Otimização dinâmica de um processo de mistura em linha de diesel

Nos dias atuais, os assuntos concernentes às questões ambientais, principalmente quanto aos impactos ambientais que os derivados do petróleo causam ao meio ambiente, estão cada vez mais presentes na área política, econômica e social. Diante dessa questão, as restrições impostas a esses produtos estão cada vez mais rigorosas, a ponto de afetar as margens de lucro nas refinarias, como também o processo de produção, que convive com a insegurança de obter produtos fora das especificações. Neste contexto, o uso de ferramentas mais sofisticadas, como a otimização dinâmica, é de grande aceitação para contornar estes problemas que não conseguem ser superados pelas tecnologias atuais como a otimização estacionária, devido às suas limitações, que faz com que esta tecnologia seja incapaz de otimizar o processo diante das restrições e de muitas perturbações dinâmicas normalmente presentes. Este trabalho propõe, otimizar dinamicamente um processo de mistura em linha de diesel para a produção de diesel S1800, que é um processo passível de alterações nas margens de lucro de um refinaria e que produz o derivado mais produzido no Brasil. Será avaliado a eficiência da otimização dinâmica com a presença de uma perturbação e também sem a presença da mesma, monitorando as propriedades quanto às restrições impostas pela ANP, na Resolução ANP N° 42

Nonlinear Programming Approaches for Efficient Large-Scale Parameter Estimation with Applications in Epidemiology

The development of infectious disease models remains important to provide scientists with tools to better understand disease dynamics and develop more effective control strategies. In this work we focus on the estimation of seasonally varying transmission parameters in infectious disease models from real measles case data. We formulate both discrete-time and continuous-time models and discussed the benefits and shortcomings of both types of models. Additionally, this work demonstrates the flexibility inherent in large-scale nonlinear programming techniques and the ability of these techniques to efficiently estimate transmission parameters even in very large-scale problems. This computational efficiency and flexibility opens the door for investigating many alternative model formulations and encourages use of these techniques for estimation of larger, more complex models like those with age-dependent dynamics, more complex compartment models, and spatially distributed data. However, the size of these problems can become excessively large even for these powerful estimation techniques, and parallel estimation strategies must be explored. Two parallel decomposition approaches are presented that exploited scenario based decomposition and decomposition in time. These approaches show promise for certain types of estimation problems

Integrated Model-Centric Decision Support System for Process Industries

To bring the advances in modeling, simulation and optimization environments (MSOEs), open-software architectures, and information technology closer to process industries, novel mechanisms and advanced software tools must be devised to simplify the definition of complex model-based problems. Synergistic interactions between complementary model-based software tools must be refined to unlock the potential of model-centric technologies in industries. This dissertation presents the conceptual definition of a single and consistent framework for integrated process decision support (IMCPSS) to facilitate the realistic formulation of related model-based engineering problems. Through the integration of data management, simulation, parameter estimation, data reconciliation, and optimization methods, this framework seeks to extend the viability of model-centric technologies within the industrial workplace. The main contribution is the conceptual definition and implementation of mechanisms to ease the formulation of large-scale data-driven/model-based problems: data model definitions (DMDs), problem formulation objects (PFOs) and process data objects (PDOs). These mechanisms allow the definition of problems in terms of physical variables; to embed plant data seamlessly into model-based problems; and to permit data transfer, re-usability, and synergy among different activities. A second contribution is the design and implementation of the problem definition environment (PDE). The PDE is a robust object-oriented software component that coordinates the problem formulation and the interaction between activities by means of a user-friendly interface. The PDE administers information contained in DMD and coordinates the creation of PFOs and PIFs. Last, this dissertation contributes a systematic integration of data pre-processing and conditioning techniques and MSOEs. The proposed process data management system (pDMS) implements such methodologies. All required manipulations are supervised by the PDE, which represents an important advantage when dealing with high volumes of data. The IMCPSS responds to the need for software tools centered in process engineers for which the complexity of using current modeling environments is a barrier for broader application of model-based activities. Consequently, the IMCPSS represents a valuable tool for process industries, as the facilitation of problem formulation is translated into incorporation of plant data in less error-prone manner, maximization of time dedicated to the analysis of processes, and exploitation of synergy among activities based on process models

Dynamische Optimierung zur Identifikation von Regulationsstrategien des Stoffwechsels

This work analyzes regulatory strategies of metabolic pathways by using a dynamic optimization framework with the quasi-sequential approach. For this purpose it was in a first part necessary to improve the accuracy of state profile approximation and to derive a finite element placement strategy for the quasi-sequential approach. Since the accuracy control is only done in the simulation layer, the nonlinear programming solver need not be restarted and the resulting enhanced accuracy accelerates convergence performance and increases the robustness of the solution to initialization of the parameterised control profiles. The second part consider two studies for the identification of regulatory strategies of unbranched metabolic pathways with the extended quasi-sequential approach: Study A investigates the extent to which transcriptional regulation controls metabolism. With increasing enzyme costs, the optimal regulatory program shifts from a sparse regulation of initial and terminal reactions to a pervasive regulation of all reactions within a pathway. The predicted regulatory strategies were confirmed by a subsystem dependent data analysis of Escherichia coli and can be explained by a trade-off between protein cost minimization and response time optimization due to changes in environmental conditions. Study B investigates the cases where all enzymes are transcriptional regulated. Here it is shown that protein abundance and protein synthesis capacity are key factors that determine the optimal activation strategies. Furthermore, in case of pathways with large differences in protein abundance, complex pathway activation strategies are optimal. Signatures of these pathway activation strategies as well as their dependence on the proposed constraints were confirmed by data analysis for a large number of metabolic pathways in several hundred prokaryotes.In dieser Arbeit werden optimale Regulationsstrategien von Stoffwechselnetzwerken unter Anwendung der quasi-sequentiellen Methode identifiziert und analysiert. Diese Vorgehensweise setzt Erweiterungen der quasi-sequentiellen Methode voraus, welche im ersten Teil der Arbeit beschrieben werden. Die Erweiterungen betreffen eine Kontrolle der Approximationsgenauigkeit der Zustandsverläufe und eine adaptive Diskretisierung während der Lösung des Optimierungsproblems. Hierbei wurde die Approximationskontrolle in der Simulationsschicht der quasi-sequentiellen Methode realisiert. Dies führt dazu, dass gradienten-basierte Lösungsalgorithmen in ihrer iterativen Lösungsstrategie ungehindert fortfahren können und somit keine Neustarts notwendig sind. Weiterhin verbessert die gesicherte Approximationsgenauigkeit die Konvergenzeigenschaften und erhöht die Robustheit gegenüber den Startwertschätzungen der zu optimierenden Steuerungsprofile.Im zweiten Teil wird die Identifikation von Regulationsstrategien des Stoffwechsels mit den obigen Erweiterungen der quasi-sequentiellen Methode für zwei verschiedene Regulationsszenarien durchgeführt. In Szenario A wird durch die Formulierung von Optimalsteuerungsproblemen untersucht, welche Aufgabe die transkriptionelle Regulation bei der Kontrolle von Stoffwechselnetzwerken übernimmt. Es ergibt sich für steigende Kosten der Enzyme ein Umschalten des optimalen regulatorischen Programms von einer dünn verteilten, transkriptionellen Regulation zu einer umfassenden, transkriptionellen Regulation. Die Vorhersagen dieser regulatorischen Strategien wurden durch eine teilsystem-bezogene Datenanalyse in Escherichia coli überprüft und können durch einen Kompromiss zwischen zu minimierenden Kosten für Proteine und einer optimalen Antwortzeit auf veränderte Umweltbedingungen erklärt werden. In Szenario B wird die Situation untersucht, wo alle Enzyme transkriptionell reguliert werden und sich somit der Fokus auf optimale Aktivierungsstrategien verändert. Dabei ergeben sich, in Abhängigkeit der Proteinmassen und der Proteinsynthesekapazität verschiedene Ausprägungen von optimalen Aktivierungsstrategien. Weiterhin ergeben sich für große Unterschiede in den benötigten Proteinmassen komplexe Aktivierungsstrategien. Die Signaturen dieser Aktivierungsstrategien und auch der Einfluss der Beschränkungen wurden in den Regulationen von vielen Stoffwechselnetzwerken in hunderten Prokaryoten nachgewiesen

Nonlinear Programming Approaches for Efficient Large-Scale Parameter Estimation with Applications in Epidemiology

The development of infectious disease models remains important to provide scientists with tools to better understand disease dynamics and develop more effective control strategies. In this work we focus on the estimation of seasonally varying transmission parameters in infectious disease models from real measles case data. We formulate both discrete-time and continuous-time models and discussed the benefits and shortcomings of both types of models. Additionally, this work demonstrates the flexibility inherent in large-scale nonlinear programming techniques and the ability of these techniques to efficiently estimate transmission parameters even in very large-scale problems. This computational efficiency and flexibility opens the door for investigating many alternative model formulations and encourages use of these techniques for estimation of larger, more complex models like those with age-dependent dynamics, more complex compartment models, and spatially distributed data. However, the size of these problems can become excessively large even for these powerful estimation techniques, and parallel estimation strategies must be explored. Two parallel decomposition approaches are presented that exploited scenario based decomposition and decomposition in time. These approaches show promise for certain types of estimation problems

コタイ サンカブツケイ ネンリョウ デンチ ( SOFC ) ハツデン システム ノ ドウテキ サイテキカ

京都大学0048新制・課程博士博士(工学)甲第14172号工博第3006号新制||工||1446(附属図書館)26478UT51-2008-N489京都大学大学院工学研究科化学工学専攻(主査)教授 長谷部 伸治, 教授 三浦 孝一, 教授 前 一廣学位規則第4条第1項該当Doctor of EngineeringKyoto UniversityDFA