Copper biosorption over green silver nanocomposite using artificial intelligence and statistical physics formalism

Abstract

The metal uptake capacity of green synthesized silver nanoparticles (AgNPs) and activated carbon (AC) are widespread. The AgNPs loaded AC for the copper ions (Cu2+) biosorption via an artificial neural network (ANN) and statistical physics formalism (SPF) applied at a pressure range of 0.03–0.3 bar and temperatures of 310, 340, and 370 K were studied. For ANN modeling, a multi-layer perceptron was used with five input nodes to obtain biosorption data. The percentage of Cu2+ uptake efficiency was used as an output variable. The transfer function was applied using the Tanh transference function. SPF through grand canonical ensemble provides models for monolayer and multilayer layers of biosorption. These models explain the sorption by calculating the layers involved, adsorbed species, and energy levels. The results demonstrated that the ANN modeling could be quite helpful in predicting biosorption parameters. The effective covariables were dose, pH, and concentration. The ANN models were consistent with experimental values albeit with minor differences. The error values were between −6.5 + 2.0%. Results showed that adsorbate species remain perpendicular to the adsorbent surfaces while macro- and micropore volumes seem critical. These adsorbents carry single or multiple layers of biosorption and tend to fluctuate with variations in temperature. Exothermic and endothermic processes are represented by negative and positive biosorption energies. Energy distributions in sophisticated SPF models confirm surface characteristics and interactions with adsorbates.