A comparative study of hybrid artificial neural network models for predicting Cr(VI) adsorption onto activated carbon

A comparative analysis of the application of the standard, hybrid serial gray-box, and hybrid parallel gray-box artificial neural networks was carried out to predict Cr(VI) adsorption by activated carbon. The dataset was developed through batch kinetic experiments with varying operational parameters. The major reaction transport mechanism was found to be intraparticle diffusion while the major attachment mechanism was chemical bonding. Adsorption kinetics was well represented by the diffusion-chemisorption (DC) model while desorption kinetics followed the pseudo-second order model. It was discovered that the overall DC kinetic rate, K DC was inversely proportional to both the effective diffusion coefficient as well as the diffusivity and proves useful to indirectly assess diffusional effects created by changing operational parameters. The DC kinetic model was subsequently instituted into the hybrid models. The development of the hybrid neural networks was built-in with the joint effect of operating parameters of time and particle size for the standard (SANN) and parallel neural network (PANN), together with the kinetic parameters of the DC model such as overall rate, initial rate and sorption capacity for the serial neural network (DC-ANN). The comparative performance of the networks was subsequently evaluated using error functions and the Bland-Altman plot. The hybrid neural networks produced the best prediction to the target data. Sensitivity analysis revealed a substantial positive influence on the DC-ANN model due to the inclusion of the kinetic equation parameters. Consequently, the DC-ANN model required significantly less computational effort.