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|Type:||Artigo de evento|
|Title:||Alternative Two-layer Optimization Approach Of A Three Phase Catalytic Slurry Reactor By Evolutionary Optimization With Genetic Algorithms|
De Vasco Toledo E.C.
Maciel Filho R.
|Abstract:||Nowadays there is a significant incentive to develop optimization strategies, especially those related to real time process integration. In fact, large scale process, in which high production rates are usual, even very few improvements in process performance may bring competitive advantage among production units. Several factors can influence the dynamic behaviour of the multiphase catalytic reactors such as reactant temperature, refrigerant fluid temperature, concentrations, and environmental restrictions, change phase, proprieties variations. The investigation of these factors permits recognition of their influence level on the process performance especially to obtain the desired product.The present work introduces an investigation study of the optimization of a three-phase catalytic slurry reactor with phase changes to determine the optimal operational conditions and, in a second stage, advanced control algorithms will be evaluated. The idea is to define the optimal operational range or the set-points in me optimization layer and then to use them in the advanced control layer. A dynamic heterogeneous mathematical model formulation was used, which basically consists on mass and energy balance equations for the reactants as well as for the catalyst particles involving phase change of both reactants and coolant (Vasco de Toledo et al., 2001 and Mariano et al. 2005). The kinetic law considers the hydrogenation reaction of o-cresol to obtain the 2-methil-cyclo-hexanol, in the presence of the catalyst Ni/SiO2. In this work was used a genetic algorithm (GA) that solves a nonlinear quadratic problem subject to bounds on the variables. In recent years, stochastic search optimizations algorithms such as evolutionary algorithms (EA) have been developed for solving several optimization problems in Chemical engineering (Deb, 2001, Deb et al, 2002). The extension of the explained procedure to n-dimensional array is straightforward. The problem is that the number of vertexes increases exponentially with the number of variables. In cases with many variables, a proper design of experiments will reduce substantially the required effort, and this procedure is used in this work.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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