Mathematical modeling of dispersion in single interface flow analysis

• Tipo: Artigo em Revista Científica Internacional
• Data: 2010

Autores

Sofia S M Rodrigues (Autor) Outra; Karine L Marques (Autor) Outra; Joao A Lopes (Autor) Outra; Joao L M Santos (Autor) FFUP; Jose L F C Lima (Autor) FFUP

Revista

Título: Analytica Chimica Acta

Vol. 663 Nº 2

Páginas: 178-183

ISSN: 0003-2670

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 0 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Química

CORDIS: Ciências da Saúde

Outras Informações

ID Authenticus: P-003-87D

DOI: 10.1016/j.aca.2010.01.051

Resumo (PT): This work describes the optimization of the recently proposed fluid management methodology single interface flow analysis (SIFA) using chemometrics modelling. The influence of the most important physical and hydrodynamic flow parameters of SIFA systems on the axial dispersion coefficients estimated with the axially dispersed plug-flow model, was evaluated with chemometrics linear (multivariate linear regression) and non-linear (simple multiplicative and feed-forward neural networks) models. A D-optimal experimental design built with three reaction coil properties (length, configuration and internal diameter), flow-cell volume and flow rate, was adopted to generate the experimental data. Bromocresol green was used as the dye solution and the analytical signals were monitored by spectrophotometric detection at 614 nm. Results demonstrate that, independent of the model type, the statistically relevant parameters were the reactor coil length and internal diameter and the flow rate. The linear and non-linear multiplicative models were able to estimate the axial dispersion coefficient with validation r2 = 0.86. Artificial neural networks estimated the same parameter with an increased accuracy (r2 = 0.93), demonstrating that relations between the physical parameters and the dispersion phenomena are highly non-linear. The analysis of the response surface control charts simulated with the developed models allowed the interpretation of the relationships between the physical parameters and the dispersion processes. <br> <br> Keywords: Single interface flow analysis; Optimization; Experimental design; Multivariate linear regression; Feed-forward neural networks <br> <a target="_blank" href="http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TF4-4Y9SVWM-2&_user=2460038&_coverDate=03%2F24%2F2010&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000057398&_version=1&_urlVersion=0&_userid=2460038&md5=91e19a6c751163be40b4705bc50f4fa3 "> Texto integral </a> <br> <br>

Abstract (EN): This work describes the optimization of the recently proposed fluid management methodology single interface flow analysis (SIFA) using chemometrics modelling. The influence of the most important physical and hydrodynamic flow parameters of SIFA systems on the axial dispersion coefficients estimated with the axially dispersed plug-flow model, was evaluated with chemometrics linear (multivariate linear regression) and non-linear (simple multiplicative and feed-forward neural networks) models. A D-optimal experimental design built with three reaction coil properties (length, configuration and internal diameter), flow-cell volume and flow rate, was adopted to generate the experimental data. Bromocresol green was used as the dye solution and the analytical signals were monitored by spectrophotometric detection at 614 rim. Results demonstrate that, independent of the model type, the statistically relevant parameters were the reactor coil length and internal diameter and the flow rate. The linear and non-linear multiplicative models were able to estimate the axial dispersion coefficient with validation r(2) = 0.86. Artificial neural networks estimated the same parameter with an increased accuracy (r(2) = 0.93), demonstrating that relations between the physical parameters and the dispersion phenomena are highly non-linear. The analysis of the response surface control charts simulated with the developed models allowed the interpretation of the relationships between the physical parameters and the dispersion processes.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6