Neuro-fuzzy modeling of Cu(II) and Cr(VI) adsorption from aqueous solution by wheat straw

In this study, an intelligent architecture-based neuro-fuzzy technique is used for the prediction of removal capacity of Cu(II) and Cr(VI) from aqueous solution using wheat straw as a biosorbent. The effect of operating parameters such as initial pH, temperature, contact time, straw particle size, and biosorbent's chemical treatment are studied to optimize the conditions for maximum removal capacity. A large database is collected from the different carried out experiments to allow the modelization of the adsorption process. This process could be described using classical mathematical models such as Langmuir, Freundlich, or a hybrid model. However, the learnt neuro-fuzzy architecture with the obtained experimental data presents good correlation (R-2 = 0.9999) and shows that the intelligent model is able to predict accurately the removal amount of Cu(II) and Cr(VI) from wheat straw under different conditions.