The characterization of binary and ternary ion exchange equilibria

Allen, R. M.; Addison, PA; Dechapunya, A.H.

doi:10.1016/0300-9467(89)80056-5

Cited by 34 publications

(34 citation statements)

References 17 publications

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“…However, reliable theoretical formulations for the calculation of the coefficient of the activity of ions in the solid phase do not exist. Smith and Woodburn (1978) had originally proposed a solution to this problem which was later used by several authors (ALLEN et al, 1989;BOYER et al, 1999;CANEVESI et al, 2009;MEHABLIA et al, 1994;SHALLCROSS et al, 1988) who used Wilson's model for the calculation of the coefficient of activity for fluid phases to represent the non-idealities in the solid phase under analysis. The model's parameter was estimated from equilibrium data.…”

Section: The Law Of Mass Actionmentioning

confidence: 99%

Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action

et al. 2012

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ABSTRACT. 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 SO , 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.Keywords: artificial neural network, mass action law, ion-exchange.Aplicação de redes neurais artificiais e da Lei da Ação das Massas na predição de equilíbrio de sistemas ternários de troca-iônica RESUMO. Os dados de equilíbrio de processos de troca iônica são geralmente modelados pelo emprego de Lei da Ação das Massas. Esta metodologia é rigorosa do ponto de vista termodinâmico e considera as não-idealidades na fase sólida e na fase aquosa. No entanto, as redes neurais artificiais também podem ser empregadas na modelagem de equilíbrio de fases. Neste trabalho, ambas as metodologias foram utilizadas para descrever o equilíbrio na troca iônica nos sistemas binários SO No treinamento da rede foram utilizados os dados gerados pela Aplicação da Lei da Ação das Massa nos sistemas binários. Os resultados obtidos mostraram que na modelagem de equilíbrio dos sistemas binários ambas as metodologias apresentaram desempenho semelhante, entretanto na predição do equilíbrio do sistema ternário as Redes Neurais Artificiais não foram eficientes. Também foram treinadas redes com a inclusão de dados experimentais ternários. Na modelagem do equilíbrio do sistema ternário, a Lei da Ação das Massas foi mais eficiente que as redes neurais em todos os casos.Palavras-chave: redes neurais artificiais, lei da ação das massas, troca iônica.

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Section: The Law Of Mass Actionmentioning

confidence: 99%

Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action

et al. 2012

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“…Nesta abordagem é necessário calcular os coeficientes de atividade dos íons em solução e no trocador iônico. Para o cálculo do coeficiente de atividade dos íons em solução utilizou-se o modelo de Bromley (Bromley, 1973) representado pela seguinte equação: Não existe um modelo desenvolvido para o cálculo do coeficiente de atividade de íons na resina, diversos autores (Smith e Woodburn, 1978;El Prince e Babcock, 1975;Fernández, 2004;Allen et al, 1989;Allen e Addison, 1990;Vo e Shallcross, 2003;Mehablia et al, 1994;Shallcross et al,, 1988) ( ) ( ) Boyer et al, 1999).…”

Section: Modelagem Pela Lei Da Ação Das Massasunclassified

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

Canevesi¹,

Zanella²,

Martins³

et al. 2010

ete

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Resumo AbstractA 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 Networks. This work makes a comparison between both methodologies used on modeling of the equilibrium on ion exchange processes of the binary systems Pb 2+ -Na + ,

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“…The Wilson equation has the second advantage that its original development was as an athermal solution theory, thus making it especially applicable to ion-exchange calculations since heats of reaction in ion-exchange are almost always small (Allen et al, 1989 Elprince and Babcock (1975); Smith and Woodburn (1978); Shallcross et al (1988); Allen et al (1989); Allen and Addison (1990) and Mehablia et al (1992) Rearranging Equation (5.32) yields Equation (5.33). The form of Equation (5.33) was used in the modelling of the Resin-in-Pulp (RIP) slurry mixture system as described in the consolidated model in Chapter 6 of this dissertation.…”

Section: Model Predictions Of the Resin Phase Activity Coefficientsmentioning

confidence: 99%

“…There are several reasons for this. A significant contributor to this difficulty proved to be the sensitivity of the parameter estimation technique to exp~rimental error and the nature in which the objective function's surface plot behaves (Mehablia et al, 1992;Perona, 1993;Allen et al, 1989;Martinez et al, 1994). This difficulty is posed by the nature of the surface of the objective function described by Equation (5.35), Chapter 5.…”

Section: Model Calculations To Determine the Thermodynamic Equilibriumentioning

confidence: 99%

“…The model was successfully applied to the binary and ternary systems of W, Won Amberlite IR-120. Elprince and Babcock (1975), Shallcross et al (1988), Allen et al (1989) and Melis et al (1995) are some of the other numerous investigators who used the thermodynamic equilibrium constant in various forms to model equilibria in various multicomponent ionexchange electrolyte systems.…”

Section: Ion-exchange Affinity and Selectivity Represented By The Sepmentioning

confidence: 99%

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The use of ion-exchange resins for the recovery of valuable species from slurries of sparingly soluble solids

Villiers

Deventer

Lorenzen

1997

Minerals Engineering

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The characterization of binary and ternary ion exchange equilibria

Cited by 34 publications

References 17 publications

Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action

Prediction of ternary ion-exchange equilibrium using artificial neural networks and Law of Mass Action

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

The use of ion-exchange resins for the recovery of valuable species from slurries of sparingly soluble solids

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