Two relatively new EPA policies encourage the inclusion of pollution prevention in regulatory enforcement settlements. The advantages to a firm include reduction or elimination of environmental problems at the source (thus decreasing reliance on end-of-pipe controls), enhanced prospects for future compliance, and a potential for a reduction in the assessed penalty. We discuss the factors that influence both EPA and firms to include pollution prevention in enforcement settlements, characterize the process in a few exemplary cases, and recommend ways to enhance and expand these activities. The research presented focused on case study analysis of 10 recent EPA-negotiated enforcement settlements that included chemical substitutions, process changes, or closed-loop recycling

A identificação de modelos é usualmente a tarefa mais significativa e demorada no trabalho de implementação e manutenção de sistemas de controle que usam Controle Preditivo baseado em Modelos (MPC) tendo em vista a complexidade da tarefa e a importância que o modelo possui para um bom desempenho do controlador. Após a implementação, o controlador tende a permanecer com o modelo original mesmo que mudanças de processo tenham ocorrido levando a uma degradação das ações do controlador. Este trabalho apresenta uma aplicação industrial de re-identificação em malha fechada. A metodologia de excitação da planta utilizada foi apresentada em Sotomayor et al. (2009). Tal técnica permite obter o comportamento das variáveis de processo sem desligar o MPC e sem modificar sua estrutura, aumentando assim, o automatismo e a segurança do procedimento de re-identificação. O sistema re-identificado foi uma coluna debutanizadora de uma refinaria brasileira sendo que os modelos fazem parte do controle preditivo multivariável dessa coluna de destilação. A metodologia foi aplicada com sucesso podendo-se obter os seis novos modelos para atualizar o controlador em questão, o que resultou em uma melhoria de seu desempenho. / Model identification is usually the most significant and time-consuming task of implementing and maintaining control systems based on models (MPC) concerning the complexity of the task and the importance of the model for a good performance of the controller. After being implemented the MPC tends to remain with the original model even after process changes have occurred, leading to a degradation of the controller actions. The present work shows an industrial application of closed-loop re-identification. The plant excitation methodology used here was presented in Sotomayor et al. (2009). Such technique allows for obtaining the behavior of the process variables with the MPC still working and without modifying the MPC structure, increasing automation and safety of the re-identification procedure. The system re-identified was a debutanizer column of a Brazilian refinery being the models part of the multivariable predictive control of this distillation column. The methodology was applied with reasonable success managing to obtain 6 new models to update this MPC, and resulting in improved control performance.

Na implementação de controladores MPC, o desenvolvimento e a definição dos modelos do processo é a etapa mais crítica e a que mais consome tempo. Normalmente, os modelos são obtidos através de testes de identificação realizados na planta, onde se observam as respostas em malha aberta das variáveis controladas a perturbações introduzidas individualmente nas variáveis manipuladas. Por este motivo, a aplicação das técnicas de identificação em malha fechada a controladores MPC com restrições nas entradas e/ou saídas é, reconhecidamente, uma área de aplicação de interesse crescente. Neste trabalho é estudada a modificação do controlador MPC convencional através da inclusão de uma nova restrição de excitação em adição às restrições normais do controlador, com a finalidade de perturbar o processo de forma controlada, propiciando a identificação em malha fechada de modelos mais precisos do processo, a partir de modelos aproximados. São desenvolvidas quatro abordagens para implementação desta filosofia e apresentadas simulações para vários casos teóricos, utilizando modelos de dois processos industriais obtidos de artigos recentes relacionados a controle multivariável com incertezas nos modelos. Os resultados das simulações indicam que os dados produzidos permitiram a correta identificação dos modelos tanto no caso nominal (modelo igual à planta) quanto para casos onde a planta era diferente do modelo empregado para as predições do MPC. / In MPC implementation, the process models development and definition is the most critical and time consuming task. Normally, the models are obtained through plant identification tests where perturbations are individually introduced in the manipulated variable while the controlled variable open-loop behavior is observed. For this reason, the application of closed-loop identification techniques to MPC controllers with input or output constraints is a growing interest area. This work studies the traditional MPC controller modification with the inclusion of a new excitation constraint, in addition to input or output constraints, whose function is to perturb the process in a controlled way, permitting the closed-loop identification of more precise models, based on known approximated models. Four implementation methodologies are developed and some simulated theoretical cases are presented using models of two industrial processes extracted from recent papers related to multivariable control with models uncertainty. The simulation results show that the obtained datasets allow the identification of the correct model, both in the nominal case (when the model used by MPC is the true model of the plant) and in the uncertain case, where the model used by MPC is different from the true model.

A Identificação de Sistemas é uma tarefa significativa em termos de tempo e custo no trabalho de implementação de sistemas de controle que usam Controle Preditivo baseado em Modelos (MPC). Após a implementação, o controlador tende a permanecer com o mesmo modelo por muito tempo, ignorando mudanças que tenham ocorrido com o processo, perdendo qualidade e podendo até ser abandonado. Este trabalho propõe uma metodologia simples e eficaz para se proceder à reidentificação de uma planta industrial que use MPC mantendo o processo em malha fechada. Os principais aspectos deste problema são discutidos, e as escolhas que foram feitas para a realização dos experimentos e obtenção dos modelos são explicadas. Apresenta-se um caso em Matlab sobre um sistema 2x2 cobrindo diferentes situações, e é feita uma comparação de identificação realizada através de sinais PRBS e de testes com degraus, sempre em malha fechada. Aplica-se a metodologia a um controlador industrial, e os modelos identificados são introduzidos no controlador. O princípio básico desta metodologia consiste em efetuar perturbações multivariáveis nos set-points ou restrições ativas das controladas e determinar o modelo através da estrutura ARX. Entre as vantagens da metodologia proposta, estão a facilidade de automatizar a identificação do processo e a garantia de manter o processo sob controle durante os testes. / System identification is a major task in the process of implementing Model-based Predictive Control (MPC) algorithms in industrial applications. Once the controller is working, there is a tendency to leave it with the original model for a long time, neglecting changes to the process during this time, leading to performance degradation. This work proposes a simple and effective methodology to re-identify plants under MPC in closed loop. The main issues concerning this problem are discussed, and choices for experiments are made. A Matlab case involving a 2x2 problem is presented, covering a range of different situations, and a comparison between identification using PRBS reference signals and standard step tests is shown. An industrial case is studied, applying the proposed method to a real situation, re-identifying an existing MPC model and reconfiguring it afterwards. This methodology is based on the application of multivariable perturbations on the controlled variables set-points or active restrictions, obtaining an ARX model structure. It uses an automatic process identification proceeding, keeping the process under control along the tests.

In this research, carbon emissions and carbon savings in the smart grid are modelled and quantified. Carbon emissions are defined as the product of the activity (energy) and the corresponding carbon factor. The carbon savings are estimated as the difference between the conventional and improved energy usage multiplied by the corresponding carbon factor. An adaptive seasonal model based on the hyperbolic tangent function (HTF) is developed to define seasonal and daily trends of electricity demand and the resultant carbon emissions. A stochastic model describing profiles of energy usage and carbon emissions for groups of consumers is developed. The flexibility of the HTF for modelling cycles of energy consumption is demonstrated and discussed with several case studies. The analytical description to determine electricity grid carbon intensity in the UK is derived, using the available fuel mix data from the Elexon portal. The uncertain realisation of energy data is forecasted and assimilated using the ensemble Kalman filter (EnKF). The numerical optimisation of carbon emissions and savings in the smart grid is further performed using the ensemble-based Closed-loop Production Optimisation Scheme (EnOpt). The EnOpt involves the optimisation of fuel costs and carbon emissions (maximisation of carbon savings) in the smart grid subject to the operational control constraints. The software codes for the based on the application of EnKF and EnOpt are developed, and the optimisation of energy, cost and emissions is performed. The numerical simulation shows the ability of EnKF in forecasting and assimilating the energy data, and the robustness of the EnOpt in optimising costs and carbon savings. The proposed approach addresses the complexity and diversity of the power grid and may be implemented at the level of the transmission operator in collaboration with the operational wholesale electricity market and distribution network operators. The final stage of work includes the quantification of carbon emissions and savings in demand response (DR) programmes. DR programmes such as Short Term Operating Reserve (STOR), Triad, Fast Reserve, Frequency Control by Demand Management (FCDM) and smart meter roll-out are included, with various types of smart interventions. The DR programmes are modelled with appropriate configurations and assumptions in power plants used in the energy industry. This enables the comparison of emissions between the business-as-usual (BAU) and the smart solutions applied, thus deriving the carbon savings. Several case studies involving the modelling and analysing DR programmes are successfully performed. Thus, the thesis represents novel analytical and numerical techniques applied in the fast-growing UK market of smart energy solutions.

Les structures du système nerveux responsables des modalités de la prise de décision forment un circuit constitué par les ganglions de la base, le cortex, le thalamus et leurs nombreuses interconnexions. Ce circuit peut être décrit comme un ensemble de boucles fonctionnant en parallèle et interagissant en différents points. Des interactions entre ces boucles et de la plasticité de leurs connexions émergent les choix et donc les actions d’un individu. Ces comportements émergents et les phénomènes d’apprentissage qui en découlent sont abordés à travers une approche en boucle fermée dans laquelle le modèle théorique est en interaction constante avec l’environnement où se déroule la tâche comportementale étudiée. A cette fin, des outils de modélisation neuronale et d’analyse dédiés ont été développés dans le laboratoire d’accueil. Nous explorons donc ici la dynamique des flux d’information au sein de ce circuit à travers un modèle computationnel décrit à l’échelle du neurone et de la synapse. A partir d’observations expérimentales préalables réalisées sur le primate et de modèles computationnels antérieurs, nous avons développé de manière incrémentale un réseau capable d’apprendre à réaliser les tâches comportementales dans plusieurs protocoles et conditions. Le résultat obtenu ici est un modèle computationnel d’apprentissage et de prise de décision dans les ganglions de la base qui permet de tester des hypothèses expérimentales et d’effectuer des investigations physiopathologiques ou pharmacologiques in silico à l’échelle cellulaire. Le développement de ce modèle computationnel a été mené en parallèle avec l’étude expérimentale d’un protocole de prise de décision et la mise au point d’un modèle de maladie de Parkinson chez la salamandre (Pleurodeles waltlii). / The nervous system structures involved in decision making constitute a circuit formed by the basal ganglia, the cortex, the thalamus and their numerous interconnections. This circuit can be described as a set of loops operating in parallel and interacting at different points. The decisions and therefore the actions of an individual emerge from the interactions between these loops and the plasticity of their connections. These emerging behaviors and arising learning processes are addressed through a closed-loop approach in which the theoretical model is in constant interaction with the environment of the task. To this end, neural modeling and dedicated analysis software tools were developed in the laboratory. We explore here the dynamics of information flows within this circuit through a computational model described at the neuron and synapse level. Taking into account previous experimental observations from primates and earlier computational models, we incrementally developed a network capable of learning to perform behavioral tasks under several protocols and conditions. The result here is a computational model of learning and decision making in the basal ganglia that allows for the testing of experimental hypotheses and also to conduct in silico pathophysiological or pharmacological investigations at the cellular level. The development of this computational model was conducted in parallel with the development of an experimental protocol of decision making and with the adjustment of a model of Parkinson disease in the salamander (Pleurodeles waltlii).

No description available.

An MRI’s superior soft tissue contrast and ability to perform parametric scanning make it a powerful tool for use during medical procedures; from surgery to rehabilitation. However, the MRI’s strong static magnetic field, fast switching gradients and constrained space make accomplishing procedures within it difficult. Recent advances in the field of robotics have enabled the creation of devices capable of assisting medical practitioners in this environment. In this work, technologies to enable the use and control of robotic assistive devices for MRI interventions are presented. This includes a modular controller that is designed, built and used to control two surgical systems with minimal effect on image quality. Progressive improvements to an MRI conditional actuator including the construction of a first of a kind plastic piezoelectric resonant motor stator that improves the motor’s compatibility with the MRI is presented. Finally, control algorithms are evaluated for real-time functional MRI based control of a rehabilitation robot which includes the use of a robot for controlling brain activity of a subject in an online experiment.

This thesis deals with the design of the propulsion and braking system for narrow gauge railcar. It contains the design of hydrostatic drive system with closed-loop hydraulic circuit. Then thesis describes the concept of propulsion system placement in the frame of the railcar and design of the braking system.

The aim of this diploma thesis was to get familiar with methods to generate surrogates and how to apply them on cardiovascular signals. The first part of this diploma thesis describes the basic theory of baroreflex function and methods to generate surrogate data. Surrogate data were generated from data, acquired from the database, by using three different methods. In the next part of this diploma thesis, coherence significance between blood pressure and heart intervals was calculated by using surrogates. In the end two hypotheses were defined and tested by which it was detected whether the orthostatic change of the measurement position has effect on the causal coherence change and baroreflex function.