Quantificação de açúcares com uma língua eletrónica: calibração multivariada com seleção de sensores

Este trabalho incide na análise dos açúcares majoritários nos alimentos (glucose, frutose e sacarose) com uma língua eletrónica potenciométrica através de calibração multivariada com seleção de sensores. A análise destes compostos permite contribuir para a avaliação do impacto dos açúcares na saúde e seu efeito fisiológico, além de permitir relacionar atributos sensoriais e atuar no controlo de qualidade e autenticidade dos alimentos. Embora existam diversas metodologias analíticas usadas rotineiramente na identificação e quantificação dos açúcares nos alimentos, em geral, estes métodos apresentam diversas desvantagens, tais como lentidão das análises, consumo elevado de reagentes químicos e necessidade de pré-tratamentos destrutivos das amostras. Por isso se decidiu aplicar uma língua eletrónica potenciométrica, construída com sensores poliméricos selecionados considerando as sensibilidades aos açucares obtidas em trabalhos anteriores, na análise dos açúcares nos alimentos, visando estabelecer uma metodologia analítica e procedimentos matemáticos para quantificação destes compostos. Para este propósito foram realizadas análises em soluções padrão de misturas ternárias dos açúcares em diferentes níveis de concentração e em soluções de dissoluções de amostras de mel, que foram previamente analisadas em HPLC para se determinar as concentrações de referência dos açúcares. Foi então feita uma análise exploratória dos dados visando-se remover sensores ou observações discordantes através da realização de uma análise de componentes principais. Em seguida, foram construídos modelos de regressão linear múltipla com seleção de variáveis usando o algoritmo stepwise e foi verificado que embora fosse possível estabelecer uma boa relação entre as respostas dos sensores e as concentrações dos açúcares, os modelos não apresentavam desempenho de previsão satisfatório em dados de grupo de teste. Dessa forma, visando contornar este problema, novas abordagens foram testadas através da construção e otimização dos parâmetros de um algoritmo genético para seleção de variáveis que pudesse ser aplicado às diversas ferramentas de regressão, entre elas a regressão pelo método dos mínimos quadrados parciais. Foram obtidos bons resultados de previsão para os modelos obtidos com o método dos mínimos quadrados parciais aliado ao algoritmo genético, tanto para as soluções padrão quanto para as soluções de mel, com R²ajustado acima de 0,99 e RMSE inferior a 0,5 obtidos da relação linear entre os valores previstos e experimentais usando dados dos grupos de teste. O sistema de multi-sensores construído se mostrou uma ferramenta adequada para a análise dos iii açúcares, quando presentes em concentrações maioritárias, e alternativa a métodos instrumentais de referência, como o HPLC, por reduzir o tempo da análise e o valor monetário da análise, bem como, ter um preparo mínimo das amostras e eliminar produtos finais poluentes.

Aplicación de SPEA2 al cálculo de esquemas de dosificación para el tratamiento quimioterapéutico del cáncer

En este trabajo se aborda la aplicación de SPEA2, un método para optimización multiobjetivo, al cálculo de un esquema de dosificación para el tratamiento quimioterapéutico de una masa tumoral; entiéndase por esquema de dosificación la especificación del o de los agentes cito-tóxicos, sus dosis y tiempos en que deben administrarse. El problema de optimización aquí resuelto es uno multiobjetivo, pues el esquema de dosificación a calcularse debe minimizar no solo el tamaño del tumor, sino también la toxicidad remanente al término del tratamiento, su costo, etc. El SPEA2 es un algoritmo genético que aplica el criterio de Pareto; por lo tanto, lo que calcula es una aproximación a la frontera de Pareto, soluciones de entre las cuales el usuario puede escoger la “mejor”. En el proceso de esta investigación se construyó SoT-Q, una herramienta de software que consiste de dos módulos principales: un optimizador para calcular los esquemas de dosificación óptimos, y un simulador para aplicar dichos esquemas a un paciente (simulado) con masa tumoral; el funcionamiento del simulador se basa en un modelo fármaco-dinámico que representa el tumor. El programa SoT-Q podría en el futuro -una vez extensamente probado y depurado- asistir a médicos oncólogos en la toma de decisiones respecto a tratamientos quimioterapéuticos; o podría servir también como ayuda pedagógica en el entrenamiento de nuevos profesionales de la salud. Los resultados obtenidos fueron muy buenos; en todos los casos de prueba utilizados se logró reducir de manera significativa tanto el tamaño del tumor como la toxicidad remanente al término del tratamiento; en algunos casos la reducción fue de tres órdenes de magnitud.

Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning

Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms.

Network Development with Bicycle Lanes

Bicycling as an active mode of transport can offer great individual and societal benefits. Allocating space for bicycle facilities is the key to promoting cycling as bicyclists perceive better safety and convenience in separate bikeways. In this thesis, a method is proposed for optimizing the selection and scheduling of capacity enhancements in road networks while also optimizing the allocation of road space to bicycle lanes. The goal is to determine what fraction of the available space should be allocated to bicycles, as the network evolves, in order to minimize the present value of the total cost of the system cost. The allocation method is combined with a genetic algorithm to select and schedule road expansion projects under certain budget constraints.

Otimização evolucionária para problemas de transferência de calor em PCI usando MEC

Dissertação (mestrado)—Universidade de Brasília, Faculdade UnB Gama, Programa de Pós-graduação em Integridade de Materiais da Engenharia, 2015.

IMPACT OF PLUG-IN ELECTRIC VEHICLES AND WIND GENERATORS ON HARMONIC DISTORTION OF ELECTRIC DISTRIBUTION SYSTEMS

Harmonic distortion on voltages and currents increases with the increased penetration of Plug-in Electric Vehicle (PEV) loads in distribution systems. Wind Generators (WGs), which are source of harmonic currents, have some common harmonic profiles with PEVs. Thus, WGs can be utilized in careful ways to subside the effect of PEVs on harmonic distortion. This work studies the impact of PEVs on harmonic distortions and integration of WGs to reduce it. A decoupled harmonic three-phase unbalanced distribution system model is developed in OpenDSS, where PEVs and WGs are represented by harmonic current loads and sources respectively. The developed model is first used to solve harmonic power flow on IEEE 34-bus distribution system with low, moderate, and high penetration of PEVs, and its impact on current/voltage Total Harmonic Distortions (THDs) is studied. This study shows that the voltage and current THDs could be increased upto 9.5% and 50% respectively, in case of distribution systems with high PEV penetration and these THD values are significantly larger than the limits prescribed by the IEEE standards. Next, carefully sized WGs are selected at different locations in the 34-bus distribution system to demonstrate reduction in the current/voltage THDs. In this work, a framework is also developed to find optimal size of WGs to reduce THDs below prescribed operational limits in distribution circuits with PEV loads. The optimization framework is implemented in MATLAB using Genetic Algorithm, which is interfaced with the harmonic power flow model developed in OpenDSS. The developed framework is used to find optimal size of WGs on the 34-bus distribution system with low, moderate, and high penetration of PEVs, with an objective to reduce voltage/current THD deviations throughout the distribution circuits. With the optimal size of WGs in distribution systems with PEV loads, the current and voltage THDs are reduced below 5% and 7% respectively, which are within the limits prescribed by IEEE.

Lightweight Middleware for Software Defined Radio (SDR) Inter-Components Communication

The ability to use Software Defined Radio (SDR) in the civilian mobile applications will make it possible for the next generation of mobile devices to handle multi-standard personal wireless devices and ubiquitous wireless devices. The original military standard created many beneficial characteristics for SDR, but resulted in a number of disadvantages as well. Many challenges in commercializing SDR are still the subject of interest in the software radio research community. Four main issues that have been already addressed are performance, size, weight, and power. This investigation presents an in-depth study of SDR inter-components communications in terms of total link delay related to the number of components and packet sizes in systems based on Software Communication Architecture (SCA). The study is based on the investigation of the controlled environment platform. Results suggest that the total link delay does not linearly increase with the number of components and the packet sizes. The closed form expression of the delay was modeled using a logistic function in terms of the number of components and packet sizes. The model performed well when the number of components was large. Based upon the mobility applications, energy consumption has become one of the most crucial limitations. SDR will not only provide flexibility of multi-protocol support, but this desirable feature will also bring a choice of mobile protocols. Having such a variety of choices available creates a problem in the selection of the most appropriate protocol to transmit. An investigation in a real-time algorithm to optimize energy efficiency was also performed. Communication energy models were used including switching estimation to develop a waveform selection algorithm. Simulations were performed to validate the concept.

Ordonnancement des opérations dans une unité d’extrusion

Les travaux de ce mémoire traitent du problème d’ordonnancement et d’optimisation de la production dans un environnement de plusieurs machines en présence de contraintes sur les ressources matérielles dans une usine d’extrusion plastique. La minimisation de la somme pondérée des retards est le critère économique autour duquel s’articule cette étude car il représente un critère très important pour le respect des délais. Dans ce mémoire, nous proposons une approche exacte via une formulation mathématique capable des donner des solutions optimales et une approche heuristique qui repose sur deux méthodes de construction de solution sérielle et parallèle et un ensemble de méthodes de recherche dans le voisinage (recuit-simulé, recherche avec tabous, GRASP et algorithme génétique) avec cinq variantes de voisinages. Pour être en totale conformité avec la réalité de l’industrie du plastique, nous avons pris en considération certaines caractéristiques très fréquentes telles que les temps de changement d’outils sur les machines lorsqu’un ordre de fabrication succède à un autre sur une machine donnée. La disponibilité des extrudeuses et des matrices d’extrusion représente le goulot d’étranglement dans ce problème d’ordonnancement. Des séries d’expérimentations basées sur des problèmes tests ont été effectuées pour évaluer la qualité de la solution obtenue avec les différents algorithmes proposés. L’analyse des résultats a démontré que les méthodes de construction de solution ne sont pas suffisantes pour assurer de bons résultats et que les méthodes de recherche dans le voisinage donnent des solutions de très bonne qualité. Le choix du voisinage est important pour raffiner la qualité de la solution obtenue. Mots-clés : ordonnancement, optimisation, extrusion, formulation mathématique, heuristique, recuit-simulé, recherche avec tabous, GRASP, algorithme génétique

Retrofit de systèmes de revalorisation de chaleur industrielle à basse température par optimisation exergo-économique

Ce projet porte, dans un souci d’efficacité énergétique, sur la récupération d’énergie des rejets thermiques à basse température. Une analyse d’optimisation des technologies dans le but d’obtenir un système de revalorisation de chaleur rentable fait objet de cette recherche. Le but sera de soutirer la chaleur des rejets thermiques et de la réappliquer à un procédé industriel. Réduire la consommation énergétique d’une usine entre habituellement en conflit avec l’investissement requis pour les équipements de revalorisation de chaleur. Ce projet de maitrise porte sur l’application d’optimisations multiobjectives par algorithme génétique (GA) pour faciliter le design en retrofit des systèmes de revalorisation de chaleur industrielle. L’originalité de cette approche consiste à l’emploi du «fast non-dominant sorting genetic algorithm» ou NSGA-II dans le but de trouver les solutions optimales entre la valeur capitale et les pertes exergétiques des réseaux d’échangeurs de chaleur et de pompes à chaleur. Identifier les solutions optimales entre le coût et l’efficacité exergétique peut ensuite aider dans le processus de sélection d’un design approprié en considérant les coûts énergétiques. Afin de tester cette approche, une étude de cas est proposée pour la récupération de chaleur dans une usine de pâte et papier. Ceci inclut l’intégration d’échangeur de chaleur Shell&tube, d’échangeur à contact direct et de pompe à chaleur au réseau thermique existant. Pour l’étude de cas, le projet en collaboration avec Cascades est constitué de deux étapes, soit de ciblage et d’optimisation de solutions de retrofit du réseau d’échangeur de chaleur de l’usine de tissus Cascades à Kinsley Falls. L’étape de ciblage, basée sur la méthode d’analyse du pincement, permet d’identifier et de sélectionner les modifications de topologie du réseau d’échangeurs existant en y ajoutant de nouveaux équipements. Les scénarios résultants passent ensuite à l’étape d’optimisation où les modèles mathématiques pour chaque nouvel équipement sont optimisés afin de produire une courbe d’échange optimal entre le critère économique et exergétique. Pourquoi doubler l’analyse économique d’un critère d’exergie? D’abord, parce que les modèles économiques sont par définition de nature imprécise. Coupler les résultats des modèles économiques avec un critère exergétique permet d’identifier des solutions de retrofit plus efficaces sans trop s’éloigner d’un optimum économique. Ensuite, le rendement exergétique permet d’identifier les designs utilisant l’énergie de haute qualité, telle que l’électricité ou la vapeur, de façon plus efficace lorsque des sources d’énergie de basse qualité, telles que les effluents thermiques, sont disponibles. Ainsi en choisissant un design qui détruit moins d’exergie, il demandera un coût énergétique moindre. Les résultats de l’étude de cas publiés dans l’article montrent une possibilité de réduction des coûts en demande de vapeur de 89% tout en réduisant la destruction d’exergie de 82%. Dans certains cas de retrofit, la solution la plus justifiable économiquement est également très proche de la solution à destruction d’exergie minimale. L’analyse du réseau d’échangeurs et l’amélioration de son rendement exergétique permettront de justifier l’intégration de ces systèmes dans l’usine. Les diverses options pourront ensuite être considérées par Cascades pour leurs faisabilités technologiques et économiques sachant qu’elles ont été optimisées.

Physicochemical complexity in complex chemical systems

Self-replication and compartmentalization are two central properties thought to be essential for minimal life, and understanding how such processes interact in the emergence of complex reaction networks is crucial to exploring the development of complexity in chemistry and biology. Autocatalysis can emerge from multiple different mechanisms such as formation of an initiator, template self-replication and physical autocatalysis (where micelles formed from the reaction product solubilize the reactants, leading to higher local concentrations and therefore higher rates). Amphiphiles are also used in artificial life studies to create protocell models such as micelles, vesicles and oil-in-water droplets, and can increase reaction rates by encapsulation of reactants. So far, no template self-replicator exists which is capable of compartmentalization, or transferring this molecular scale phenomenon to micro or macro-scale assemblies. Here a system is demonstrated where an amphiphilic imine catalyses its own formation by joining a non-polar alkyl tail group with a polar carboxylic acid head group to form a template, which was shown to form reverse micelles by Dynamic Light Scattering (DLS). The kinetics of this system were investigated by 1H NMR spectroscopy, showing clearly that a template self-replication mechanism operates, though there was no evidence that the reverse micelles participated in physical autocatalysis. Active oil droplets, composed from a mixture of insoluble organic compounds in an aqueous sub-phase, can undergo processes such as division, self-propulsion and chemotaxis, and are studied as models for minimal cells, or protocells. Although in most cases the Marangoni effect is responsible for the forces on the droplet, the behaviour of the droplet depends heavily on the exact composition. Though theoretical models are able to calculate the forces on a droplet, to model a mixture of oils on an aqueous surface where compounds from the oil phase are dissolving and diffusing through the aqueous phase is beyond current computational capability. The behaviour of a droplet in an aqueous phase can only be discovered through experiment, though it is determined by the droplet's composition. By using an evolutionary algorithm and a liquid handling robot to conduct droplet experiments and decide which compositions to test next, entirely autonomously, the composition of the droplet becomes a chemical genome capable of evolution. The selection is carried out according to a fitness function, which ranks the formulation based on how well it conforms to the chosen fitness criteria (e.g. movement or division). Over successive generations, significant increases in fitness are achieved, and this increase is higher with more components (i.e. greater complexity). Other chemical processes such as chemiluminescence and gelation were investigated in active oil droplets, demonstrating the possibility of controlling chemical reactions by selective droplet fusion. Potential future applications for this might include combinatorial chemistry, or additional fitness goals for the genetic algorithm. Combining the self-replication and the droplet protocells research, it was demonstrated that the presence of the amphiphilic replicator lowers the interfacial tension between droplets of a reaction mixture in organic solution and the alkaline aqueous phase, causing them to divide. Periodic sampling by a liquid handling robot revealed that the extent of droplet fission increased as the reaction progressed, producing more individual protocells with increased self-replication. This demonstrates coupling of the molecular scale phenomenon of template self-replication to a macroscale physicochemical effect.

Dynamical modeling of biogas production supported by an experimental anaerobic digestion process

This paper presents the development of a combined experimental and numerical approach to study the anaerobic digestion of both the wastes produced in a biorefinery using yeast for biodiesel production and the wastes generated in the preceding microbial biomass production. The experimental results show that it is possible to valorise through anaerobic digestion all the tested residues. In the implementation of the numerical model for anaerobic digestion, a procedure for the identification of its parameters needs to be developed. A hybrid search Genetic Algorithm was used, followed by a direct search method. In order to test the procedure for estimation of parameters, first noise-free data was considered and a critical analysis of the results obtain so far was undertaken. As a demonstration of its application, the procedure was applied to experimental data.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

Implementación de un algoritmo genético para la asignación de aulas en un centro de estudio (ING)

The genetic algorithm is a very efficient tool to solve optimization problems. On the other hand, the classroom assignation in any education center, particularly those that does not have enough quantity of classrooms for the courseʼs demand converts it in an optimization problem. In the Department of Computer Science (Universidad de Costa Rica) this work is carried out manually every six months. Besides, at least two persons of the department are dedicated full time to this labor for one week or more. The present article describes an automatic solution that not only reduces the response time to seconds but it also finds an optimal solution in the majority of the cases. In addition gives flexibility in using the program when the information involved with classroom assignation has to be updated. The interface is simple an easy to use.

Formulations and Metaheuristics for combinatorial optimization problems

Combinatorial optimization problems have been strongly addressed throughout history. Their study involves highly applied problems that must be solved in reasonable times. This doctoral Thesis addresses three Operations Research problems: the first deals with the Traveling Salesman Problem with Pickups and Delivery with Handling cost, which was approached with two metaheuristics based on Iterated Local Search; the results show that the proposed methods are faster and obtain good results respect to the metaheuristics from the literature. The second problem corresponds to the Quadratic Multiple Knapsack Problem, and polynomial formulations and relaxations are presented for new instances of the problem; in addition, a metaheuristic and a matheuristic are proposed that are competitive with state of the art algorithms. Finally, an Open-Pit Mining problem is approached. This problem is solved with a parallel genetic algorithm that allows excavations using truncated cones. Each of these problems was computationally tested with difficult instances from the literature, obtaining good quality results in reasonable computational times, and making significant contributions to the state of the art techniques of Operations Research.

GOOAL: A routine based on Genetic Algorithms to track harmonics in electric power systems

Voltage and current waveforms of a distribution or transmission power system are not pure sinusoids. There are distortions in these waveforms that can be represented as a combination of the fundamental frequency, harmonics and high frequency transients. This paper presents a novel approach to identifying harmonics in power system distorted waveforms. The proposed method is based on Genetic Algorithms, which is an optimization technique inspired by genetics and natural evolution. GOOAL, a specially designed intelligent algorithm for optimization problems, was successfully implemented and tested. Two kinds of representations concerning chromosomes are utilized: binary and real. The results show that the proposed method is more precise than the traditional Fourier Transform, especially considering the real representation of the chromosomes.