Fully Bayesian inference for α-stable distributions using a Poisson series representation

In this paper we develop an approach to Bayesian Monte Carlo inference for skewed α-stable distributions. Based on a series representation of the stable law in terms of infinite summations of random Poisson process arrival times, our framework leads to a simple representation in terms of conditionally Gaussian distributions for certain latent variables. Inference can therefore be carried out straightforwardly using techniques such as auxiliary variables versions of Markov chain Monte Carlo (MCMC) methods. The Poisson series representation (PSR) is further extended to practical application by introducing an approximation of the series residual terms based on exact moment calculations. Simulations illustrate the proposed framework applied to skewed α-stable simulated and real-world data, successfully estimating the distribution parameter values and being consistent with other (non-Bayesian) approaches. The methods are highly suitable for incorporation into hierarchical Bayesian models, and in this case the conditionally Gaussian structure of our model will lead to very efficient computations compared to other approaches.Godsill acknowledges partial funding for the work from the EPSRC BTaRoT project EP/K020153/1, and Tatjana Lemke acknowledges PhD funding from Fraunhofer ITWM, Kaiserslautern.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.dsp.2015.08.01

Expert control applied to electric submersible pumps

Orientador: Janito Vaqueiro FerreiraTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Na indústria do petróleo, é comum o uso de bombas centrífugas submersas (BCS) para elevar o óleo até a superfície. Normalmente as bombas BCS operam com fluido bifásico ''gás-líquido''. A presença de grandes quantidades de gás dentro da bomba gera instabilidades que deterioram o desempenho da bomba, o que pode causar até mesmo a interrupção da produção. Assim, nesta pesquisa, utilizou-se um controle especialista que tem como núcleo um sistema complexo adaptativo, o qual a partir da experiência pode gerar conhecimento sobre o funcionamento da BCS. Deste modo, o sistema especialista pode ser capaz de controlar o sistema BCS mantendo-o numa condição estável. Baseado em um modelo não-linear de uma BCS, que representa o comportamento da bomba operando com vazão bifásica, foi treinado um sistema especialista usando um algoritmo genético que gera um conjunto de regras (conhecimento adquirido) que com o tempo controlará e manterá o sistema BCS em condições operacionais seguras. No início do treinamento, o sistema de controle criado por algoritmos genéticos gerou comportamentos erráticos. No entanto, ao longo do tempo, o sistema especialista começou a entender o desempenho do sistema BCS, levando-o para condições estáveis. Além disso, se colocarmos o sistema BCS em condições instáveis, o sistema de controle o coloca de volta em condições seguras. O controle especialista, através do conhecimento adquirido com a experiência, possibilita manter uma BCS trabalhando com fluidos bifásicos em condições estáveis, o que permite evitar danos e paradas repentinas do equipamento durante a produção de petróleoAbstract: In the oil industry, it is common to use electric submersible pumps (ESP) to lift oil to the surface. It is usual for ESP to operate with biphasic fluid flow ''gas-liquid''. The presence of large amounts of gas within the pump generates instabilities and a deterioration in the performance of the pump, which can even cause production to be interrupted. Thus, in this research we aimed to use a control system, which has as its core an adaptive complex system, to generate knowledge about the operation of the ESP. In this way the expert system was able to control the ESP keeping it in the best possible condition. Based on a nonlinear model of an ESP, which represents the behavior of the pump operating with two-phase flow, has trained an expert system using a genetic algorithm that generate a set of rules (acquired knowledge) that over time would control and maintain the ESP in safe operating conditions. At the beginning of the training, the expert system created by genetic algorithms generated erratic behaviors. However, over the time, the expert system began to understand the performance of the ESP system and set it to stable conditions. Furthermore, if the ESP system starts to operate in unstable conditions, the control system sets it back within safe conditions. The expert control, through acquired knowledge with experience, may keep an ESP that is working with biphasic fluids in stable condition, which avoids equipment damage sudden stops during the oil production.DoutoradoMecatrônicaDoutor em Engenharia Mecânic