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This paper presents an effectiveness strategy to model and solve nonlinear mathematical optimization problems in electrical engineering using General Algebraic Modeling System (GAMS) for undergraduate students. A classical problem known as economic dispatch has been selected to show the need of using mathematical tools to solve a large scale optimization problem related with engineering. The economic dispatch is a classical optimization problem in operation of thermal electric systems, being the main idea to find an economical operation for thermal generators. This operation is based on a quadratic cost curve with some operating constraints, i.e., power balance and generation capacities. A numerical simulation is implemented using GAMS optimization package in demo version. This research has been developed with the support of 36 students of the course of Regulation and Operation of Electrical Systems (ROES) in the program of Electrical Engineering at Universidad Tecnológica de Pereira (UTP). [ABSTRACT FROM AUTHOR]

The aim of this paper study the impact of class-based storage policy based on the optimal configuration of U-flow single command warehouses using a on an ABC product classification. For that purpose, the authors propose a non linear optimization model to minimize the expected travel distance of the warehouse and use analytical methods to solve it. The most important contribution is to provide a mathematical proof that regardless of the storage policy (It does not matter the specifics of the turnover pattern of the products), the optimal warehouse has a width that is the double of its length, and the pick and deposit point should be located in the middle of the width of the warehouse. In addition, authors perform a sensitivity analysis that indicates that the optimal solution is robust, meaning that a certain deviation from the optimum layout does not impose a significant penalty on the expected travel distance of the warehouse. [ABSTRACT FROM AUTHOR]

The purpose of the optimum designs is to determine the optimal experimental conditions in terms of minimum variance of the estimated vector of parameters, so that statistical inferences can be generated as accurate as possible. This theory presupposes knowledge of the function relating the explanatory variables with the response variable. In this situation, usually get designs with support points as parameters in the model. Since p-point designs assume that the model function is known, these could not be optimal in some practical situations because they do not allow to test the goodness of fit of the assumed model. In this paper we present a generalization of the methodology proposed by to increase the number of support points in D-optimality criterion. An expression for the variance of the predicted response in terms of a constant δ will be provided, which allow to determine the points to be added to the D-optimal design. We propose a strategy for choosing δ. Finally we provide a pseudo-optimal design with (p + s) support points. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for optimal relocation of transformers in distribution systems. The problem is formulated as an optimization model of multiobjective non-linear integer type, which considers investment and operating costs, and the benefit of the concept of assets to be recognized by usage charges as stipulated in Resolution CREG 097 of 2008. The model is solved using the multiobjective optimization algorithm NSGA II. To check the validity of the methodology, a real distribution system belonging to an electric utility in Colombia is used, which takes into account both single and three phase distribution transformers. The results compared with respect to the case without considering the relocation of distribution transformers, reflect the importance of this methodology and its benefits to the distribution companies, and serves as a support tool for compliance with the standards set by regulatory bodies that define the appropriate chargeability of distribution transformers. [ABSTRACT FROM AUTHOR]

In this paper we identified the best conditions for the removal of Acid Blue 9 dye (AB9) using flower wastes (FW) as an adsorbent were determined using a full factorial 2³ and a Box-Behnken design for further optimization. Adsorbent dose (D), dye concentration (C) and contact time (t), were the assessed variables. The dye content was quantified by UV-Vis spectrometry. The statistical model presented an adequate adjustment coefficient (R² = 99,18%), allowing to achieve a removal of 98,5% with a dosage of 7,8 gL-1, dye concentration of 7,11 mgL-1 and contact time of 104 min. These results suggest that flowers wastes are an alternative and potential adsorbent material for the treatment of dissolved acid dyes. [ABSTRACT FROM AUTHOR]

Unbalanced operation of distribution systems deteriorates power quality and increases investment and operation costs. Feeder reconfiguration and phase swapping are the two main approaches for load balancing, being the former more difficult to execute due to the reduced number of sectionalizing switches available in most distribution systems. On the other hand, phase swapping constitutes a direct, effective and low cost alternative for load balancing. The main contribution of this paper is the proposal of an optimization model and a solution technique for phase balancing planning in distribution systems. As regards the optimization model, a mixed integer nonlinear programming formulation is proposed. On the other hand, the proposed solution technique consists on a specialized genetic algorithm. To show the effectiveness of the proposed approach, several tests are carried out with two distribution systems of 37 and 19 buses, this last one with different load models. Results showed that in addition to the achievement of the primary objective of loss reduction, phase balancing allows obtaining other technical benefits such as improvement of voltage profile and alleviation of congested lines. [ABSTRACT FROM AUTHOR]

PCA and LDA are two of most widely used techniques for face recognition with IR images. In this paper we report the results obtained by using Genetics Algorithms for optimization the characteristic vector generated by these techniques, by assignation of weights to each vector according its performance in the classification task. It shows that, under the proposed scheme, is able to obtain a lower classification error compared to conventional method. [ABSTRACT FROM AUTHOR]