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This study addresses the problem of designing a new natural gas transmission network or expanding an existing network while minimizing the total investment and operating costs. A substantial reduction in costs can be obtained by effectively designing and operating the network. A well-designed network helps natural gas companies minimize the costs while increasing the customer service level. The aim of the study is to determine the optimum installation scheduling and locations of new pipelines and compressor stations. On an existing network, the model also optimizes the total flow through pipelines that satisfy demand to determine the best purchase amount of gas. A mixed integer nonlinear programming model for steady-state natural gas transmission problem on tree-structured network is introduced. The problem is a multi-period model, so changes in the network over a planning horizon can be observed and decisions can be made accordingly in advance. The problem is modeled and solved with easily accessible modeling and solving tools in order to help decision makers to make appropriate decisions in a short time. Various test instances are generated, including problems with different sizes, period lengths and cost parameters, to evaluate the performance and reliability of the model. Test results revealed that the proposed model helps to determine the optimum number of periods in a planning horizon and the crucial cost parameters that affect the network structure the most.

This thesis presents frameworks for the optimal strategic production planning of petroleum refineries operating in competition in multiple markets. The game theoretic concept of the Cournot oligopoly is used as the basic competitive model, and the Nash equilibrium as the solution concept for the formulated problems, which are reformulated into potential games. Nonlinear programming potential game frameworks are developed for static and dynamic production planning problems, as well for mixed integer nonlinear expansion planning problems in which refiners have access to potential upgrades increasing their competitiveness. This latter model represents a novel problem in game theory as it contains both integer and continuous variables and thus must satisfy both discrete and continuous mathematical definitions of the Nash equilibrium. The concept of the mixed-integer game is introduced to explore this problem and the theoretical properties of the new class of games, for which conditions are identified defining when a class of two-player games will possess Nash equilibria in pure strategies, and conjectures offered regarding the properties of larger problems and the class as a whole. In all examples, petroleum refinery problems are solved to optimality (equilibrium) to illustrate the competitive utility of the mathematical frameworks. The primary benefit of such frameworks is the incorporation of the influence of market supply and demand on refinery profits, resulting in rational driving forces in the underlying production planning problems. These results are used to justify the development of frameworks for enterprise optimization as a means of decision making in competitive industries. / Thesis / Doctor of Philosophy (PhD) / This thesis presents a mathematical framework in which refinery production planning problems are solved to optimal solutions in competing scenarios. Concepts from game theory are used to formulate these competitive problems into mathematical programs under single objective functions which coordinate the interests of the competing refiners. Several different cases are considered presenting refinery planning problems as static and dynamic programs in which decisions are time independent or dependent, respectively. A theoretical development is also presented in the concept of the mixed integer game, a game theoretic problem containing both continuous and discrete valued variables and which must satisfy both continuous and discrete definitions of Nash equilibrium. This latter development is used to examine refinery problems in which individual refiners have access to numerous unit upgrades which can potentially improve performance. The results are used to justify a game theoretic approach to enterprise optimization.

<p> Control structure design is an essential step in control system synthesis and has big impact on achievable closed-loop performance. This thesis develops a systematic approach of selecting optimal control structures based on closed-loop dynamic performance and other criteria, such as integrity.</p> <p> The main contribution of this thesis is a rigorous mathematical formulation for control structure design problem that includes full closed-loop transient analysis with additional integrity requirement. The multi-objective framework is extendable so that different control performance objectives can be easily added. Unique process requirements and engineer inputs can be taken into account as additional constraints. The proposed formulation is a Mixed Integer Nonlinear Programming (MINLP) with complementarity constraints. The research scope is limited to linear process models and linear controller algorithms.</p> <p> The tailored solving strategy that makes this challenging problem computationally tractable is introduced in this thesis. The modified Branch and Bound algorithm takes advantage of the special problem structure by using control knowledge to generate valid lower bound efficiently. Prior knowledge can be cooperated as heuristic tuning parameters to guide the solving process so that a reasonably good solution can be found early in the solving process. The complexity study shows the solving strategy can attack design problem size up to 8x8. Considering the percentage of good structures needing evaluation will decrease with problem size even larger problems will be tractable.</p> <p> The common control structures in process industries, such as square and nonsquare Single-Input-Single-Output (SISO) loop pairing using PID controller and block-centralized structure using Model Predictive Controller (MPC), are addressed in this thesis. The usefulness of this research has been demonstrated by several case studies, include Tennessee Eastman problem. The proposed methodology finds a physically sound pairing with good performance for Tennessee Eastman problem in less than one hour, while several off-the-shelf NLP, MINLP and global solvers cannot find a solution in five days.</p> / Thesis / Doctor of Philosophy (PhD)

This thesis focuses on the challenges that arise from the scheduling of heat exchanger network maintenance problems which undergo fouling and run continuously over time. The original contributions of the current research consist of the development of novel optimisation methodologies for the scheduling of cleaning actions in heat exchanger network problems, the application of the novel solution methodology developed to other general maintenance scheduling problems, the development of a stochastic programming formulation using this optimisation technique and its application to these scheduling problems with parametric uncertainty. The work presented in this thesis can be divided into three areas. To efficiently solve this non-convex heat exchanger network maintenance scheduling problem, new optimisation strategies are developed. The resulting contributions are outlined below. In the first area, a novel methodology is developed for the solution of the heat exchanger network maintenance scheduling problems, which is attributed towards a key discovery in which it is observed that these problems exhibit bang-bang behaviour. This indicates that when integrality on the binary decision variables is relaxed, the solution will tend to either the lower or the upper bound specified, obviating the need for integer programming solution techniques. Therefore, these problems are in ac- tuality optimal control problems. To suitably solve these problems, a feasible path sequential mixed integer optimal control approach is proposed. This methodology is coupled with a simple heuristic approach and applied to a range of heat exchanger network case studies from crude oil refinery preheat trains. The demonstrated meth- odology is shown to be robust, reliable and efficient. In the second area of this thesis, the aforementioned novel technique is applied to the scheduling of the regeneration of membranes in reverse osmosis networks which undergo fouling and are located in desalination plants. The results show that the developed solution methodology can be generalised to other maintenance scheduling problems with decaying performance characteristics. In the third and final area of this thesis, a stochastic programming version of the feasible path mixed integer optimal control problem technique is established. This is based upon a multiple scenario approach and is applied to two heat exchanger network case studies of varying size and complexity. Results show that this methodology runs automatically with ease without any failures in convergence. More importantly due to the significant impact on economics, it is vital that uncertainty in data is taken into account in the heat exchanger network maintenance scheduling problem, as well as other general maintenance scheduling problems when there is a level of uncertainty in parameter values.

Inferring and anticipation of genetic networks based on experimental data and environmental measurements is a challenging research problem of mathematical modeling. In this thesis, we discuss gene-environment network models whose dynamics are represented by a class of time-continuous systems of ordinary differential equations containing unknown parameters to be optimized. Accordingly, time-discrete version of that model class is studied and improved by using different numerical methods. In this aspect, 3rd-order Heun&rsquo / s method and 4th-order classical Runge-Kutta method are newly introduced, iteration formulas are derived and corresponding matrix algebras are newly obtained. We use nonlinear mixed-integer programming for the parameter estimation and present the solution of a constrained and regularized given mixed-integer problem. By using this solution and applying the 3rd-order Heun&rsquo / s and 4th-order classical Runge-Kutta methods in the timediscretized model, we generate corresponding time-series of gene-expressions by this thesis. Two illustrative numerical examples are studied newly with an artificial data set and a realworld data set which expresses a real phenomenon. All the obtained approximate results are compared to see the goodness of the new schemes. Different step-size analysis and sensitivity tests are also investigated to obtain more accurate and stable predictions of time-series results for a better service in the real-world application areas. The presented time-continuous and time-discrete dynamical models are identified based on given data, and studied by means of an analytical theory and stability theories of rarefication, regularization and robustification.

With the rise of online retailer giants like Amazon, and enhancements in internet and mobile technologies, online shopping is becoming increasingly popular. This has lead to new opportunities in online price optimization. The overarching motivation and theme of this thesis is to review these opportunities and provide methods and models in the context of retailers' online pricing decisions. In Chapter 2 a multi-period revenue maximization and pricing optimization problem in the presence of reference prices is formulated as a mixed integer nonlinear program. Two algorithms are developed to solve the optimization problem: a generalized Benders' decomposition algorithm and a myopic heuristic. This is followed by numerical computations to illustrate the effciency of the solution approaches as well as some managerial pricing insights. In Chapter 3 a data-driven quadratic programming optimization model for online pricing in the presence of customer ratings is proposed. A new demand function is developed for a multi-product, nite horizon, online retail environment. To solve the optimization problem, a myopic pricing heuristic as well as exact solution approaches are introduced. Using customer reviews ratings data from Amazon.com, a new customer rating forecasting model is validated. This is followed by several analytical and numerical insights. In Chapter 4 a multinomial choice model is used for customer purchase decision to find optimal personalized price discounts for an online retailer that incorporates customer locations and feedback from their reviews. Closed form solutions are derived for two special cases of this problem. To gain some analytical insights extensive numerical experiments are carried followed by several analytical and numerical insights. / Thesis / Doctor of Philosophy (PhD) / The increase in online retail and the improvements in mobile technologies has lead to advantages and opportunities for both customers and retailers. One of these advantages is the ability to keep and efficiently access records of historical orders for both customers and retailers. In addition, online retailing has dramatically decreased the cost of price adjustments and discounts compared to the brick and mortar environment. At the same time, with the increase in online retailing we are witnessing proliferations of online reviews in e-commerce platforms. Given this availability of data and the new capabilities in an online retail environment, there is a need to develop pricing optimization models that integrate all these new features. The overarching motivation and theme of this thesis is to review these opportunities and provide methods and models in the context of retailers' online pricing decisions.

Substâncias tóxicas como o fenol e outros compostos aromáticos dificultam o tratamento de efluentes via digestores biológicos. Estes compostos tóxicos em altas concentrações são nocivos aos lodos biológicos, podendo inviabilizar por completo o tratamento. Nas últimas décadas, os Processos Oxidativos Avançados (POAs), como os processos Fenton e foto- Fenton, surgiram como alternativa para o tratamento de compostos tóxicos. Os POAs degradam os compostos orgânicos pela geração de compostos oxidantes fortes, como o radical hidroxila, a partir de reagentes como peróxido de hidrogênio. Os processos Fenton e foto-Fenton fazem uso de ferro (II), um catalisador relativamente barato, para catalisar a decomposição do peróxido de hidrogênio, reação denominada como reação de Fenton. Em virtude dos complexos mecanismos presentes nos processos Fenton e foto-Fenton, torna-se necessária uma compreensão da cinética do processo, que envolve reações térmicas e fotoquímicas, por meio de sua modelagem matemática fenomenológica. A modelagem da degradação do fenol via processos Fenton e foto-Fenton proposta por este trabalho começa pela estequiometria dos dois processos, que descreve as reações químicas, térmicas e fotoquímicas existentes. A partir destas, é possível desenvolver o modelo cinético dos processos Fenton e foto-Fenton, no qual se determina a velocidade com que estas reações ocorrem. O passo seguinte é o da modelagem hidráulica (ou de escoamento) dos reatores de processo Fenton e foto-Fenton, sendo que para o segundo processo, o modelo deve levar em conta a propagação da radiação por dentro de reator. Foram realizados 3 experimentos de degradação de fenol via processo Fenton para análise das variações das concentrações de fenol, catecol e hidroquinona. Os dados experimentais são comparados com resultados simulados com intuito do ajuste das constantes cinéticas do modelo. Com as constantes ajustadas, são realizadas comparações entre os processos Fenton e foto-Fenton para análise de suas eficiências. A partir dos modelos matemáticos dos reatores de processos Fenton e foto-Fenton, é desenvolvido um modelo de otimização baseado em superestrutura de redes de reatores para a síntese de uma planta de tratamento de efluentes contaminados com fenol. Objetivou-se a redução dos custos de capital, operação e depreciação desta planta, sujeitos às restrições de projeto e ao modelo da superestrutura, resultando em modelos de programação não-linear inteira mista. Foram geradas soluções ótimas para o tratamento de efluentes contaminados com fenol em redes de um, dois e três reatores de POAs. / Toxic substances such as phenol and other aromatic compounds make the wastewater treatment by biological (aerobic or anaerobic) digestors more difficult. These toxic compounds in high concentrations are harmful for the biological sludge and they may render the treatment impractical. In recent decades, Advanced Oxidative Processes (AOPs) appeared as an alternative for the treatment of toxic compounds. AOPs degrade the organic compounds by generating strong oxidizing compounds, such as the hydroxyl radical, from reactants such as hydrogen peroxide. The Fenton and photo-Fenton processes make use of iron (II), a relatively inexpensive catalyst, to catalyze the hydrogen peroxide decomposition, reaction known as the Fenton reaction. Because of the complex nature of the mechanisms that take place in the Fenton and photo-Fenton processes, the understanding of the process kinetics, which involves thermal and photochemical reactions, becomes necessary through its first-principle mathematical modeling. The modeling of phenol degradation by the Fenton and photo-Fenton processes proposed in this work starts with the stoichiometry of the two processes that enumerates the existing thermal and photochemical reactions. Furthermore, it is possible to develop the Fenton and photo- Fenton kinetic model, which determines the reaction rates. The next step is to model the hydraulic (or flow) behavior of the Fenton and photo-Fenton process reactor, whereas the model for the latter must consider how the radiation propagates inside the reactor. Three experiments of the phenol degradation by the Fenton process were carried out to analyze the concentration variation for phenol, catechol and hydroquinone. The experimental data are compared with simulated results aiming the estimation of the kinetic constants of the model. Using the adjusted constants, the Fenton and photo-Fenton processes were compared to analyze their efficiencies. From the mathematical models of the Fenton and photo-Fenton process reactors, an optimization model based on reactor network superstructure is developed for the synthesis of a phenol contaminated wastewater treatment plant. The objective is to minimize the plant capital, operation and depreciation costs, subject to design constraints and to the superstructure model, thus resulting in mixed integer nonlinear programming models. Optimal solutions were generated for the phenol contaminated wastewater treatment in networks with one, two and three AOP reactors.

Die Habilitationsschrift beschäftigt sich mit Theorie, Algorithmen und Software zur Lösung von nichtkonvexen, gemischt-ganzzahligen, nichtlinearen Optimierungsproblemen (MINLP). Sie besteht aus 14 Kapiteln, die in zwei Teile gegliedert sind. Im ersten Teil werden grundlegende Optimierungswerkzeuge beschrieben und im zweiten Teil werden Lösungsalgorithmen vorgestellt. Fast alle vorgeschlagenen Algorithmen wurden als Teil der objektorientierten C++ Bibliothek LaGO implementiert. Numerische Experimente mit verschiedenen MINLP-Problemen zeigen die Möglichkeiten und Grenzen dieser Verfahren. / This book is concerned with theory, algorithms and software for solving nonconvex mixed integer nonlinear programs. It consists of two parts. The first part describes basic optimization tools, such as block-separable reformulations, convex and Lagrangian relaxations, decomposition methods and global optimality criteria. The second part is devoted to algorithms. Starting with a short overview on existing methods, we present deformation, rounding, partitioning and Lagrangian heuristics, and a branch-cut-and-price algorithm. The algorithms are implemented as part of an object-oriented library, called LaGO. We report numerical results on several mixed integer nonlinear programs to show abilities and limits of the proposed solution methods.