Modelagem do processo de troca iônica pela Lei da Ação das Massas e redes neurais artificiais

The Mass Action Law is usually employed in modeling of ion exchange processes equilibrium. This methodology is based on the definition of the chemical equilibrium constant and considers the non ideality of solid and aqueous phases. Another alternative to chemical and phase equilibrium modeling is the use of Artificial Neural Networks. This work makes a comparison between both methodologies used on modeling of the equilibrium on ion exchange processes of the binary systems Pb2+-Na+,  Cu2+-Na+ e Na+-Pb2+, and the ternary system Cu2+-Na+- Pb2+ in the conditions of concentration corresponding to 0,005 eq/L and temperature of 303K, using the natural zeolyte clinoptilotita as an ion exchanger. The obtained data by the Mass Action Law from the binary systems were used as an input signal on the Artificial Neural Network training. The used networks had three layers (input, hidden and output layer), and as input signals there were used the concentration and the composition of the ions in solution and as output variable the composition of the ions on the ion exchanger were used. Results have shown that both methodologies were efficient on the binaries systems modeling. Both methodologies were also applied on prediction of the ternary systems behavior from binary systems data. There were made tests with Artificial Neural Networks including the ternary system data on the learning step. The obtained results from non predictive networks on the ternary system equilibrium description were better than those obtained from the Mass Action Law and from predictive networks. Key words: Mass Action Law, artificial neural network, ion exchange.A Lei da Ação das Massas é geralmente empregada na modelagem dos dados experimentais de equilíbrio de processos de troca iônica. Esta metodologia é baseada na definição da constante termodinâmica de equilíbrio químico e considera as não idealidades na fase sólida e na fase aquosa. Outra alternativa para a modelagem de equilíbrio químico e de fases são as Redes Neurais Artificiais. Este trabalho compara ambas as metodologias na modelagem do equilíbrio da troca iônica dos sistemas binário Pb2+-Na+, Cu2+- Na+ e Na+-Pb2+ e do sistema ternário Cu2+-Na+-Pb2+. Na concentração de 0,005 eq/L e temperatura de 303K empregando como trocado iônico a zeólita natural clinoptilotita. Os dados obtidos pela Lei da Ação das Massas nos sistemas binários foram usados como variável de entrada no treinamento da Rede Neural Artificial. As redes utilizadas possuíam três camadas (entrada, oculta e saída), como variáveis de entrada foi utilizadas a concentração e a composição dos íons em solução e como variável resposta a composição dos íons no trocador iônico. Os resultados mostraram que ambas as metodologias foram eficiente na modelagem de sistemas binários. Também foram aplicadas ambas as metodologias na predição do comportamento ternário a partir das informações dos sistemas binários. Ambas as metodologias se mostraram ineficientes na predição dos sistemas ternários. Foram realizados testes com as Redes Neurais com a inclusão de dados experimentais de sistemas ternários na etapa de treinamento. Os resultados obtidos com as redes não preditivas na descrição do equilíbrio do sistema ternário foram superiores aos obtidos com a Lei da Ação das massas e com a rede preditiva. Palavras-chave: lei da ação das massas, rede neural artificial, troca iônica

Comparative study of the adsorption of cadmium and zinc on activated bone char

The goal of this work was to study the adsorption equilibrium of ions Zn2+ and Cd2+ using bovine bone char in fixed bed columns. Dynamic tests were performed with upstream flow fixed bed column, at 30oC and average particle diameter of 0.08 mm. Initially, the optimal operating flow rate was determined, which was 4 mL min.-1 for both metals. The dynamic isotherms, obtained by mass balance in the breakthrough curves, were fitted to the Langmuir and Freundlich models. Simulations of the dynamics of ion adsorption provided satisfactory results, wherein the mass transfer coefficient was directly affected by the inflow concentration of ions, within the range of the study.

Modeling the water uptake by chicken carcasses during cooling by immersion

In this study, water uptake by poultry carcasses during cooling by water immersion was modeled using artificial neural networks. Data from twenty-five independent variables and the final mass of the carcass were collected in an industrial plant to train and validate the model. Different network structures with one hidden layer were tested, and the Downhill Simplex method was used to optimize the synaptic weights. In order to accelerate the optimization calculus, Principal Component Analysis (PCA) was used to preprocess the input data. The obtained results were: i) PCA reduced the number of input variables from twenty-five to ten; ii) the neural network structure 4-6-1 was the one with the best result; iii) PCA gave the following order of importance: parameters of mass transfer, heat transfer, and initial characteristics of the carcass. The main contributions of this work were to provide an accurate model for predicting the final content of water in the carcasses and a better understanding of the variables involved

<b>Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action</b> - doi: 10.4025/actascitechnol.v34i1.9656

The Law of Mass Action generally models the equilibrium data from ion exchange processes. This methodology is rigorous in terms of thermodynamics and takes into consideration the non-idealities in the solid and aqueous phases. However, the artificial neural networks may also be employed in the phase equilibrium modeling. In this study, both methodologies were tested to describe the ion exchange equilibrium in the binary systems SO42--NO3-, SO42--Cl-, NO3-Cl- and in the ternary system SO42--Cl--NO3-, by AMBERLITE IRA 400 resin as ion exchanger. Datasets used in current study were generated by the application of the Law of Mass Action in the binary systems. Results showed that in the equilibrium modeling of binary systems both methodologies had a similar performance. However, in the prediction of the ternary system equilibrium, the Artificial Neural Networks were not efficient. Networks were also trained with the inclusion of ternary experimental data. The Law of Mass Action in the equilibrium modeling of the ternary system was more efficient than Artificial Neural Networks in all cases.</p

The effect of the islet grafts on the non-fasting glycemia of diabetic rats.

<p>The graph shows the islet grafts' effects on the fed blood glucose concentrations during the 3 days after the islets' transplantation. Data represent the mean ± SEM of 7 to 8 rats per experimental group. The following letters represent significant differences between the groups on the indicated days: (a) control, (b) diabetic (STZ), (c) T NP and (d) T LP (p<0.05 by one-way ANOVA).</p

The composition of the normal- and low-protein diets.

<p>*The mixtures of salts and vitamins that were used in the manufacturing of the diet followed the recommendations of the AIN 93 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030685#pone.0030685-deOliveira1" target="_blank">[13]</a>. Values are presented as g/100 g of diet.</p

Insulin secretion in the grafted diabetic rats.

<p>Data represent the mean ± SEM of 7 to 8 rats per experimental group. The inset represents the area under the insulinemic curve during the entire ivGTT timecourse (*p<0.05 by Student's t-test). The insulin levels in the STZ rats were not detectable by this method.</p

The effect of maternal protein restriction on adult rats.

<p>Data represent the mean ± SEM obtained from 30–45 animals per experimental group. Significant differences were determined using Student's t-test with *p<0.001.</p

The effect of the islet grafts on the retroperitoneal fat accumulation of diabetic rats.

<p>The graph shows the effect of grafted islets on the retroperitoneal fat pad. Data represent the mean ± SEM of 7 to 8 rats per group. The following letters represent significant differences between all experimental groups by a one-way ANOVA with p<0.05 indicated by (a) control, (b) diabetic (STZ), (c) T NP and (d) T LP.</p