Artificial neural network-based surrogate modeling of multi-component dynamic adsorption of heavy metals with a biochar

Publication date: Available online 16 August 2018Source: Journal of Environmental Chemical EngineeringAuthor(s): J. Moreno-Pérez, A. Bonilla-Petriciolet, D.I. Mendoza-Castillo, H.E. Reynel-Ávila, Y. Verde-Gómez, R. Trejo-ValenciaAbstractThis paper reports the application of four neural network surrogate models for the correlation and prediction of asymmetric breakthrough curves obtained from the multi-component adsorption of cadmium, nickel, zinc and copper ions on a biochar. Artificial neural networks namely: Feed forward back propagation neural network, Feed forward back propagation neural network with distributed time delay, Cascade forward neural network and Elman neural network have been assessed and compared where their limitations and capabilities have been discussed. The impact of the architecture of these surrogated models, including the activation functions and training algorithms, has been analyzed using error and residuals analyses in different zones of the adsorption breakthrough curves obtained from single, ternary and quaternary solutions of tested heavy metals. Overall, the bed adsorption capacities for these metals ranged from 2.01 to 5.40, 0.16 to 4.46 and 0.03 to 2.15 mmol/g in single, ternary and quaternary feeds, respectively, at tested operation conditions. Highest adsorption capacities were obtained for copper in single and multi-metallic solutions and they ranged from 2.15 to 5.4 mmol/g. Results of this paper showed that Cascade forward neural n...
Source: Journal of Environmental Chemical Engineering - Category: Chemistry Source Type: research