Master's Dissertation
DOI
10.11606/D.3.2006.tde-08122006-141824
Document
Author
Full name
Roberto Nicolas De Jardin Júnior
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2006
Supervisor
Committee
Guardani, Roberto (President)
Alves, Rita Maria de Brito
Giulietti, Marco
Title in Portuguese
Modelagem matemática de um processo industrial de produção de cloro e soda por eletrólise de salmoura visando sua otimização.
Keywords in Portuguese
Modelagem e simulação de processo
Otimização de processo
Redes neurais
Abstract in Portuguese
Title in English
Mathematical modeling of an industrial process for chlorine and caustic manufacturing using brine electrolysis aiming at its optimization.
Keywords in English
Neural nets
Process modeling and simulation
Process optimization
Abstract in English
The present work consists on the development of a mathematical model on an industrial chlorine and sodium hydroxide production plant, aiming at the optimization of production efficiency and costs saving concerning electrical energy and vapor consumption. Two process steps were considered in the study: electrolysis and NaOH-liquor concentration by evaporation. Since there are no adequate models reported in the literature for simulating electrolysis-based processes like the one considered, empirical models for the different types of electrolysis cells were developed based on the fitting of neural networks to operational data from industrial operation. In this case, feedforward neural networks containing three neuron layers were fitted to the data. The raw data obtained from industrial operation at Carbocloro plant, in Cubatão ? SP, were first treated by means of multivariate statistical techniques, with the purpose of detecting and eliminating data containing gross errors and outliers, as well as to identify correlations among variables and different operational regimes of the industrial plant. Although material and energy balances for the evaporation step have been initially adopted, this approach could not be used in simulations due to the lack of valid models to predict liquid ? vapor equilibria for the specific system. Thus, a neural network model was also fitted to data from operation of the evaporation step. Fitting of the neural network models resulted in good agreement between model predictions and measured values of the model output variables, and this enabled their use in simulation studies for the electrolysis and evaporation process steps. The neural network-based mathematical model was utilized in process optimization studies aiming at the best financial gain under given operational conditions.