In this thesis the design of a MMIC vector modulator operating in 9GHz-10GHz band is investigated and performed. Sub-sections of the vector modulator are 4-port (4.8dB) 1200 phase shift relative to the dedicated port power splitter, digitally controlled variable gain amplifier and the in phase power combiner. Alternative methods are searched in order to implement the structure properly in the given frequency band. The final design is appropriate for MMIC structure. 4-port (4.8dB) 1200 phase shift relative to the dedicated port power splitter is studied. The performance is simulated and optimized first on Microwave Office, then on Advanced Design System (ADS) tools. Various methods to design a digitally controlled variable gain amplifier are studied. The final topology is simulated and optimized on ADS tool. An in phase power combiner is designed. The performance of the combiner is simulated and optimized on ADS tool. Lumped element models are replaced with CASWELL H-40 models to achieve a MMIC structure and a layout is drawn. The finalized vector modulator is simulated and optimized on ADS tool. Key words: MMIC, Vector Modulator, Digitally Controlled Variable Gain Amplifier, Layout

A otimização de processos vem se tornando uma ferramenta fundamental para o aumento da lucratividade das plantas químicas. Diversos métodos de otimização foram propostos ao longo dos anos, sendo que a otimização em tempo real (RTO) é a solução mais consolidada industrialmente, enquanto que o self-optimizing control (SOC) surge como uma alternativa simplificada, com um menor custo de implantação em relação a esse. Neste trabalho são estudados diversos aspectos da metodologia de SOC, iniciando pela análise do impacto do ponto de operação para o desenvolvimento de estruturas de controle auto-otimizáveis. São propostas modificações na formulação do problema de otimização de SOC de modo que as variáveis controladas sejam determinadas no mesmo problema de otimização em que é escolhido o ponto de operação, permitindo a redução da perda do processo. De forma a analisar a influência da dinâmica nos resultados obtidos, é realizado um estudo comparativo da perda gerada no processo ao longo da operação para as estruturas de otimização baseadas em RTO e em SOC. Com base nos resultados obtidos para uma unidade didática, mostra-se que o comportamento dinâmico do distúrbio possui grande influência na escolha da técnica de otimização, quebrando a ideia de que o RTO é um limite superior do SOC. A aplicação industrial das técnicas clássicas de SOC é validada em uma unidade de separação de propeno, baseada em uma unidade real em operação. A partir da modelagem do processo em simulador comercial, foram geradas as variáveis controladas que permitam uma perda aceitável para a unidade, comprovando a viabilidade de implantação da metodologia em unidades reais. / Process optimization has become a fundamental tool for increasing chemical plants profit. Several optimization methods have been proposed over the years, and real-time optimization (RTO) is the most consolidated solution industrially while self-optimizing control (SOC) appears as a simplified alternative with a lower implementation cost. In this work several aspects of SOC methodology are studied, starting from the analysis of the impact of operating point in the development of self-optimizing control structures. Improvements are proposed in SOC optimization problem formulation where controlled variables are determined in the same optimization problem that operating point, thus reducing significantly process loss. In order to analyze the influence of dynamics on the results, a comparative study is accomplished comparing the loss generated in the process throughout the operation for optimization structures based on RTO and SOC. With the results generated for a toy unit, it is shown that the disturbance dynamic behavior has a great influence on choosing the optimization technique, breaking the idea that RTO is an upper limit of SOC. The industrial application of classical SOC techniques is tested on a propylene separation unit, really operating nowadays. The process was modelled in a commercial simulator and with this model it was generated the best set of controlled variables, based on SOC, that achieve an acceptable loss for the unit, showing that the methodology can be applied in in real units.

A otimização de processos vem se tornando uma ferramenta fundamental para o aumento da lucratividade das plantas químicas. Diversos métodos de otimização foram propostos ao longo dos anos, sendo que a otimização em tempo real (RTO) é a solução mais consolidada industrialmente, enquanto que o self-optimizing control (SOC) surge como uma alternativa simplificada, com um menor custo de implantação em relação a esse. Neste trabalho são estudados diversos aspectos da metodologia de SOC, iniciando pela análise do impacto do ponto de operação para o desenvolvimento de estruturas de controle auto-otimizáveis. São propostas modificações na formulação do problema de otimização de SOC de modo que as variáveis controladas sejam determinadas no mesmo problema de otimização em que é escolhido o ponto de operação, permitindo a redução da perda do processo. De forma a analisar a influência da dinâmica nos resultados obtidos, é realizado um estudo comparativo da perda gerada no processo ao longo da operação para as estruturas de otimização baseadas em RTO e em SOC. Com base nos resultados obtidos para uma unidade didática, mostra-se que o comportamento dinâmico do distúrbio possui grande influência na escolha da técnica de otimização, quebrando a ideia de que o RTO é um limite superior do SOC. A aplicação industrial das técnicas clássicas de SOC é validada em uma unidade de separação de propeno, baseada em uma unidade real em operação. A partir da modelagem do processo em simulador comercial, foram geradas as variáveis controladas que permitam uma perda aceitável para a unidade, comprovando a viabilidade de implantação da metodologia em unidades reais. / Process optimization has become a fundamental tool for increasing chemical plants profit. Several optimization methods have been proposed over the years, and real-time optimization (RTO) is the most consolidated solution industrially while self-optimizing control (SOC) appears as a simplified alternative with a lower implementation cost. In this work several aspects of SOC methodology are studied, starting from the analysis of the impact of operating point in the development of self-optimizing control structures. Improvements are proposed in SOC optimization problem formulation where controlled variables are determined in the same optimization problem that operating point, thus reducing significantly process loss. In order to analyze the influence of dynamics on the results, a comparative study is accomplished comparing the loss generated in the process throughout the operation for optimization structures based on RTO and SOC. With the results generated for a toy unit, it is shown that the disturbance dynamic behavior has a great influence on choosing the optimization technique, breaking the idea that RTO is an upper limit of SOC. The industrial application of classical SOC techniques is tested on a propylene separation unit, really operating nowadays. The process was modelled in a commercial simulator and with this model it was generated the best set of controlled variables, based on SOC, that achieve an acceptable loss for the unit, showing that the methodology can be applied in in real units.

A otimização de processos vem se tornando uma ferramenta fundamental para o aumento da lucratividade das plantas químicas. Diversos métodos de otimização foram propostos ao longo dos anos, sendo que a otimização em tempo real (RTO) é a solução mais consolidada industrialmente, enquanto que o self-optimizing control (SOC) surge como uma alternativa simplificada, com um menor custo de implantação em relação a esse. Neste trabalho são estudados diversos aspectos da metodologia de SOC, iniciando pela análise do impacto do ponto de operação para o desenvolvimento de estruturas de controle auto-otimizáveis. São propostas modificações na formulação do problema de otimização de SOC de modo que as variáveis controladas sejam determinadas no mesmo problema de otimização em que é escolhido o ponto de operação, permitindo a redução da perda do processo. De forma a analisar a influência da dinâmica nos resultados obtidos, é realizado um estudo comparativo da perda gerada no processo ao longo da operação para as estruturas de otimização baseadas em RTO e em SOC. Com base nos resultados obtidos para uma unidade didática, mostra-se que o comportamento dinâmico do distúrbio possui grande influência na escolha da técnica de otimização, quebrando a ideia de que o RTO é um limite superior do SOC. A aplicação industrial das técnicas clássicas de SOC é validada em uma unidade de separação de propeno, baseada em uma unidade real em operação. A partir da modelagem do processo em simulador comercial, foram geradas as variáveis controladas que permitam uma perda aceitável para a unidade, comprovando a viabilidade de implantação da metodologia em unidades reais. / Process optimization has become a fundamental tool for increasing chemical plants profit. Several optimization methods have been proposed over the years, and real-time optimization (RTO) is the most consolidated solution industrially while self-optimizing control (SOC) appears as a simplified alternative with a lower implementation cost. In this work several aspects of SOC methodology are studied, starting from the analysis of the impact of operating point in the development of self-optimizing control structures. Improvements are proposed in SOC optimization problem formulation where controlled variables are determined in the same optimization problem that operating point, thus reducing significantly process loss. In order to analyze the influence of dynamics on the results, a comparative study is accomplished comparing the loss generated in the process throughout the operation for optimization structures based on RTO and SOC. With the results generated for a toy unit, it is shown that the disturbance dynamic behavior has a great influence on choosing the optimization technique, breaking the idea that RTO is an upper limit of SOC. The industrial application of classical SOC techniques is tested on a propylene separation unit, really operating nowadays. The process was modelled in a commercial simulator and with this model it was generated the best set of controlled variables, based on SOC, that achieve an acceptable loss for the unit, showing that the methodology can be applied in in real units.

Waterflooding is a common type of oil recovery techniques where water is pumped into the reservoir for increased productivity. Reservoir states change with time, as such, different injection and production settings will be required to lead the process to optimal operation which is actually a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is not appropriate for reservoir production due to numerous uncertain properties involved. Models that are updated through the current industrial practice of ‘history matching’ may fail to predict reality correctly and therefore, solutions based on history-matched models may be suboptimal or non-optimal at all. Due to its ability in counteracting the effects uncertainties, direct feedback control has been proposed recently for optimal waterflooding operations. In this work, two feedback approaches were developed for waterflooding process optimization. The first approach is based on the principle of receding horizon control (RHC) while the second is a new dynamic optimization method developed from the technique of self-optimizing control (SOC). For the SOC methodology, appropriate controlled variables (CVs) as combinations of measurement histories and manipulated variables are first derived through regression based on simulation data obtained from a nominal model. Then the optimal feedback control law was represented as a linear function of measurement histories from the CVs obtained. Based on simulation studies, the RHC approach was found to be very sensitive to uncertainties when the nominal model differed significantly from the conceived real reservoir. The SOC methodology on the other hand, was shown to achieve an operational profit with only 2% worse than the true optimal control, but 30% better than the open-loop optimal control under the same uncertainties. The simplicity of the developed SOC approach coupled with its robustness to handle uncertainties proved its potentials to real industrial applications.

Waterflooding is a common type of oil recovery techniques where water is pumped into the reservoir for increased productivity. Reservoir states change with time, as such, different injection and production settings will be required to lead the process to optimal operation which is actually a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is not appropriate for reservoir production due to numerous uncertain properties involved. Models that are updated through the current industrial practice of ‘history matching’ may fail to predict reality correctly and therefore, solutions based on history-matched models may be suboptimal or non-optimal at all. Due to its ability in counteracting the effects uncertainties, direct feedback control has been proposed recently for optimal waterflooding operations. In this work, two feedback approaches were developed for waterflooding process optimization. The first approach is based on the principle of receding horizon control (RHC) while the second is a new dynamic optimization method developed from the technique of self-optimizing control (SOC). For the SOC methodology, appropriate controlled variables (CVs) as combinations of measurement histories and manipulated variables are first derived through regression based on simulation data obtained from a nominal model. Then the optimal feedback control law was represented as a linear function of measurement histories from the CVs obtained. Based on simulation studies, the RHC approach was found to be very sensitive to uncertainties when the nominal model differed significantly from the conceived real reservoir. The SOC methodology on the other hand, was shown to achieve an operational profit with only 2% worse than the true optimal control, but 30% better than the open-loop optimal control under the same uncertainties. The simplicity of the developed SOC approach coupled with its robustness to handle uncertainties proved its potentials to real industrial applications.