Process modeling stategy combining analytical and data based techniques - I. NN identification of reaction rates with known kinetics coefficients

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2007

Autores

Petia Georgieva (Autor) FEUP; Cristina Oliveira (Autor) FEUP; Fernando Rocha (Autor) FEUP; Sebastião Feyo de Azevedo (Autor) FEUP

Ata de Conferência Internacional

Título: IEEE International Joint Conference on Neural Networks

Páginas: 1530-1535

International Joint Conference on Neural Networks

Orlando, Florida, USA, 12 a 17 de Agosto de 2007

Indexação

ISI Proceedings

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia química

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia química

Outras Informações

Abstract (EN): This work deals with the fusion of the data-based and analytical submodels in the process engineering. In contrast to the traditional way of process reaction rates identification by an exhaustive and/or expensive search for the most appropriate parameterized structure, a neural network (NN) based procedure is developed here to identify the reaction rates in the framework of a first principles process model. Since the reaction rates are not measured variables a particular network training structure and algorithm are developed to make possible the supervised NN learning. Our contribution is focused on the general modeling of a class of nonlinear systems representing several industrial processes including crystallization and precipitation, polymerization reactors, distillation columns, biochemical fermentation and biological systems. The proposed algorithm is further applied for estimation of the precipitation rate of calcium phosphate and compared with alternative solutions.

Idioma: Inglês

Tipo (Avaliação Docente): Científica