Groundwater is a major source of water supply, and aquifers form major storage reservoirs as well as water conveyance systems, worldwide. The viability of groundwater as a source of water to the world's population is threatened by overexploitation and contamination. The rational management of water resource systems requires an understanding of their response to existing and planned schemes of exploitation, pollution prevention and/or remediation. Such understanding requires the collection of data to help characterize the system and monitor its response to existing and future stresses. It also requires incorporating such data in models of system makeup, water flow and contaminant transport. As the collection of subsurface characterization and monitoring data is costly, it is imperative that the design of corresponding data collection schemes is cost-effective. A major benefit of new data is its potential to help improve one's understanding of the system, in large part through a reduction in model predictive uncertainty and corresponding risk of failure. Traditionally, value-of-information or data-worth analyses have relied on a single conceptual-mathematical model of site hydrology with prescribed parameters. Yet there is a growing recognition that ignoring model and parameter uncertainties render model predictions prone to statistical bias and underestimation of uncertainty. This has led to a recent emphasis on conducting hydrologic analyses and rendering corresponding predictions by means of multiple models. We develop a theoretical framework of data worth analysis considering model uncertainty, parameter uncertainty and potential sample value uncertainty. The framework entails Bayesian Model Averaging (BMA) with emphasis on its Maximum Likelihood version (MLBMA). An efficient stochastic optimization method, called Differential Evolution Method (DEM), is explored to aid in the design of optimal sampling schemes aiming at maximizing data worth. A synthetic case entailing generated log hydraulic conductivity random fields is used to illustrate the procedure. The proposed data worth analysis framework is applied to field pneumatic permeability data collected from unsaturated fractured tuff at the Apache Leap Research Site (ALRS) near Superior, Arizona.

<div>Multi-model inference covers a wide range of modern statistical applications such as variable selection, model confidence set, model averaging and variable importance.</div><div>The performance of multi-model inference depends on the availability of candidate models, whose quality has been rarely studied in literature. In this dissertation, we study genetic algorithm (GA) in order to obtain high-quality candidate models. Inspired by the process of natural selection, GA performs genetic operations such as selection, crossover and mutation iteratively to update a collection of potential solutions (models) until convergence. The convergence properties are studied based on the Markov chain theory and used to design an adaptive termination criterion that vastly reduces the computational cost. In addition, a new schema theory is established to characterize how the current model set is improved through evolutionary process. Extensive numerical experiments are carried out to verify our theory and demonstrate the empirical power of GA, and new findings are obtained for two real data examples. </div>

In this thesis, three control methodologies are proposed for suppressing multi-mode vibration in flexible structures. Controllers developed using these methods are designed to (i) be able to cope with large and sudden changes in the system's parameters, (ii) be robust to unmodelled dynamics, and (iii) have a fast transient response. In addition, the controllers are designed to employ a minimum number of sensor-actuator pairs, and yet pose a minimum computational demand so as to allow real-time implementation. A cantilever beam with magnetically clamped loads is designed and constructed as the research vehicle for evaluation of the proposed controllers. Using this set-up, sudden and large dynamic variations of the beam loading can be tested, and the corresponding changes in the plant's parameters can be observed. Modal testing reveals that the first three modes of the plant are the most significant and need to be suppressed. It is also identified that the first and third modes are spaced more than a decade apart in frequency. The latter characteristic increases the difficulty of effectively controlling all three modes simultaneously using one controller. To overcome this problem, the resonant control method is chosen as the basis for the control methodologies discussed in this thesis. The key advantage of resonant control is that it can be tuned to provide specific attenuation only at and immediately close to the resonant frequency of concern. Consequently, it does not cause control spillover to other modes owing to unmodeled dynamics. Because of these properties, a resonant controller can be configured to form a parallel structure with the objective of targeting and cancelling multiple modes individually. This is possible regardless of the mode spacing. In addition, resonant control requires only a minimum number of collocated sensor-actuator pairs for multi-mode vibration cancellation. All these characteristics make resonant control a suitable candidate for multi-mode vibration cancellation of flexible structures. Since a resonant controller provides negligible attenuation away from the natural frequencies that it has been specifically designed for, it is very sensitive to changes of a system's natural frequencies and becomes ineffective when these mode frequencies change. Hence, for the case of a dynamically loaded structure with consequent variations in mode frequencies, the resonant control method must be modified to allow tracking of system parameter changes. This consideration forms the theme of this thesis, which is to allow adaptive multi-mode vibration control of dynamically-loaded flexible structures. Three controller design methodologies based on the resonant control principle are consequently proposed and evaluated. In the first approach, all possible loading conditions are assumed to be a priori known. Based on this assumption, a multi-model multi-mode resonant control (M4RC) method is proposed. The basis of the M4RC approach is that it comprises a bank of known loading models that are designed such that each model gives optimum attenuation for a particular loading condition. Conceptually, each model is implemented as a set of fixed-parameter controllers, one for each mode of concern. In reality, each mode controller is implemented as an adjustable resonant controller that is loaded with the fixed-model parameters of the corresponding mode. The M4RC method takes advantage of the highly frequency-sensitive nature of resonant control to allow simple and rapid selection of the optimum controller. Identification of the set of resonant frequencies is implemented using a bank of band-pass filters that correspond to the mode frequencies of the known models. At each time interval a supervisor scheme determines for each mode which model has the closest frequency to the observed vibration frequency and switches the corresponding model controller output to attenuate the mode. Selection is handled on a mode-by-mode basis, such that for each mode the closest model is selected. The proposed M4RC is relatively simple and less computationally complex compared to other multi-model methods reported in the literature. In particular, the M4RC uses a simple supervisor scheme and requires only a single controller per mode. Other multi-model methods use more complex supervision schemes and require one controller per model. The M4RC method is evaluated through both simulation and experimental studies. The results reveal that the proposed M4RC is very effective for controlling multi-mode vibration of a flexible structure with known loading conditions, but is ineffective for unmodeled loading conditions. In the second approach, the assumption that all loading conditions are a priori known is relaxed. An adaptive multi-mode resonant control (ARC) method is proposed to control the flexible structure for all possible (including unknown) loading conditions. On-line estimation of the structure's natural frequencies is used to update the adaptive resonant controller's parameters. The estimation of the natural frequencies is achieved using a parallel set of second-order recursive least-squares estimators, each of which is designed for a specific mode of concern. To optimise the estimation accuracy for each mode frequency, a different sampling rate suitable for that mode is used for the corresponding estimator. Simulation and experiment results show that the proposed adaptive method can achieve better performance, as measured by attenuation level, over its fixed-parameter counterpart for a range of unmodeled dynamics. The results also reveal that, for the same sequences of known loading changes, the transient responses of the ARC are slower than those of the M4RC. In the third approach, a hybrid multi-model and adaptive resonant control is utilized to improve the transient response of the ARC. The proposed multi-model multi-mode adaptive resonant control (M4ARC) method is designed as a combination of the M4RC and ARC methods. The basis of the proposed method is to use the M4RC fixed-parameter model scheme to deal with transient conditions while the ARC adaptive parameter estimator is still in a state of fluctuation. Then, once the estimator has reached the vicinity of its steady-state, the adaptive model is switched in place of the fixed model to achieve optimum control of the unforeseen loading condition. Whenever a loading change is experienced, the simple M4RC supervisor scheme is used to identify the closest model and to load the adjustable resonant controllers with the fixed parameters for that model. Meanwhile, the mode estimators developed for the ARC method are used to identify the exact plant parameters for the modes of concern. As soon as these parameters stop rapidly evolving and reach their steady-state, they are loaded into the respective adjustable controllers. The same process is repeated whenever a loading change occurs. Given the simplicity of the M4ARC method and its minimal computation demand, it is easily applicable for real-time implementation. Simulation and experiment results show that the proposed M4ARC outperforms both the ARC with respect to transient performance, and the M4RC with respect to unmodeled loading conditions. The outcomes of this thesis provide a basis for further development of the theory and application of active control for flexible structures with unforeseen configuration variations. Moreover, the basis for the proposed multi-model adaptive control can be used in other areas of control (not limited to vibration cancellation) where fast dynamic reconfiguration of the controller is necessary to accommodate structural changes and fluctuating external disturbances.

Hairy prairie-clover (Dalea villosa Nutt. (Spreng) var. villosa), a rare plant species, grows in the Canadian Prairies. Populations of Dalea in Canada are threatened by the loss of sand dune habitat because of changes in land use and altered ecological processes such as grazing and fire. Local populations of Dalea are further threatened by one or more specific threats, including herbivory from native and domestic ungulates and invasion of habitats by exotic plants. The overall objective of this thesis was to gain more knowledge about Dalea and to determine the impact of threats and management practices to the Saskatchewan populations and their habitats. Observational studies were conducted at each of two sites in Saskatchewan supporting Dalea. First, at the Dundurn Sandhills site, structural equation modeling was used to examine landscape, ecological, and management factors associated with high rates of herbivory on Dalea and with reductions in the long-term survival and productivity of Dalea. The conditions which deer (Odocoileus hemionus and Odocoileus virginianus) or cattle (Bos taurus) were responsible for the most intense rates of herbivory to Dalea plants and patches were determined. Generally, deer appeared responsible for the most herbivory, whereas cattle grazing on Dalea increased with stocking densities. At the same time, new hypotheses about ecological processes affecting Dalea productivity in the Dundurn Sandhills were explored. In particular, it appeared that deer may be responding to cattle grazing in Dalea habitat by avoiding those areas, and that mid-season germination and recruitment of many Dalea plants may occur following precipitation events. Second, at the Mortlach site, the costs and benefits of using grazing management to control leafy spurge (Euphorbia esula L. var. esula) were assessed, especially in consideration of the potential negative effects of intense herbivory on Dalea productivity. Aspects of the grazing regime including stocking density and the livestock species influenced herbivory on Dalea and its reproductive output, but there were no apparent links between the abundance of leafy spurge abundance and the reproductive output of Dalea. The findings of these two studies are relevant for the conservation and management of Dalea in Saskatchewan.

In this thesis, an architecture for the adaptive mode transition control of unmanned aerial vehicles (UAV) is presented. The proposed architecture consists of three levels: the highest level is occupied by mission planning routines where information about way points the vehicle must follow is processed. The middle level uses a trajectory generation component to coordinate the task execution and provides set points for low-level stabilizing controllers. The adaptive mode transitioning control algorithm resides at the lowest level of the hierarchy consisting of a mode transitioning controller and the accompanying adaptation mechanism. The mode transition controller is composed of a mode transition manager, a set of local controllers, a set of active control models, a set point filter, a state filter, an automatic trimming mechanism and a dynamic compensation filter. Local controllers operate in local modes and active control models operate in transitions between two local modes. The mode transition manager determines the actual mode of operation of the vehicle based on a set of mode membership functions and activates a local controller or an active control model accordingly. The adaptation mechanism uses an indirect adaptive control methodology to adapt the active control models. For this purpose, a set of plant models based on fuzzy neural networks is trained based on input/output information from the vehicle and used to compute sensitivity matrices providing the linearized models required by the adaptation algorithms. The effectiveness of the approach is verified through software-in-the-loop simulations, hardware-in-the-loop simulations and flight testing.

abstract: Microbial fuel cells(MFC) use micro-organisms called anode-respiring bacteria(ARB) to convert chemical energy into electrical energy. This process can not only treat wastewater but can also produce useful byproduct hydrogen peroxide(H2O2). Process variables like anode potential and pH play important role in the MFC operation and the focus of this dissertation are pH and potential control problems. Most of the adaptive pH control solutions use signal-based-norms as cost functions, but their strong dependency on excitation signal properties makes them sensitive to noise, disturbances, and modeling errors. System-based-norm( H-infinity) cost functions provide a viable alternative for the adaptation as they are less susceptible to the signal properties. Two variants of adaptive pH control algorithms that use approximate H-infinity frequency loop-shaping (FLS) cost metrics are proposed in this dissertation. A pH neutralization process with high retention time is studied using lab scale experiments and the experimental setup is used as a basis to develop a first-principles model. The analysis of such a model shows that only the gain of the process varies significantly with operating conditions and with buffering capacity. Consequently, the adaptation of the controller gain (single parameter) is sufficient to compensate for the variation in process gain and the focus of the proposed algorithms is the adaptation of the PI controller gain. Computer simulations and lab-scale experiments are used to study tracking, disturbance rejection and adaptation performance of these algorithms under different excitation conditions. Results show the proposed algorithm produces optimum that is less dependent on the excitation as compared to a commonly used L2 cost function based algorithm and tracks set-points reasonably well under practical conditions. The proposed direct pH control algorithm is integrated with the combined activated sludge anaerobic digestion model (CASADM) of an MFC and it is shown pH control improves its performance. Analytical grade potentiostats are commonly used in MFC potential control, but, their high cost (>$6000) and large size, make them nonviable for the field usage. This dissertation proposes an alternate low-cost($200) portable potentiostat solution. This potentiostat is tested using a ferricyanide reactor and results show it produces performance close to an analytical grade potentiostat. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

Plusieurs types d’expérimentation existent pour étudier et comprendre les systèmes biologiques. Dans ces travaux, nous nous intéressons à la simulation in silico, c’est-à-dire à la simulation numérique de modèles sur un ordinateur. Les systèmes biologiques sont composés d’entités, à la fois nombreuses et variées, en interaction les unes avec les autres. Ainsi, ils peuvent être modélisés par l’intermédiaire de deux approches complémentaires : l’approche population-centrée et l’approche individu-centrée. Face à la multitude et à la variété des phénomènes composant les systèmes biologiques, il nous semble pertinent de coupler ces deux approches pour obtenir une modélisation mixte. En outre, en raison de la quantité conséquente d’informations que représente l’ensemble des entités et des interactions à modéliser, la simulation numérique des systèmes biologiques est particulièrement coûteuse en temps de calcul informatique. Ainsi, dans ce mémoire, nous proposons des solutions techniques de parallélisation permettant d’exploiter au mieux les performances offertes par les architectures multicoeur et multiprocesseur et les architectures graphiques pour la simulation de systèmes biologiques à base de modélisations mixtes. Nous appliquons nos travaux au domaine de la coagulation du sang et plus particulièrement à l’étude de la cinétique biochimique à l’échelle microscopique ainsi qu’à la simulation d’un vaisseau sanguin virtuel. Ces deux applications nous permettent d’évaluer les performances offertes par les solutions techniques de parallélisation que nous proposons, ainsi que leur pertinence dans le cadre de la simulation des systèmes biologiques. / Several types of experimentation exist to study and understand biological systems. Inthis document, we take an interest in in silico simulation, i.e. numerical simulation ofmodels on a computer. Biological systems are made of many various entities, interactingwith each other. Therefore, they can be modeled by two complementary approaches: thepopulation-based approach and the individual-based one. Because of the multitude anddiversity of the phenomena constituting biological systems, we find the coupling of thesetwo approaches relevant to provide a hybrid modelisation. Moreover, because of the hugequantity of data that the entities and interactions represent, numerical simulation of biologicalsystems is especially computationaly intensive. This is why, in this document, we proposeparallel computing methods to take advantage of the performances offered by multicore andmultiprocessor architectures and by graphical ones for the simulation of biological systemsusing hybrid modelisations. We apply our work to blood coagulation and especially to thestudy of biochemical kinetics at the microscopic scale and the simulation of a virtual bloodvessel. These two applications enable us to assess both the performances obtained by theparallel computing methods we proposed and their relevance for biological systems simulation.

A presente tese tem por propósito contribuir para uma política de planejamento no campo da educação a distância (EAD). Para isto concebe uma proposta multi-modelo de referência lastreada na definição de estratégias em espectro de alta complexidade, que considera uma seqüência de procedimentos sistematizados nas seguintes fases: (i) Determinação das necessidades de informação, em duas etapas: (a) identificação dos fatores críticos de sucesso (FCS) e (b) identificação das áreas de informação; (ii) Determinação das competências, em três etapas: (a) determinação dos conhecimentos, (b) determinação das habilidades, e (c) determinação das atitudes; (iii) Determinação dos graus de avaliação de competências; (iv) Determinação das estratégias em redes de conhecimentos. Evidencia-se a aplicação a um estudo de caso nas concessões rodoviárias no Brasil, na perspectiva das parcerias público-privadas (PPPs). A consecução da pesquisa foi por meio da intervenção de especialistas e um grupo pequeno de estudantes de um programa de EAD (MBA) aplicado às PPPs. A coleta de dados foi por meio de um formulário semi-estruturado, do tipo escalar, em uma matriz de julgamento, com a intervenção de especialistas. Vários instrumentos de apoio foram utilizados na elaboração da modelagem, com vistas a reduzir a subjetividade dos resultados alcançados: escalagem psicométrica - Lei dos Julgamentos Categóricos de Thurstone (LJC), Multicriteriais-Compromise Programinng, Electre III, e Promethee II; Análise Multivariada; Krigagem, Redes Neurais Artificiais (RNA); Redes Neurofuzzy. Os resultados produzidos mostraram-se satisfatórios, validando o procedimento proposto para EAD. Procedimento este, fundamental na definição de programas destinados para planejar a capacitação de recursos humanos a distância, bem como para a constituição de outros elementos do capital intelectual em políticas de EAD. / This thesis intends to contribute to the planning guidelines in the field of distance education (DE). Thus, it develops a multi-model reference proposal supported by the definition of a highly complex spectrum of strategies that considers a sequence of systematic procedures in the following phases: (i) Determining the information needs in two stages: (a) identification of the critical success factors (CSF), and (b) identification of the information areas; (ii) Determination of competences in three stages, determining: (a) knowledge, (b) skills, and (c) attitudes; (iii) Determination of the degree of competence evaluation; and (iv) Determination of strategies in knowledge networks. There is the application to a case study of the road concessions in Brazil, within the perspective of public-private partnerships (PPPs). The research was achieved through the intervention of specialists and a small group of students from a DE program (MBA) applied to the PPPs. The data collection was conducted by means of a semi-structured form, the scalar type in a trial matrix, to which experts ascribed their assessments. Several support instruments were used in the modeling elaboration in order to reduce subjectivity in the results: psychometric scales - Thurstones Law of Comparative Judgment (LCJ), Multi-criteria Compromise Programming, Electre III, and Promethee II; Multivariate Analysis; Krigage, Artificial Neural Networking (ANN); Neuro-fuzzy networks. The results produced are satisfactory, validating the proposed procedure for DE. This is an essential procedure for the definition of programs designed to plan the training of human resources at a distance, as well as to establish other elements of intellectual capital for DE guidelines.

As colunas depropanizadoras existentes nas refinarias de petróleo têm como função a separação entre as correntes de propano e butano. O objetivo de controle nestas colunas é a especificação de um teor máximo de iso-butano e mais pesados (C4 +) na corrente de propano e do teor máximo de propano e mais leves (C3 -) na corrente de butano. Controladores multivariáveis tradicionais, que normalmente são implementados nas colunas depropanizadoras, apresentam grande dificuldade para manter os produtos dentro de suas especificações, isto se deve ao fato de que este processo apresenta um comportamento bastante não-linear ao longo de toda sua região de operação. Neste trabalho temos como objetivo estudar as dificuldades encontradas no projeto de controle para esse tipo de sistema e implantar na planta industrial um controlador multivariável utilizando múltiplos modelos para controle da coluna. Para realizarmos este estudo utilizamos o simulador de processos HYSYSÔ para verificarmos o comportamento estático e dinâmico do processo. Os modelos utilizados para representar o processo são aqueles obtidos durante o estudo do comportamento dinâmico. Para implantação do controlador na unidade industrial é utilizado o SICON (Sistema de Controle da Petrobras) sendo algumas de suas rotinas modificadas para permitir a inclusão dos múltiplos modelos. Durante o estudo são comparadas as performances dos controladores QDMC e MMPC (Multi-Model Predictive Control) resolvido através de um algoritmo para NLP (Non Linear Programming). O controlador multimodelos (MMPC) é apresentado na forma de variáveis de estado podendo controlar sistemas de grande porte, inclusive sistemas com dinâmicas lentas e rápidas. Esta formulação permite prever as variáveis controladas em instantes de tempo esparsos e diferentes para cada controlada. O MMPC é capaz de tratar problemas de controle não-linear usando modelos lineares, introduzindo o conceito de robustez com a utilização do conjunto de modelos. O MMPC exige um menor esforço de sintonia que o QDMC sendo adequado para uma região mais ampla de operação. / Depropanizer columns are used in oil refineries for the separation of the propane stream from the butane stream. The control objective of these columns is the specification of a maximum content of iso-butane and heavier components (C4+) in the propane product and the maximum content of propane and lighter components (C3-) in the butane roduct. Multivariable controllers usually mplemented in depropanizer columns frequently resent great difficulty to maintain the products inside their specification ranges. This deficiency is due to the fact that the process presents a quite non-linear behavior along its operating window. The objective of the present work is to study the difficulties found in the design of the control system for the aforesaid process, and to implement in an industrial plant a multivariable controller using multiple models for the control of the separation column. To accomplish this study we used the HYSYSÔ process simulator to verify the static and dynamic behavior of the process. The models used to represent the real process in the controller are those obtained during the study of the dynamic behavior. The controller implementation in the industrial unit was done with SICON (Control System of Petrobras), which had some of its routines modified to allow the inclusion of multiple models. Along the work, performances of QDMC and MMPC(Multi-Model Predictive Control) controllers were compared. MMPC was solved through an algorithm for NLP (Non Linear Programming). The Multi-Model (MMPC) controller was implemented using a state space formulation which allows for the implementation of very large systems and besides, systems with simultaneous slow and fast dynamics. This formulation allows to foresee the controlled variables at sparse sample instants, that can be distinct for each controlled variable. MMPC is able to handle non-linear control problems using linear models by introducing the robustness concept with the use of a set of models. MMPC demands a smaller tuning effort than QDMC, and can be adapted to a wide range of operating conditions.

Durante a operação de um controlador MPC, a planta pode ir para outro ponto de operação principalmente pela decisão operacional ou pela presença de perturbações medidas/não-medidas. Assim, o modelo do controlador deve ser adaptado para a nova condição de operação favorecendo o controle sob as novas condições. Desta forma, as condições ótimas de controle podem ser alcançadas com a maior quantidade de modelos identificados e com um controlador adaptativo que seja capaz de selecionar o melhor modelo. Neste trabalho é apresentada uma metodologia de controle adaptativo com identificação on-line do melhor modelo o qual pertence a um conjunto previamente levantado. A metodologia proposta considera um controlador em duas camadas e a excitação do processo através de um sinal GBN na camada de otimização com o controlador em malha fechada. Está sendo considerada a validação deste controlador adaptativo através da comparação dos resultados com duas diferentes técnicas Controlador MMPC e Identificação ARX, para a comprovação dos bons resultados desta metodologia. / During the operation of a MPC, the plant can change the operation point mainly due to management decision or due to the presence of measured or unmeasured disturbances. Thus, the model of the controller must be adapted to improve the control in the new operation conditions. In such a way, a better control policy can be achieved if a large number of models are identified at the possible operation points and it is available an adaptive controller that is capable of selecting the best model. In this work is presented a methodology of adaptive control with on-line identification of the most adequate model which belongs to a set of models previously obtained. The proposed methodology considers a two-layer controller and process excitation by a GBN signal in the LP optimization layer with the controller in closed loop mode. It is also presented the adaptive controller validation by comparing the proposed approach with two different techniques - MMPC and ARX Identification, to confirm the good results with this new methodology to the adaptive controller.