Análise da eficiência de aquecedor solar espiral com e sem efeito estufa para piscinas

The technical and economic viability of solar heating for swimming pools is unquestionable, besides there it replaces the high costs and environmental impacts of conventional supply of energy, and it improves an optimization in the pool heating uses. This work applies the principles of the greenhouse effect: advanced thermodynamics, heat retention and equalization of temperature, to optimize the solar heating equipment, reducing the area required by collectors as much as 40% (still estimated value) for commercial collectors, with minor architectural and aesthetic impacts on the environment. It features a solar heating alternative in pools, whose main characteristics: low cost, simplicity in manufacturing and assembly and a faster heating. The system consists of two collectors spiral hoses made of polyethylene with a hundred meters each, and working on a forced flow, with only one pass of the working fluid inside the coils, and is used to pump itself treatment of pool water to obtain the desired flow. One of the collectors will be exposed to direct solar radiation, and the other will be covered by a glass slide and closed laterally, so providing the greenhouse effect. The equipment will be installed in parallel and simultaneously exposed to the sun in order to obtain comparative data on their effectiveness. Will be presented results of thermal tests for this the two cases, with and without transparent cover. Will be demonstrated, by comparison, the thermal, economic and material feasibility of these systems for heating swimming pools.

Algoritmo Q-learning como estratégia de exploração e/ou explotação para metaheurísticas GRASP e algoritmo genético

Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process