Medidas de dispersão de gás em células mecânicas industriais e seus condicionantes.

A qualidade da dispersão de gás em células de flotação é comumente caracterizada através de parâmetros como velocidade superficial do gás (Jg), hold-up do gás (?g), distribuição de tamanho de bolha (db ou D3,2) e fluxo de superfície de bolha (Sb). Sendo um processo de separação de minerais que é dependente da interação (colisão + adesão) entre partículas hidrofóbicas e bolhas de ar, a flotação tem seu desempenho dependente de uma dispersão de gás apropriada na polpa de minério. Desta forma, este trabalho objetivou caracterizar o estado da dispersão de gás de duas células em um banco composto por quatro células Wemco de 42,5 m³ (subaeradas), operando em série na usina da Vale Fertilizantes (Cajati-SP). Realizaram-se três campanhas de medidas que foram conduzidas sob diferentes condições operacionais: a) Diâmetro do rotor (D) de 1,09 m e rotação (N) entre 145 RPM e 175 RPM; b) D = 0,99 m e N entre 110 RPM e 190 RPM; c) D = 0,99 m e N de 120 RPM e de 130 RPM. Observaram-se os seguintes valores de dispersão de gás: 0,7 <= Jg <= 5,4 cm/s, 7 <= ?g <= 15%, 1,6 <= D3,2 <= 2,4 mm e Sb na faixa de 24 a 162 s-1. A magnitude de Jg medida na 1ª e 2ª campanhas mostrou-se acima dos valores reportados pela literatura, indicando necessidade de modificação de condições operacionais dos equipamentos, assim como cuidadosa manutenção. Posteriormente, a 3ª campanha indicou maior conformidade dos parâmetros de dispersão de gás em relação à literatura, constatando-se uma considerável melhora de desempenho do processo de flotação.

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.