Algorithms and Methods for Optimizing the Spent Nuclear Fuel Allocation Strategy

Commercial nuclear power plants produce long-lasting nuclear waste, primarily in the form of spent nuclear fuel (SNF) assemblies. Spent fuel pools (SFP) and canisters or casks that sit at an independent spent fuel storage installation (ISFSI) at the reactor site store the fuel assemblies that are removed from operating reactors. The federal government has developed a plan to move the SNF from reactor sites to a Consolidated Interim Storage Facility (CISF) or a geological repository. In order to develop a predictable pick-up schedule and give utilities notice of an impending pickup from a reactor site, the federal government developed a queuing strategy based on the first-in-first-out algorithm, known as oldest fuel first (OFF). The OFF algorithm allows the federal government to remove SNF from reactor sites in the same order the assemblies came out of the reactor. While an OFF allocation strategy may result in a fair approach, it is far from the most cost-effective approach. The problem with accepting SNF using an OFF algorithm is that a handful of sites are no longer producing power and exist only to store the SNF they produced. This is an expensive process, which results in an annual cost of ~$8M [22]. Utilizing different algorithms to reduce the amount of time these shutdown reactors keep SNF on site may reduce the total system costs for the federal government. A greedy algorithm, genetic mutation algorithm, simulated annealing algorithm, and an integer programming formulation were all developed to reduce the number of years that reactors were shut down with SNF on site

Aspectos computacionales de algunos métodos de ajuste paramétrico de modelos aplicados a ciertos procesos de polimerización

En los últimos años se ha producido un cambio dramático en la industria de los procesos químicos. Los procesos industriales están ahora altamente integrados con respecto a los flujos de materia y energía, limitados aún mas fuertemente por altas calidades en las especificaciones de los productos y sujetos a estrictas medidas de seguridad y a la regulación de emisiones ambientales. Estas severas condiciones de operación a menudo colocan nuevas restricciones en la flexibilidad en la operación de los procesos. Todos estos factores producen grandes incentivos económicos para el mejoramiento y buen desempeño en los sistemas de control de las plantas industriales modernas [26]. Estas plantas requieren de sofisticados sistemas de cómputo para la implementación de estrategias de control. Es así que la mayoría de las nuevas plantas en las industrias químicas, del petróleo, papel, acero y otras están diseñadas y construidas con redes de microcomputadores para la adquisición de datos y control del proceso

NATURAL ALGORITHMS IN DIGITAL FILTER DESIGN

Digital filters are an important part of Digital Signal Processing (DSP), which plays vital roles within the modern world, but their design is a complex task requiring a great deal of specialised knowledge. An analysis of this design process is presented, which identifies opportunities for the application of optimisation. The Genetic Algorithm (GA) and Simulated Annealing are problem-independent and increasingly popular optimisation techniques. They do not require detailed prior knowledge of the nature of a problem, and are unaffected by a discontinuous search space, unlike traditional methods such as calculus and hill-climbing. Potential applications of these techniques to the filter design process are discussed, and presented with practical results. Investigations into the design of Frequency Sampling (FS) Finite Impulse Response (FIR) filters using a hybrid GA/hill-climber proved especially successful, improving on published results. An analysis of the search space for FS filters provided useful information on the performance of the optimisation technique. The ability of the GA to trade off a filter's performance with respect to several design criteria simultaneously, without intervention by the designer, is also investigated. Methods of simplifying the design process by using this technique are presented, together with an analysis of the difficulty of the non-linear FIR filter design problem from a GA perspective. This gave an insight into the fundamental nature of the optimisation problem, and also suggested future improvements. The results gained from these investigations allowed the framework for a potential 'intelligent' filter design system to be proposed, in which embedded expert knowledge, Artificial Intelligence techniques and traditional design methods work together. This could deliver a single tool capable of designing a wide range of filters with minimal human intervention, and of proposing solutions to incomplete problems. It could also provide the basis for the development of tools for other areas of DSP system design

Identificación, estimación y control de sistemas no-lineales mediante RGO

Se trata la identificación de sistemas, esto es: la estimación de modelos de sistemas dinámicos a partir de los datos observados. La estimación trata de evaluar y diseñar los estimadores de estado operando antes en un entorno estocástico. Se busca la mejora de la resolución de los problemas de identificación y estimación de estados de sistemas dinámicos no-lineales y el control adaptativo de los mismos. Se presenta un nuevo método híbrido para la optimización de funciones no lineales y no diferenciales que varían con el tiempo sin la utilización de demandas numéricas. Este método está basado en los Algoritmos Genéticos con una menor técnica de búsqueda que se ha llamado Optimización Genética Restringida. A partir de este algoritmo se presenta un método de altas prestaciones para la identificación de sistemas no lineales variables con el tiempo con modelos lineales y no lineales. Se presentan dos aplicaciones diferentes de estos métodos. _________________________________________________The system identification deals with the problem of estimating modeis of dynamical systems from observed data. The estimation tries to evaluate and to design state estimators. The two of them are supposed to operate in a stochastic environment. In this thesis, It has been tried to improve the methods of identification and state estimation of non-linear dynamical systems and their adaptive control. A new optimization hybrid method of non-linear and non-differentiable, time varying functions without using numerical derivatives is presented. This is important because of noise. This method based on Genetic Algorithms introduces a new technique called Restricted Genetié Optimization (ROO). This optimization method unifies the thesis and due to the fact that it is a basic method, it can be applied to a lot of problems related with non-differentiable and time-varying functions. Based on this algorithm, a high performance method for the identification of non-linear, time-varying systems with linear and non-linear modeis, is presented. This method can be used on-line and in a closed loop. For this reason, it is well adapted to control. This method uses an on line identification algorithm that begins by calculating what ARX is the best adapted to the system. This way the order and the delay of the system are known. Then, an ARMAX that is used as a seed to start the RGO and to create a NARMAX model, is calculated. The RGO algorithm can describe a new non-linear estimator for filtering of systems with non-linear processes and observation modeis based on the RGO optimization. The simulation results are used to compare the performance of this method with EKF (Extended Kaiman Filter), IEKF (Iterated Extended Kaiman Filter), SNF (Second-order Non-linear Filter), SIF (Single-stage Iterated Filter) y MSF (Montecarlo Simulation Filter) with different levels of noise. When this method is applied to the state space identification a new method is obtained. This method begins by calculating an ARX and then uses RGO in order to improve the previous identification. This method is based on the fuil parametrization and balanced realizations. This way low sensitivity realizations are obtained and the structural issues of multivariable canonical parametrizations are circumvented. Two applications of this method are considered. The first application is the predictive control with RGO of the Twin Rotor MIMO System (TRMS), that is a laboratory set-up designed for control experiments. In certain aspects, its behaviour resembles that of a helicopter. From the control point of view, it exemplifies a high order non-linear system with significant cross-couplings. The second one is the robot localization based on different kind of sensor information. To fuse all the different information, an algorithm is necessary. In this case, it has been used an extension of the Kalman algorithm with RGO