Avaliação da adsorção de indio (III) através de redes neuronais artificiais e modelos difusivos de transferência de massa

Abstract

Technological development has increased the consumption of indium in the last decade and is currently classified as a critical material due to its scarcity. Its main application is related to the production of liquid crystal display. Due to this context, the recycling of indium is necessary. One route is to obtain it after leaching from a liquid crystal display, which can be done through the unitary adsorption operation. This work aimed to investigate the adsorption of In(III) using the neural network and mass transfer models. The adsorption of In (III) was evaluated using ten different adsorbents, activated carbon, multi-walled carbon nanotubes functionalized with OH, carbon nanotubes functionalized with COOH, chitin, chitosan, corn straw, sugarcane bagasse, orange peel and rice husks. The neural network models were able to predict the adsorption capacity for all adsorbents with R2 of 0.9998 and MSE 8.423x10-5 using ANFIS and R2 of 0.9913 and MSE 0.1721 using ANN. Among all ten adsorbents, the multiwalled carbon nanotubes functionalized with OH and another COOH, chitin and chitosan were the ones with the best adsorption capacity. It was found that chitosan has an In(III) adsorption capacity of 1000 mg g-1. Scanning electron microscopy of chitin and chitosan confirmed that both have a rigid surface without the presence of pores. Equilibrium isotherms showed that adsorption capacity increases with temperature. From the external mass transfer model and the modified Biot number values (8.82×10–4 and 2.71) it was found that the external mass transfer is the dominant phenomenon in the adsorption of In(III) onto chitin and chitosan. In summary, the In(III) atoms move out of solution and are instantly adsorbed to the surface through precipitation or coordination bonds.