Compatibilidad del principio de óptimo de Bellman con los métodos secuenciales de optimización de redes de tuberías

En el proceso de cálculo de redes de tuberías se maneja un conjunto de variables con unas características muy peculiares, ya que son discretas y estandarizadas. Por lo tanto su evolución se produce por escalones (la presión nominal, el diámetro y el costo de los tubos). Por otro lado la presión de diseño de la red es una función directa de la presión de cabecera. En el proceso de optimización mediante programación dinámica la presión de cabecera se va reduciendo gradualmente en cada secuencia del proceso, haciendo que evolucione a la par la presión de diseño, lo que genera a su vez saltos discriminados en la presión nominal de los tramos, y con ello en su costo y en su gradiente de cambio. En esta tesis doctoral se analiza si estos cambios discriminados que se producen en el gradiente de cambio de algunos tramos en el curso de una secuencia, ocasionados por la evolución de la presión de cabecera de la red, generan interferencias que alteran el proceso secuencial de la programación dinámica. La modificación del gradiente de cambio durante el transcurso de una secuencia se conoce con el nombre de mutación, la cual puede ser activa cuando involucra a un tramo optimo modificando las condiciones de la transacción o pasiva si no crea afección alguna. En el análisis realizado se distingue entre la mutación del gradiente de cambio de los tramos óptimos (que puede generarse exclusivamente en el conjunto de los trayectos que los albergan), y entre los efectos que el cambio de timbraje produce en el resto de los tramos de la red (incluso los situados aguas abajo de los nudos con holgura de presión nula) sobre el mecanismo iterativo, estudiando la compatibilidad de este fenómeno con el principio de óptimo de Bellman. En el proceso de investigación llevado a cabo se destaca la fortaleza que da al proceso secuencial del método Granados el hecho de que el gradiente de cambio siempre sea creciente en el avance hacia el óptimo, es decir que el costo marginal de la reducción de las pérdidas de carga de la red que se consigue en una iteración siempre sea más caro que el de la iteración precedente. Asimismo, en el estudio realizado se revisan los condicionantes impuestos al proceso de optimización, incluyendo algunos que hasta ahora no se han tenido en cuenta en los estudios de investigación, pero que están totalmente integrados en la ingeniería práctica, como es la disposición telescópica de las redes (reordenación de los diámetros de mayor a menor de cabeza a cola de la red), y la disposición de un único diámetro por tramo, en lugar de que estén compartidos por dos diámetros contiguos (con sus salvedades en caso de tramos de gran longitud, o en otras situaciones muy específicas). Finalmente se incluye un capítulo con las conclusiones, aportaciones y recomendaciones, las cuales se consideran de gran utilidad para la ingeniería práctica, entre las que se destaca la perfección del método secuencial, la escasa transcendencia de las mutaciones del gradiente de cambio y la forma en que pueden obviarse, la inocuidad de las mutaciones pasivas y el cumplimiento del principio de Bellman en todo el proceso de optimización. The sizing process of a water distribution network is based on several variables, being some of them special, as they are discrete and their values are standardized: pipe pressure rating, pipe diameter and pipe cost. On another note, the sizing process is directly related with the pressure at the network head. Given that during the optimization by means of the Granados’ Method (based on dynamic programming) the pressure at the network head is being gradually reduced, a jump from one pipe pressure rating to another may arise during the sequential process, leading to changes on the pipe cost and on the gradient change (unitary cost for reducing the head losses). This chain of changes may, in turn, affect the sequential process diverting it from an optimal policies path. This thesis analyses how the abovementioned alterations could influence the results of the dynamic programming algorithm, that is to say the compatibility with the Bellman’s Principle of Optimality, which states that the sequence has to follow a route of optimal policies, and that past decisions should not influence the remaining ones. The modification of the gradient change is known as mutation. Mutations are active when they affect the optimal link (the one which was selected to be changed during iteration) or passive when they do not alter the selection of the optimal link. The thesis analysed the potential mutations processes along the network, both on the optimal paths and also on the rest of the network, and its influence on the final results. Moreover, the investigation analysed the practical restrictions of the sizing process that are fully integrated in the applied engineering, but not always taken into account by the optimization tools. As the telescopic distribution of the diameters (i.e. larger diameters are placed at the network head) and the use of a unique diameter per link (with the exception of very large links, where two consecutive diameters may be placed). Conclusions regarding robustness of the dynamic programming algorithm are given. The sequence of the Granados Method is quite robust and it has been shown capable to auto-correct the mutations that could arise during the optimization process, and to achieve an optimal distribution even when the Bellman’s Principle of Optimality is not fully accomplished. The fact that the gradient change is always increasing during the optimization (that is to say, the marginal cost of reducing head losses is always increasing), provides robustness to the algorithm, as looping are avoided in the optimization sequence. Additionally, insight into the causes of the mutation process is provided and practical rules to avoid it are given, improving the current definition and utilization of the Granados’ Method.

Optimización del consumo de combustible de un vehículo dependiendo de las condiciones de la carretera

El consumo de combustible en un automóvil es una característica que se intenta mejorar continuamente debido a los precios del carburante y a la creciente conciencia medioambiental. Esta tesis doctoral plantea un algoritmo de optimización del consumo que tiene en cuenta las especificaciones técnicas del vehículo, el perfil de orografía de la carretera y el tráfico presente en ella. El algoritmo de optimización calcula el perfil de velocidad óptima que debe seguir el vehículo para completar un recorrido empleando un tiempo de viaje especificado. El cálculo del perfil de velocidad óptima considera los valores de pendiente de la carretera así como también las condiciones de tráfico vehicular de la franja horaria en que se realiza el recorrido. El algoritmo de optimización reacciona ante condiciones de tráfico cambiantes y adapta continuamente el perfil óptimo de velocidad para que el vehículo llegue al destino cumpliendo el horario de llegada establecido. La optimización de consumo es aplicada en vehículos convencionales de motor de combustión interna y en vehículos híbridos tipo serie. Los datos de consumo utilizados por el algoritmo de optimización se obtienen mediante la simulación de modelos cuasi-estáticos de los vehículos. La técnica de minimización empleada por el algoritmo es la Programación Dinámica. El algoritmo divide la optimización del consumo en dos partes claramente diferenciadas y aplica la Programación Dinámica sobre cada una de ellas. La primera parte corresponde a la optimización del consumo del vehículo en función de las condiciones de tráfico. Esta optimización calcula un perfil de velocidad promedio que evita, cuando es posible, las retenciones de tráfico. El tiempo de viaje perdido durante una retención de tráfico debe recuperarse a través de un aumento posterior de la velocidad promedio que incrementaría el consumo del vehículo. La segunda parte de la optimización es la encargada del cálculo de la velocidad óptima en función de la orografía y del tiempo de viaje disponible. Dado que el consumo de combustible del vehículo se incrementa cuando disminuye el tiempo disponible para finalizar un recorrido, esta optimización utiliza factores de ponderación para modular la influencia que tiene cada una de estas dos variables en el proceso de minimización. Aunque los factores de ponderación y la orografía de la carretera condicionan el nivel de ahorro de la optimización, los perfiles de velocidad óptima calculados logran ahorros de consumo respecto de un perfil de velocidad constante que obtiene el mismo tiempo de recorrido. Las simulaciones indican que el ahorro de combustible del vehículo convencional puede lograr hasta un 8.9% mientras que el ahorro de energía eléctrica del vehículo híbrido serie un 2.8%. El algoritmo fusiona la optimización en función de las condiciones del tráfico y la optimización en función de la orografía durante el cálculo en tiempo real del perfil óptimo de velocidad. La optimización conjunta se logra cuando el perfil de velocidad promedio resultante de la optimización en función de las condiciones de tráfico define los valores de los factores de ponderación de la optimización en función de la orografía. Aunque el nivel de ahorro de la optimización conjunta depende de las condiciones de tráfico, de la orografía, del tiempo de recorrido y de las características propias del vehículo, las simulaciones indican ahorros de consumo superiores al 6% en ambas clases de vehículo respecto a optimizaciones que no logran evitar retenciones de tráfico en la carretera. ABSTRACT Fuel consumption of cars is a feature that is continuously being improved due to the fuel price and an increasing environmental awareness. This doctoral dissertation describes an optimization algorithm to decrease the fuel consumption taking into account the technical specifications of the vehicle, the terrain profile of the road and the traffic conditions of the trip. The algorithm calculates the optimal speed profile that completes a trip having a specified travel time. This calculation considers the road slope and the expected traffic conditions during the trip. The optimization algorithm is also able to react to changing traffic conditions and tunes the optimal speed profile to reach the destination within the specified arrival time. The optimization is applied on a conventional vehicle and also on a Series Hybrid Electric vehicle (SHEV). The fuel consumption optimization algorithm uses data obtained from quasi-static simulations. The algorithm is based on Dynamic Programming and divides the fuel consumption optimization problem into two parts. The first part of the optimization process reduces the fuel consumption according to foreseeable traffic conditions. It calculates an average speed profile that tries to avoid, if possible, the traffic jams on the road. Traffic jams that delay drivers result in higher vehicle speed to make up for lost time. A higher speed of the vehicle within an already defined time scheme increases fuel consumption. The second part of the optimization process is in charge of calculating the optimal speed profile according to the road slope and the remaining travel time. The optimization tunes the fuel consumption and travel time relevancies by using two penalty factors. Although the optimization results depend on the road slope and the travel time, the optimal speed profile produces improvements of 8.9% on the fuel consumption of the conventional car and of 2.8% on the spent energy of the hybrid vehicle when compared with a constant speed profile. The two parts of the optimization process are combined during the Real-Time execution of the algorithm. The average speed profile calculated by the optimization according to the traffic conditions provides values for the two penalty factors utilized by the second part of the optimization process. Although the savings depend on the road slope, traffic conditions, vehicle features, and the remaining travel time, simulations show that this joint optimization process can improve the energy consumption of the two vehicles types by more than 6%.

Optimization of water procurement decisions in an irrigation district: the role of option contracts

Water supply instability is one of the main risks faced by irrigation districts and farmers. Water procurement decision optimisation is essential in order to increase supply reliability and reduce costs. Water markets, such as spot purchases or water supply option contracts, can make this decision process more flexible. We analyse the potential interest in an option contract for an irrigation district that has access to several water sources. We apply a stochastic recursive mathematical programming model to simulate the water procurement decisions of an irrigation district?s board operating in a context of water supply uncertainty in south-eastern Spain. We analyse what role different option contracts could play in securing its water supply. Results suggest that the irrigation district would be willing to accept the proposed option contract in most cases subject to realistic values of the option contract financial terms. Of nine different water sources, desalination and the option contract are the main substitutes, where the use of either depends on the contract parameters. The contract premium and optioned volume are the variables that have a greater impact on the irrigation district?s decisions. Key words: Segura Basin, stochastic recursive programming, water markets, water supply option contract, water supply risk.

Estratégias de comercialização e investimento, com ênfase em energias renováveis, suportadas por modelos de otimização especializados para avaliação estocástica de risco x retorno.

A comercialização de energia elétrica de fontes renováveis, ordinariamente, constitui-se uma atividade em que as operações são estruturadas sob condições de incerteza, por exemplo, em relação ao preço \"spot\" no mercado de curto prazo e a geração de energia dos empreendimentos. Deriva desse fato a busca dos agentes pela formulação de estratégias e utilização de ferramentais para auxiliá-los em suas tomadas de decisão, visando não somente o retorno financeiro, mas também à mitigação dos riscos envolvidos. Análises de investimentos em fontes renováveis compartilham de desafios similares. Na literatura, o estudo da tomada de decisão considerada ótima sob condições de incerteza se dá por meio da aplicação de técnicas de programação estocástica, que viabiliza a modelagem de problemas com variáveis randômicas e a obtenção de soluções racionais, de interesse para o investidor. Esses modelos permitem a incorporação de métricas de risco, como por exemplo, o Conditional Value-at-Risk, a fim de se obter soluções ótimas que ponderem a expectativa de resultado financeiro e o risco associado da operação, onde a aversão ao risco do agente torna-se um condicionante fundamental. O objetivo principal da Tese - sob a ótica dos agentes geradores, consumidores e comercializadores - é: (i) desenvolver e implementar modelos de otimização em programação linear estocástica com métrica CVaR associada, customizados para cada um desses agentes; e (ii) aplicá-los na análise estratégica de operações como forma de apresentar alternativas factíveis à gestão das atividades desses agentes e contribuir com a proposição de um instrumento conceitualmente robusto e amigável ao usuário, para utilização por parte das empresas. Nesse contexto, como antes frisado, dá-se ênfase na análise do risco financeiro dessas operações por meio da aplicação do CVaR e com base na aversão ao risco do agente. Considera-se as fontes renováveis hídrica e eólica como opções de ativos de geração, de forma a estudar o efeito de complementaridade entre fontes distintas e entre sites distintos da mesma fonte, avaliando-se os rebatimentos nas operações.

Modern control aspects of automatically steered vehicles. Technical note.

Transportation Department, Secretary of Transportation, Washington, D.C.

An algorithm for evaluating array expressions in OL/2 /

"January, 1971."

Wurst: a protein threading server with a structural scoring function, sequence profiles and optimized substitution matrices

Wurst is a protein threading program with an emphasis on high quality sequence to structure alignments (http://www.zbh.uni-hamburg.de/wurst). Submitted sequences are aligned to each of about 3000 templates with a conventional dynamic programming algorithm, but using a score function with sophisticated structure and sequence terms. The structure terms are a log-odds probability of sequence to structure fragment compatibility, obtained from a Bayesian classification procedure. A simplex optimization was used to optimize the sequence-based terms for the goal of alignment and model quality and to balance the sequence and structural contributions against each other. Both sequence and structural terms operate with sequence profiles.

A numerical and experimental study on energy saving solutions for mobile hydraulic machinery

Energy saving in mobile hydraulic machinery, aimed to fuel consumption reduction, has been one of the principal interests of many researchers and OEMs in the last years. Many different solutions have been proposed and investigated in the literature in order to improve the fuel efficiency, from novel system architectures and strategies to control the system to hybrid solutions. This thesis deals with the energy analysis of a hydraulic system of a middle size excavator through mathematical tools. In order to conduct the analyses the multibody mathematical model of the hydraulic excavator under investigation will be developed and validated on the basis of experimental activities, both on test bench and on the field. The analyses will be carried out considering the typical working cycles of the excavators defined by the JCMAS standard. The simulations results will be analysed and discussed in detail in order to define different solutions for the energy saving in LS hydraulic systems. Among the proposed energy saving solutions, energy recovery systems seem to be very promising for fuel consumption reduction in mobile machinery. In this thesis a novel energy recovery system architecture will be proposed and described in detail. Its dimensioning procedure takes advantage of the dynamic programming algorithm and a prototype will be realized and tested on the excavator under investigation. Finally the energy saving proposed solutions will be compared referring to the standard machinery architecture and a novel hybrid excavator with an energy saving up to 11% will be presented.

Sviluppo e Applicazione di Metodologie per l'Ottimizzazione di Sistemi Energetici

La riduzione dei consumi di combustibili fossili e lo sviluppo di tecnologie per il risparmio energetico sono una questione di centrale importanza sia per l’industria che per la ricerca, a causa dei drastici effetti che le emissioni di inquinanti antropogenici stanno avendo sull’ambiente. Mentre un crescente numero di normative e regolamenti vengono emessi per far fronte a questi problemi, la necessità di sviluppare tecnologie a basse emissioni sta guidando la ricerca in numerosi settori industriali. Nonostante la realizzazione di fonti energetiche rinnovabili sia vista come la soluzione più promettente nel lungo periodo, un’efficace e completa integrazione di tali tecnologie risulta ad oggi impraticabile, a causa sia di vincoli tecnici che della vastità della quota di energia prodotta, attualmente soddisfatta da fonti fossili, che le tecnologie alternative dovrebbero andare a coprire. L’ottimizzazione della produzione e della gestione energetica d’altra parte, associata allo sviluppo di tecnologie per la riduzione dei consumi energetici, rappresenta una soluzione adeguata al problema, che può al contempo essere integrata all’interno di orizzonti temporali più brevi. L’obiettivo della presente tesi è quello di investigare, sviluppare ed applicare un insieme di strumenti numerici per ottimizzare la progettazione e la gestione di processi energetici che possa essere usato per ottenere una riduzione dei consumi di combustibile ed un’ottimizzazione dell’efficienza energetica. La metodologia sviluppata si appoggia su un approccio basato sulla modellazione numerica dei sistemi, che sfrutta le capacità predittive, derivanti da una rappresentazione matematica dei processi, per sviluppare delle strategie di ottimizzazione degli stessi, a fronte di condizioni di impiego realistiche. Nello sviluppo di queste procedure, particolare enfasi viene data alla necessità di derivare delle corrette strategie di gestione, che tengano conto delle dinamiche degli impianti analizzati, per poter ottenere le migliori prestazioni durante l’effettiva fase operativa. Durante lo sviluppo della tesi il problema dell’ottimizzazione energetica è stato affrontato in riferimento a tre diverse applicazioni tecnologiche. Nella prima di queste è stato considerato un impianto multi-fonte per la soddisfazione della domanda energetica di un edificio ad uso commerciale. Poiché tale sistema utilizza una serie di molteplici tecnologie per la produzione dell’energia termica ed elettrica richiesta dalle utenze, è necessario identificare la corretta strategia di ripartizione dei carichi, in grado di garantire la massima efficienza energetica dell’impianto. Basandosi su un modello semplificato dell’impianto, il problema è stato risolto applicando un algoritmo di Programmazione Dinamica deterministico, e i risultati ottenuti sono stati comparati con quelli derivanti dall’adozione di una più semplice strategia a regole, provando in tal modo i vantaggi connessi all’adozione di una strategia di controllo ottimale. Nella seconda applicazione è stata investigata la progettazione di una soluzione ibrida per il recupero energetico da uno scavatore idraulico. Poiché diversi layout tecnologici per implementare questa soluzione possono essere concepiti e l’introduzione di componenti aggiuntivi necessita di un corretto dimensionamento, è necessario lo sviluppo di una metodologia che permetta di valutare le massime prestazioni ottenibili da ognuna di tali soluzioni alternative. Il confronto fra i diversi layout è stato perciò condotto sulla base delle prestazioni energetiche del macchinario durante un ciclo di scavo standardizzato, stimate grazie all’ausilio di un dettagliato modello dell’impianto. Poiché l’aggiunta di dispositivi per il recupero energetico introduce gradi di libertà addizionali nel sistema, è stato inoltre necessario determinare la strategia di controllo ottimale dei medesimi, al fine di poter valutare le massime prestazioni ottenibili da ciascun layout. Tale problema è stato di nuovo risolto grazie all’ausilio di un algoritmo di Programmazione Dinamica, che sfrutta un modello semplificato del sistema, ideato per lo scopo. Una volta che le prestazioni ottimali per ogni soluzione progettuale sono state determinate, è stato possibile effettuare un equo confronto fra le diverse alternative. Nella terza ed ultima applicazione è stato analizzato un impianto a ciclo Rankine organico (ORC) per il recupero di cascami termici dai gas di scarico di autovetture. Nonostante gli impianti ORC siano potenzialmente in grado di produrre rilevanti incrementi nel risparmio di combustibile di un veicolo, è necessario per il loro corretto funzionamento lo sviluppo di complesse strategie di controllo, che siano in grado di far fronte alla variabilità della fonte di calore per il processo; inoltre, contemporaneamente alla massimizzazione dei risparmi di combustibile, il sistema deve essere mantenuto in condizioni di funzionamento sicure. Per far fronte al problema, un robusto ed efficace modello dell’impianto è stato realizzato, basandosi sulla Moving Boundary Methodology, per la simulazione delle dinamiche di cambio di fase del fluido organico e la stima delle prestazioni dell’impianto. Tale modello è stato in seguito utilizzato per progettare un controllore predittivo (MPC) in grado di stimare i parametri di controllo ottimali per la gestione del sistema durante il funzionamento transitorio. Per la soluzione del corrispondente problema di ottimizzazione dinamica non lineare, un algoritmo basato sulla Particle Swarm Optimization è stato sviluppato. I risultati ottenuti con l’adozione di tale controllore sono stati confrontati con quelli ottenibili da un classico controllore proporzionale integrale (PI), mostrando nuovamente i vantaggi, da un punto di vista energetico, derivanti dall’adozione di una strategia di controllo ottima.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Probability distribution modelling to improve stability in nonlinear MIMO control

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

Microcomputers for civil engineering consultancy in developing countries

The civil engineering industry generally regards new methods and technology with a high amount of scepticism, preferring to use traditional and trusted methods. During the 1980s competition for civil engineering consultancy work in the world has become fierce. Halcrow recognised the need to maintain and improve their competitive edge over other consultants. The use of new technology in the form of microcomputers was seen to be one method to maintain and improve their repuation in the world. This thesis examines the role of microcomputers in civil engineering consultancy with particular reference to overseas projects. The involvement of civil engineers with computers, both past and present, has been investigated and a survey of the use of microcomputers by consultancies was carried out, the results are presented and analysed. A resume of the state-of-the-art of microcomputer technology was made. Various case studies were carried out in order to examine the feasibility of using microcomputers on overseas projects. One case study involved the examination of two projects in Bangladesh and is used to illustrate the requirements and problems encountered in such situations. Two programming applications were undertaken, a dynamic programming model of a single site reservoir and the simulation of the Bangladesh gas grid system. A cost-benefit analysis of a water resources project using microcomputers in the Aguan Valley, Honduras was carried out. Although the initial cost of microcomputers is often small, the overall costs can prove to be very high and are likely to exceed the costs of traditional computer methods. A planned approach for the use of microcomputers is essential in order to reap the expected benefits and recommendations for the implementation of such an approach are presented.

On Handling Replay Attacks in Intrusion Detection Systems

We propose a method for detecting and analyzing the so-called replay attacks in intrusion detection systems, when an intruder contributes a small amount of hostile actions to a recorded session of a legitimate user or process, and replays this session back to the system. The proposed approach can be applied if an automata-based model is used to describe behavior of active entities in a computer system.

Automatic ECG Analysis for Preliminary and Detailed Diagnostics Based on Scale-space Representation

A novel approach of automatic ECG analysis based on scale-scale signal representation is proposed. The approach uses curvature scale-space representation to locate main ECG waveform limits and peaks and may be used to correct results of other ECG analysis techniques or independently. Moreover dynamic matching of ECG CSS representations provides robust preliminary recognition of ECG abnormalities which has been proven by experimental results.

Quadratic Time Computable Instances of MaxMin and MinMax Area Triangulations of Convex Polygons

We consider the problems of finding two optimal triangulations of a convex polygon: MaxMin area and MinMax area. These are the triangulations that maximize the area of the smallest area triangle in a triangulation, and respectively minimize the area of the largest area triangle in a triangulation, over all possible triangulations. The problem was originally solved by Klincsek by dynamic programming in cubic time [2]. Later, Keil and Vassilev devised an algorithm that runs in O(n^2 log n) time [1]. In this paper we describe new geometric findings on the structure of MaxMin and MinMax Area triangulations of convex polygons in two dimensions and their algorithmic implications. We improve the algorithm’s running time to quadratic for large classes of convex polygons. We also present experimental results on MaxMin area triangulation.