Flash Joule heating synthesis of water hyacinth-derived graphene-like carbon for efficient Cr (III) removal: Integrated batch, fluidized bed, and artificial neural network modeling approach

The heavy metal contamination of water is still a major problem in the world because they are toxic, persistent, and they are not easily treated through the conventional treatment methods. In the research, a new and greener adsorbent was obtained through flash Joule heating of water hyacinth biomass, the water hyacinth-based graphene-like carbon (WHAF) was obtained, which had improved physicochemical characteristics. The synthesized WHAF had a much higher surface area and pore structure and thus, it had better adsorption performance in the removal of Cr(III). In optimized batch experiments, removal efficiencies were more than 95%. Freundlich isotherm and pseudo-second-order kinetics were the most appropriate in describing the adsorption process, which showed that there were heterogeneous interactions between the surface and chemisorption. Thermodynamic study indicated that adsorption process is endothermic and becomes more favorable to take place at higher temperatures, which indicates the effect of thermal activation in improving the adsorption process. The experiments performed in a fluidized bed reactor continuously proved that the increase in bed height and the decrease in the flow rate have a great impact on system efficiency, which proves the significance of hydrodynamic conditions. One of the main novelties of the work is the combination of the high level of material synthesis with the model of artificial intelligence. It was found that the developed ANN models had a great predictive performance (R ≈ 1), they were able to capture the nonlinear behavior of the system effectively and were better than the conventional models. The paper presents a scalable framework of sustainable material development, reactor design, and intelligent modeling, which can be a promising solution to the next-generation water treatment technologies.