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

Abstract

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 ¼ 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.

Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

“…In another study, Mandal et al [34] used the ANFIS for modeling the adsorptive removal of As(III) and Cr(VI) ions in batch and column mode using the fibrous zirconium oxide ethylenediamine adipate (ZEDA). Also, Rebouh et al [35] applied the ANFIS technique for the prediction of metal ions removal capacity from aqueous solution by wheat straw biosorbent. Moreover, Qasaimeh et al exploited the ANFIS to assess the conditions required for aquatic systems to serve as a sink for metal ions removal [36].…”

Application of neural network in metal adsorption using biomaterials (BMs): a review

“…The standards for modifying the biomaterials mentioned above are found in the literature. 55,[59][60][61]70,71,80,81,83,84 The physicochemical features of the reviewed modied biomaterial systems were determined by different analytical techniques such as BET, FTIR and SEM-EDS. The morphology of some of the modied BMSs under a scanning electron microscope is shown in Fig.…”

Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix

“…Tanhaei et al [18] described ANFILS as the model best in data prediction with very low error value. It is commonly used in production and other segment of industries [58][59][60][61][62]; agricultural processes [63] and separation processes for water treatment and purification [18,19,[64][65][66][67]. The dearth of insightful information on the response surface methodology (RSM), RSM-genetic algorithm (GA) and adaptive neuro-fuzzy inference logic system (ANFILS) modeling and optimization of the process variables involved in the adsorption of naphthalene adsorption on chitosan-CTAB-bentonite matrix necessitated this study, which, to the best of our knowledge, has not been reported in the literature.…”