Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Simulação de processos de adsorção recorrendo ao Aspen Chromatography

As operações de separação por adsorção têm vindo a ganhar importância nos últimos anos, especialmente com o desenvolvimento de técnicas de simulação de leitos móveis em colunas, tal como a cromatografia de Leito Móvel Simulado (Simulated Moving Bed, SMB). Esta tecnologia foi desenvolvida no início dos anos 60 como método alternativo ao processo de Leito Móvel Verdadeiro (True Moving Bed, TMB), de modo a resolver vários dos problemas associados ao movimento da fase sólida, usuais nestes métodos de separação cromatográficos de contracorrente. A tecnologia de SMB tem sido amplamente utilizada em escala industrial principalmente nas indústrias petroquímica e de transformação de açúcares e, mais recentemente, na indústria farmacêutica e de química fina. Nas últimas décadas, o crescente interesse na tecnologia de SMB, fruto do alto rendimento e eficiente consumo de solvente, levou à formulação de diferentes modos de operação, ditos não convencionais, que conseguem unidades mais flexíveis, capazes de aumentar o desempenho de separação e alargar ainda mais a gama de aplicação da tecnologia. Um dos exemplos mais estudados e implementados é o caso do processo Varicol, no qual se procede a um movimento assíncrono de portas. Neste âmbito, o presente trabalho foca-se na simulação, análise e avaliação da tecnologia de SMB para dois casos de separação distintos: a separação de uma mistura de frutose-glucose e a separação de uma mistura racémica de pindolol. Para ambos os casos foram considerados e comparados dois modos de operação da unidade de SMB: o modo convencional e o modo Varicol. Desta forma, foi realizada a implementação e simulação de ambos os casos de separação no simulador de processos Aspen Chromatography, mediante a utilização de duas unidades de SMB distintas (SMB convencional e SMB Varicol). Para a separação da mistura frutose-glucose, no quediz respeito à modelização da unidade de SMB convencional, foram utilizadas duas abordagens: a de um leito móvel verdadeiro (modelo TMB) e a de um leito móvel simulado real (modelo SMB). Para a separação da mistura racémica de pindolol foi considerada apenas a modelização pelo modelo SMB. No caso da separação da mistura frutose-glucose, procedeu-se ainda à otimização de ambas as unidades de SMB convencional e Varicol, com o intuito do aumento das suas produtividades. A otimização foi realizada mediante a aplicação de um procedimento de planeamento experimental, onde as experiências foram planeadas, conduzidas e posteriormente analisadas através da análise de variância (ANOVA). A análise estatística permitiu selecionar os níveis dos fatores de controlo de modo a obter melhores resultados para ambas as unidades de SMB.

Multi-criteria optimization manipulator trajectory planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions.

Node adaptation for global collocation with radial basis functions using direct multisearch for multiobjective optimization

Meshless methods are used for their capability of producing excellent solutions without requiring a mesh, avoiding mesh related problems encountered in other numerical methods, such as finite elements. However, node placement is still an open question, specially in strong form collocation meshless methods. The number of used nodes can have a big influence on matrix size and therefore produce ill-conditioned matrices. In order to optimize node position and number, a direct multisearch technique for multiobjective optimization is used to optimize node distribution in the global collocation method using radial basis functions. The optimization method is applied to the bending of isotropic simply supported plates. Using as a starting condition a uniformly distributed grid, results show that the method is capable of reducing the number of nodes in the grid without compromising the accuracy of the solution. (C) 2013 Elsevier Ltd. All rights reserved.

Supply chain optimization of residual forestry biomass for bioenergy production: the case study of Portugal

Within a large set of renewable energies being explored to tackle energy sourcing problems, bioenergy can represent an attractive solution if effectively managed. The supply chain design supported by mathematical programming can be used as a decision support tool to the successful bioenergy production systems establishment. This strategic decision problem is addressed in this paper where we intent to study the design of the residual forestry biomass to bioelectricity production in the Portuguese context. In order to contribute to attain better solutions a mixed integer linear programming (MILP) model is developed and applied in order to optimize the design and planning of the bioenergy supply chain. While minimizing the total supply chain cost the production energy facilities capacity and location are defined. The model also includes the optimal selection of biomass amounts and sources, the transportation modes selection, and links that must be established for biomass transportation and products delivers to markets. Results illustrate the positive contribution of the mathematical programming approach to achieve viable economic solutions. Sensitivity analysis on the most uncertain parameters was performed: biomass availability, transportation costs, fixed operating costs and investment costs. (C) 2015 Elsevier Ltd. All rights reserved.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Invited review: Models for the optimization of regional wastewater treatment systems

European Journal of Operational Research, nº 73 (1994)

Diagnóstico Energético e proposta de melhorias numa unidade de Produção de Peças de Borracha

O consumo de energia de forma irracional acarreta desvantagens a nível económico para o consumidor e problemas ambientais para toda a sociedade, como a escassez de recursos naturais e o aumento da poluição. Neste contexto, a otimização energética na indústria, e em particular no setor das borrachas, é indispensável de forma a utilizar racionalmente a energia e assim contribuir para a viabilidade das empresas. Este trabalho, efetuado na Flexocol - Fábrica de Artefactos de Borracha, Lda., teve como principal objetivo efetuar um levantamento energético à unidade fabril e propor alternativas que permitissem a redução do consumo de energia elétrica. Foi ainda realizado um estudo sobre a possibilidade de substituir o n-hexano, solvente utilizado na limpeza dos moldes, por um solvente mais adequado. O levantamento energético efetuado permitiu identificar o consumo das utilidades existentes na Flexocol. Esta empresa consome gasóleo e energia elétrica, sendo esta última, a forma de energia mais consumida correspondendo a 96%. O consumo global de energia é cerca 151 tep anuais, inferior a 500 tep/ano, ou seja é considerada uma empresa não consumidora intensiva de energia. Com base neste levantamento determinou-se os indicadores de consumo específico de energia e da intensidade carbónica, 2,73 tep/ ton e 1684,5 kg CO2/tep. A análise do consumo de energia elétrica dos diferentes equipamentos permitiu verificar que o setor que mais consome energia elétrica é a Vulcanização com 45,8%, seguido do setor da Mistura e Serralharia com 27,5% e 26,7%, respetivamente. O sistema de iluminação nos vários setores foi também alvo de estudo e permitiu identificar a Vulcanização como o setor com mais consumo e o da Mistura como o que menos consome. O estudo das variáveis anteriormente referidas permitiu apresentar algumas propostas de melhoria. Uma das propostas analisada foi implementação de condensadores no quadro parcial de forma a diminuir a energia reativa. Com esta medida prevê-se uma poupança de 5631 €/ano e um retorno de investimento de 0,045 anos. Foi também analisada relativamente à iluminação a possibilidade de instalação de balastros eletrónicos que conduziria a uma poupança na energia elétrica de cerca 7072 kWh/ano, mas com um retorno de investimento desfavorável. Por último estudou-se o solvente alternativo ao n-hexano. A acetona foi o solvente proposto uma vez que tem as propriedades indicadas para o fim a que se destina.

Evaluation and optimization of a session-based middleware for data management

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

A clustering approach for vehicle routing problems with hard time windows

Dissertação para obtenção do Grau de Mestre em Logica Computicional

Constraint reasoning with local search for continuous optimization

Optimization is a very important field for getting the best possible value for the optimization function. Continuous optimization is optimization over real intervals. There are many global and local search techniques. Global search techniques try to get the global optima of the optimization problem. However, local search techniques are used more since they try to find a local minimal solution within an area of the search space. In Continuous Constraint Satisfaction Problems (CCSP)s, constraints are viewed as relations between variables, and the computations are supported by interval analysis. The continuous constraint programming framework provides branch-and-prune algorithms for covering sets of solutions for the constraints with sets of interval boxes which are the Cartesian product of intervals. These algorithms begin with an initial crude cover of the feasible space (the Cartesian product of the initial variable domains) which is recursively refined by interleaving pruning and branching steps until a stopping criterion is satisfied. In this work, we try to find a convenient way to use the advantages in CCSP branchand- prune with local search of global optimization applied locally over each pruned branch of the CCSP. We apply local search techniques of continuous optimization over the pruned boxes outputted by the CCSP techniques. We mainly use steepest descent technique with different characteristics such as penalty calculation and step length. We implement two main different local search algorithms. We use “Procure”, which is a constraint reasoning and global optimization framework, to implement our techniques, then we produce and introduce our results over a set of benchmarks.

Customer service process optimization at Sonae e MC

Sonae MC is constantly innovating and keeping up with the new market trends, being increasingly focused on E-commerce due to its growing importance. In that area, a telephone line is available to support customers with their problems. However, rare were the cases in which those problems were solved in the first contact. Therefore, the goal of this work was to reengineer these processes to improve the service performance and consequently the customer’s satisfaction. Following an evolutionary approach, improvement opportunities were suggested and if correctly implemented the cases resolution time could decrease 1 day and Sonae MC will save €7.750 per month.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Global vs. local nonlinear optimization techniques for human-like movement of an anthropomorphic robot

In this paper a comparison between using global and local optimization techniques for solving the problem of generating human-like arm and hand movements for an anthropomorphic dual arm robot is made. Although the objective function involved in each optimization problem is convex, there is no evidence that the admissible regions of these problems are convex sets. For the sequence of movements for which the numerical tests were done there were no significant differences between the optimal solutions obtained using the global and the local techniques. This suggests that the optimal solution obtained using the local solver is indeed a global solution.

A new competitive implementation of the electromagnetism-like algorithm for global optimization

The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.