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Mariani V.C.,Pontifical Catholic University of Parana | Duck A.R.K.,Pontifical Catholic University of Parana | Guerra F.A.,LACTEC Institute of Technology for Development | Coelho L.D.S.,Pontifical Catholic University of Parana | Rao R.V.,Sardar Vallabhbhai National Institute of Technology, Surat
Applied Thermal Engineering | Year: 2012

Particle swarm optimization (PSO) method is a population-based optimization technique of swarm intelligence field in which each solution called "particle" flies around in a multidimensional problem search space. During the flight, every particle adjusts its position according to its own experience, as well as the experience of neighboring particles, using the best position encountered by itself and its neighbors. In this paper, a new quantum particle swarm optimization (QPSO) approach combined with Zaslavskii chaotic map sequences (QPSOZ) to shell and tube heat exchanger optimization is presented based on the minimization from economic view point. The results obtained in this paper for two case studies using the proposed QPSOZ approach, are compared with those obtained by using genetic algorithm, PSO and classical QPSO showing the best performance of QPSOZ. In order to verify the capability of the proposed method, two case studies are also presented showing that significant cost reductions are feasible with respect to traditionally designed exchangers. Referring to the literature test cases, reduction of capital investment up to 20% and 6% for the first and second cases, respectively, were obtained. Therefore, the annual pumping cost decreased markedly 72% and 75%, with an overall decrease of total cost up to 30% and 27%, respectively, for the cases 1 and 2, respectively, showing the improvement potential of the proposed method, QPSOZ. © 2011 Elsevier Ltd. All rights reserved. Source

Furtado A.C.,State University of Maringa | Alonso C.G.,State University of Maringa | Cantao M.P.,LACTEC Institute of Technology for Development | Fernandes-Machado N.R.C.,State University of Maringa
International Journal of Hydrogen Energy | Year: 2011

The ethanol oxidative reforming reaction was performed with Ni-Cu catalysts on different supports. The results indicated that Ni-Cu/Nb2O 5 and Ni-Cu/ZnO were the most appropriate catalysts for the reaction regarding activity, stability, and selectivity for hydrogen production. Ni-Cu/Nb2O5 catalysts have strong acidity (at 600 °C), while ZnO has very low acidity. Ni-Cu/Ce0.6Zr0.4O 2 catalysts, which only have weak acidity (at 250 °C), presented poor stability and hydrogen selectivity. This shows that acidity has no influence on hydrogen production. © 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved. Source

Hultmann Ayala H.V.,LACTEC Institute of Technology for Development | Dos Santos Coelho L.,Pontifical Catholic University of Parana
Expert Systems with Applications | Year: 2012

Most controllers optimization and design problems are multiobjective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Instead of aiming at finding a single solution, the multiobjective optimization methods try to produce a set of good trade-off solutions from which the decision maker may select one. Several methods have been devised for solving multiobjective optimization problems in control systems field. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theories are applied to obtain the solution of such problems. The presence of multiple objectives in a problem usually gives rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Recently, Multiobjective Evolutionary Algorithms (MOEAs) have been applied to control systems problems. Compared with mathematical programming, MOEAs are very suitable to solve multiobjective optimization problems, because they deal simultaneously with a set of solutions and find a number of Pareto optimal solutions in a single run of algorithm. Starting from a set of initial solutions, MOEAs use iteratively improving optimization techniques to find the optimal solutions. In every iterative progress, MOEAs favor population-based Pareto dominance as a measure of fitness. In the MOEAs context, the Non-dominated Sorting Genetic Algorithm (NSGA-II) has been successfully applied to solving many multiobjective problems. This paper presents the design and the tuning of two PID (Proportional-Integral-Derivative) controllers through the NSGA-II approach. Simulation numerical results of multivariable PID control and convergence of the NSGA-II is presented and discussed with application in a robotic manipulator of two-degree-of-freedom. The proposed optimization method based on NSGA-II offers an effective way to implement simple but robust solutions providing a good reference tracking performance in closed loop. © 2012 Elsevier Ltd. All rights reserved. Source

Coelho L.S.,Pontifical Catholic University of Parana | Coelho L.S.,Federal University of Parana | Guerra F.,LACTEC Institute of Technology for Development | Batistela N.J.,Federal University of Santa Catarina | Leite J.V.,Federal University of Santa Catarina
IEEE Transactions on Magnetics | Year: 2013

The parameter identification of hysteresis models is a fundamental task for correct hysteretic material simulation. In vector models, as the Jiles-Atherton (J-A) vector model, the parameter determination increases in complexity since one must solve a nonlinear system with a relative large number of variables. In these cases, fitting methods one of the most attractive solution. In this study, an improved multiobjective cuckoo search (IMCS) is introduced for the J-A parameters determination. The proposed IMCS based on the Duffing's oscillator to step size tuning is verified using data from a rotational single sheet tester in two-dimensional version. Numerical comparisons of IMCS with results using a multiobjective cuckoo search demonstrated that the performance of the IMCS is promising in parameters estimation. Furthermore, the proposed IMCS method can be easily extended to solve a wide range of multiobjective optimization problems. © 1965-2012 IEEE. Source

Lazzaretti A.E.,LACTEC Institute of Technology for Development | Vieira Neto H.,Federal University of Technology of Parana | Ferreira V.H.,Federal University of Fluminense
IEEE Transactions on Power Delivery | Year: 2015

This paper addresses one of the fundamental steps in automatic waveform analysis: transient segmentation. We present a new approach which incorporates the advantages of a multilevel wavelet decomposition and the representation of the support vector data description. Real data from a monitoring system developed for lightning overvoltage detection in overhead distribution power lines was used for comparison and validation of segmentation performance. The experiments involve the proposed segmentation approach and usual segmentation methods, such as Kalman filtering, autoregressive models, and standard discrete wavelet transform. The results show that the proposed segmentation method based on DWT+SVDD yields better overall accuracy for transient segmentation when compared to currently used methods, demonstrating the potential for applications in oscillographic recorders for smart distribution networks, where identification, characterization, and mitigation of events are critical for network operation and maintenance. © 1986-2012 IEEE. Source

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