveis do que os das décadas passadas. No entanto, uma grande parte dos transformadores ao nível dadistribuição, juntamente com outras infraestruturassubjacentes, estão em serviço há décadas e encontram-se nafasefinal do ciclo devida. Qualquer defeito no funcionamento dos transformadorespode afetara fiabilidadede toda a redeelétrica, para além de terum grande impactoeconómico no sistema.Os efeitos nefastos associadosàpoluição do arem centro urbanos, asmudançasclimáticasea dependência de fontes de energiafósseis têm levado os decisores políticos e os investigadores aexplorar alternativas para os veículos convencionais de combustão interna. Uma alternativa é a introdução de veículos elétricos. Umaampla implementação de tal meio de transporte poderia significar uma redução drástica dos gases de efeito de estufa e poderiareforçar os esforços globais para ocumprimento das metas de redução de emissõesde poluentes na atmosfera.Nesta tese é abordado o tema da elevada penetração dos veículos elétricose a sua eventual integração numarede elétricainsular. Posteriormente, são abordadas soluções de redeselétricasinteligentes com tecnologias específicas, tais como sistemas de gestão de energia e contadores inteligentes que promovamo paradigmadas casas inteligentes, que também permitem a gestão da procura ativano sector residencial.No entanto, deslastrando significativamente as cargaspara beneficiar de preçosmais reduzidosé suscetíveldecolocarconstrangimentosadicionaissobre os sistemas de distribuição, especialmentesobre ostransformadores.Osnovos tipos de cargas tais como os veículos elétricospodem introduzir ainda mais incertezassobre a operação desses ativos, sendo uma questão que suscitaespecial importância. Além disso, com ointuitode melhorar a eficiência do consumo de energia numa habitação, a gestão inteligente daenergia é um assunto que também éabordadonesta tese. Uma pletora de metodologias é desenvolvida e testadaemvários casos de estudos, a fim de responder às questões anteriormente levantadas

Efficient algorithms for risk-averse air-ground rendezvous missions

Demand for fast and inexpensive parcel deliveries in urban environments has risen considerably in recent years. A framework is envisioned to enforce efficient last-mile delivery in urban environments by leveraging a network of ride-sharing vehicles, where Unmanned Aerial Systems (UASs) drop packages on said vehicles, which then cover the majority of the distance before final aerial delivery. By combining existing networks we show that the range and efficiency of UAS-based delivery logistics are greatly increased. This approach presents many engineering challenges, including the safe rendezvous of both agents: the UAS and the human-operated ground vehicle. This dissertation presents tools that guarantee risk-optimal rendezvous between the two vehicles. We present mechanical and algorithmic tools that achieve this goal. Mechanically, we develop a novel aerial manipulator and controller that improves in-flight stability during the pickup and drop-off of packages. At a higher level and the core of this dissertation, we present planning algorithms that mitigate risks associated with human behavior at the longest time scales. First, we discuss the downfalls of traditional approaches. In aerial manipulation, we show that popular anthropomorphic designs are unsuitable for flying platforms, which we tackle with a combination of lightweight design of a delta-type parallel manipulator, and L1 adaptive control with feedforward. In planning algorithms, we present evidence of erratic driver behavior that can lead to catastrophic failures. Such a failure occurs when the UAS depletes its resource (battery, fuel) and has to crash land on an unplanned location. This is particularly dangerous in urban environments where population density is high, and the probability of harming a person or property in the event of a failure is unsafe. Studies have shown that two types of erratic behavior are common: speed variation and route choice. Speed variation refers to a common disregard for speed limits combined with different levels of comfort per driver. Route choice is conscious, unconscious, or purely random action of deviating from a prescribed route. Route choice uncertainty is high dimensional and complex both in space and time. Dealing with these types of uncertainty is important to many fields, namely traffic flow modeling. The critical difference to our interpretation is that we frame them in a motion planning framework. As such, we assume each driver has an unknown stochastic model for their behavior, a model that we aim to approximate through different methods. We aim to guarantee safety by quantifying motion planning risks associated with erratic human behavior. Only missions that plan on using all of the UAS's resources have inherent risk. We postulate that if we have a high assurance of success, any mission can be made to use more resources and be more efficient for the network by completing its objective faster. Risk management is addressed at three different scales. First, we focus on speed variation. We approach this problem with a combination of risk-averse Model Predictive Control (MPC) and Gaussian Processes. We use risk as a measure of the probability of success, centered around estimated future driver position. Several risk measures are discussed and CVaR is chosen as a robust measure for this problem. Second we address local route choice. This is route uncertainty for a single driver in some region of space. The primary challenge is the loss of gradient for the MPC controller. We extend the previous approach with a cross-entropy stochastic optimization algorithm that separates gradient-based from gradient-free optimization problems within the planner. We show that this approach is effective through a variety of numerical simulations. Lastly, we study a city-wide problem of estimating risk among several available drivers. We use real-world data combined with synthetic experiments and Deep Neural Networks (DNN) to produce an accurate estimator. The main challenges in this approach are threefold: DNN architecture, driver model, and data processing. We found that this learning problem suffers from vanishing gradients and numerous local minima, which we address with modern self-normalization techniques and mean-adjusted CVaR. We show the model's effectiveness in four scenarios of increasing complexity and propose ways of addressing its shortcomings

A Real-Time Optimal Control Approach for Water Quality and Quantity Management: Marina Reservoir Case Study

Ph.DDOCTOR OF PHILOSOPH

Radioactive Waste

The safe management of nuclear and radioactive wastes is a subject that has recently received considerable recognition due to the huge volume of accumulative wastes and the increased public awareness of the hazards of these wastes. This book aims to cover the practice and research efforts that are currently conducted to deal with the technical difficulties in different radioactive waste management activities and to introduce to the non-technical factors that can affect the management practice. The collective contribution of esteem international experts has covered the science and technology of different management activities. The authors have introduced to the management system, illustrate how old management practices and radioactive accident can affect the environment and summarize the knowledge gained from current management practice and results of research efforts for using some innovative technologies in both pre-disposal and disposal activities

Innovative Concepts and Applications for Smart Water Cities

Smart cities are emerging worldwide, including economic, institutional, social, and technical concepts in interaction with existing infrastructure to achieve sustainability and increase quality of life. Additionally, digitalisation projects in the field of urban water infrastructure (UWI) aim to increase capacity of existing infrastructure to deal with future challenges caused by climate change, growing of urban population, and maintenance. Therefore, efficient and reliable information- and communication technologies (ICT) represent a key factor for the exchange of measurement data (e.g., monitoring environmental parameters) and interconnections between different participants. However, ICT and system-wide management are not yet widely deployed and mainly concentrated on main points in network-based UWI (e.g., combined sewer overflows, inlet point of district meter areas). In this context, especially the Internet of Things (IoT) concepts enables a large-scale implementation of measurement devices even at underground and remote structures, increasing data availability significantly. Following, new possibilities in the management of network-based UWI are emerging. The research aim of this doctoral dissertation is to contribute to the ongoing development of smart water cities by developing innovative concepts in the field of urban drainage and water distribution network including nature-based solutions