Estudo de equilÃbrio de troca iÃnica de sistemas binÃrios e ternÃrios por meio de redes neurais / Ion exchange equilibrium of the binary and ternary systems using neural network and mass action law

AUTOR(ES)
DATA DE PUBLICAÇÃO

2009

RESUMO

In the majority of the applications of the process of ionic exchange in the chemical industry some ionic species are gifts that compete between itself for the active small sieges of the ionic exchanger. Therefore, the project of these systems requires an analysis of the coefficients of selectivity of ions gifts in the solution that determines the influence of the separation process. The data of balance of processes of ionic exchange generally are discrebed for the Law of the Action of the Masses, therefore in this boarding the no-idealists of the phases are consideret watery and solid. The calculation of Balance in systems of ionic exchange in multicomponent systems requires the resolution of a system of not linear equations, and depending on the number of involved species one high computational time cam be required. An alternative to the conventional modelin is the job of Artificial the Neural Nets. Inside of this context, the objective of the present work was to evaluate the application of Artificial the Neural Nets in the modeling of the binary and ternary data of balance in systems of ionic exchange, and also to evaluate the viability to apply Artificial the Neural Nets in the prediction of the data of balance of the ternary systems from information of the binary systems. To evaluate the efficiency of Artificial the Neural Nets in the description of the data of balance of systems of ionic exchange, the gotten results had been compared with the values calculated for the application of the Law of the Action of the Masses. Two experimental data sets of ionic exchange had been used. The first set was constituted of the binary and ternary systems of ions sulphate, chloride and nitrate and as exchanging ion the resin AMBERLITE ANGER 400, with total concentration of 0,2N 298K and had been gotten by SMITH and WOODBURN (1978). As the joint one was constituted of the binary and ternary data of ions of lead, has covered and ionic sodium and as exchanging the clinoptilolita, with 0,005 concentration eq/L and temperature of 303K, gotten for FERNANDEZ (2004). The data of entrance of the net had been the composition of Ãons in solution and of exit they had been the composition of the resin. The training of diverse structures of RNAs was effected. Different architectures had been tested varying the nunber of neurons of the laver of entrance and the occult layer. The nunber of neurons of the entrance layer varied of 2 up to 20 and the occult layer of 1 up to 2, searching always a structure with the lesser value of the objective function. The methods Powell and Simplex had been used to determine the weights of the net. The Law of the Action of the Masses revealed efficient in the description of the following binary systems: SO4-2-NO3-, SO4-2-Cl- e NO3 --Cl-Pb2+-Na+, Cu2+-Na+, however, the results for the system Na+-Pb2+ had not been satisfactory. In the modeling of the binary data Artificial the Neural Nets if had shown efficient in all the investigated cases. In the prediction of the ternary system the Law of the Action of the Masses only revealed efficient for systems SO42--NO3-, SO42-CI- e NO3--CI-. In the prediction of the data of ternary balance for the two evaluatede systems, using Artificial the neural Nets from the binary data generated by the Law of the Action of the Masses, one did not reveal efficient. In the ternary system (SO4Â-, NO3-,CI-) the trained Artificial Neural Nets with the binary data set and the inclusion of ternary experimental data of balance (three and seven data) had obtained to represent with precision the behavior of the system. In the ternary system (Pb+Â. Cu+Â, Na+), hte nets trained from the binary data set and with the inclusion of all the experimental data of the ternary system, the gotten results had been satisfactory, because they had presented errors near by 2% to 6%. Artificial the Neural Nets had not presented predictive capacity to describe the balance in the process of ionic exchange. However, the nets present an advantage in relation the Law of the Action of the Masses, to allow that the compositions of balance of the resin are calculated explicit.

ASSUNTO(S)

sistemas terciÃrios equilÃbrio redes neurais modelagem de dados equilibrium processos industriais de engenharia quimica troca iÃnica modeling modelagem lei da aÃÃo das massas law law mass ion exchnge inteligÃncia artificial sistemas binÃrios artificial neural network processos de separaÃÃo

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