Modeling and Optimization of Hexavalent Chromium Adsorption by Activated Eucalyptus Biochar Using Response Surface Methodology and Adaptive Neuro-Fuzzy Inference System

Abstract

Due to its excellent textural features, non-toxicity, low cost and high uptake capacity, biochar has been synthesized from various biomasses and utilized as a biosorbent to remove hexavalent chromium (Cr6+) from contaminated water. Herein, activated eucalyptus biochar (AEB) was prepared via a pyrolysis-chemical activation process and then used as a less expensive biosorbent to adsorb Cr6+ ions from an aqueous solution. Proximate, ultimate, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) analyses were employed in appraising the biosorbent characteristics. Furthermore, response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) were applied to establish the best operating conditions. Based on the results obtained, there was little discrepancy between the observed data and the data predicted by RSM and ANFIS approaches. In terms of prediction accuracy, ANFIS (MAE = 2.512 and R2=0.9200) was superior to RSM (MAE = 2.512 and R2=0.9002). Under best-optimized conditions (initial Cr6+ concentration = 38.14 mg/L, biosorbent dosage = 1.33 g/L and pH = 4.35), which were offered by the ANFIS–ACO technique, the maximum percentage removal of 99.92 ± 0.18% was achieved. The AEB performed exceptionally well due to its better textural characteristics, well-developed porous framework, and dominance of active surface functional groups, which were confirmed by BET, SEM, and FTIR analyses. The comparison of RSM, ACO and GA for process parameter optimization has not been reported in the open literature for Cr6+ adsorption by AEB and hence has been shown in this study.