Cr(VI) removal from aqueous solution using green adsorbents in continuous bed column – statistical and GA-ANN hybrid modelling

• Published in: Chemical engineering science Vol. 226; p. 115904
• Main Authors: Nag, Soma, Bar, Nirjhar, Das, Sudip Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 23.11.2020
• Subjects: Column study; GA-ANN; Jackfruit leaf; Mango leaf; Rubber leaf; Column study; Mango leaf; Rubber leaf; Jackfruit leaf; GA-ANN
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2020.115904

The GA-ANN modelling can be considered as a useful supplement for the conventional and completed mathematical models in the prediction of bioprocess parameters. [Display omitted] •Adsorption characteristics for Cr(VI) removal from aqueous solutions is reported.•Breakthrough capacities were investigated.•This study suggests that the leaves for Cr(VI) removal are economically feasible.•Multiple linear regression predicted Cr(VI) removal percentage successfully.•GA-ANN hybrid technique successfully predicted Cr(VI) removal efficiency.•The GA-ANN modelling can be a supplement for the conventional models. Mango, jackfruit, and rubber leaves showed potential for Cr(VI) elimination in a batch mode. The present study was performed in fixed bed downflow columns at multiple flow rates, bed depths, and influent concentrations for Cr(VI) elimination using the above green adsorbents. The experiments were performed at influent flow rates of 5 to 25 ml.min−1, concentrations of 5 to 80 ml.L−1, and bed depths of 3 to 9 cm. The adsorption capacity for reduced flow rates and concentrations were more effective. Well-known kinetic models had been used to fit the experimental data to determine their associated parameters. Thomas model was the best fit, especially for the mango leaf. The Thomas maximum adsorption capacity of mango, jackfruit, and rubber leaves was 69.52, 22.45, and 15.79 mg.g−1, respectively. The multiple linear regression and GA-ANN technique predicted Cr(VI) removal efficiency successfully with a high cross-correlation coefficient.