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

Abstract

This 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 network was the best model for multi-metallic adsorption breakthrough curve modeling. This neural network showed the lowest modeling errors for the multi-component adsorption breakthrough curves. This paper introduces new results on the application of ANNs surrogate models for the simulation of multi-component adsorption process involved in water treatment and purification.