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|Type:||Artigo de evento|
|Title:||Neural Modeling Of A Continuous Alcoholic Fermentation Process And Its Optimization By Successive Quadratic Programming|
Da Costa A.C.
|Abstract:||This work focuses on the process operation aspects using model-based optimization of an continuous alcoholic fermentation process. A data-driven identification method based on Multilayer Perceptron Neural Network (MLPNN) and optimal design of experiments was development. The neural model is optimized using Successive Quadratic Programming (SQP) to find out the optimal operational conditions so that the conversion is maximized to the defined allowable limits. In order to check the validity of the computational modeling, the results were compared to the optimization of a deterministic model, whose kinetic parameters were experimentally determined. It was observed that the values for productivity and conversion obtained using the MLPNN models are similar to that obtained using the deterministic model.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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