Diretrizes para o estabelecimento de parcerias entre empresas do real estate residencial.

O presente trabalho tem como foco o estudo e análise das alianças estratégicas realizadas entre empresas do setor do real estate, no período entre 2006 e 2010, enfatizando as alianças realizadas entre empresas atuantes predominantemente nas cidades de São Paulo e Rio de Janeiro com empresas atuantes nas regiões Norte e Nordeste do Brasil, cujo objetivo por parte das empresas paulistas foi o espalhamento geográfico. Considerando o volume representativo de alianças estratégicas verificado no setor no período em questão e a geração de resultados dos empreendimentos objetos de tais parcerias inferiores às expectativas estabelecidas, o objetivo do trabalho é a apresentação de um conjunto de diretrizes que possa contribuir para o planejamento, realização e condução de futuras parcerias entre empresas do setor, visando a mitigar dificuldades e a explorar da melhor forma possível os benefícios que as alianças estratégicas podem proporcionar. Para tanto, realizou-se uma pesquisa por meio de um estudo de casos múltiplos abrangendo o estudo de empresas de capital aberto que atuavam predominantemente nas capitais do eixo Rio-São Paulo, empresas atuantes em nível regional no Norte e Nordeste brasileiro que realizaram parcerias com as empresas do Sudeste, além de empresas de consultoria que estiveram envolvidas nas parcerias por meio de prestação de serviços. Tal pesquisa permitiu identificar as principais dificuldades, vantagens e desvantagens decorrentes das parcerias em questão, cujos dados foram analisados e discutidos à luz da revisão bibliográfica, embasando assim o conjunto de diretrizes proposto. As diretrizes apresentadas visam a contribuir com todo o processo que envolve a realização de uma parceria, contemplando desde aspectos de planejamento, gestão até aspectos operacionais e são complementadas por recomendações que somadas às diretrizes podem elevar a probabilidade de êxito das parcerias.

Real time optimization in chemical process: evaluation of strategies, improvements and industrial application.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process.