Modeling and Optimization of Heavy Metals Biosorption by Low-Cost Sorbents Using Response Surface Methodology

This paper exploits, through modeling and optimization, the experimental laboratory data on the biosorption of heavy metal ions Pb(II), Cd(II), and Zn(II) from aqueous media using soybean and soybean waste biomasses. The biosorption modeling was performed using the Response Surface Methodology, followed by optimization based on numerical methods. The aim of the modeling was to establish the most probable mathematical relationship between the dependent variables (the biosorption efficiency of the biosorbents when adsorbing metal ions, R(%), and the biosorption capacity of sorbents, q(mg/g)) and the process parameters (pH; sorbent dose, DS (g/L); initial metal ion concentration in solution, c(0) (mg/L); contact time, t(c )(min); temperature, T (degrees C)), validated by methodologies specific to the multiple regression analysis. Afterward, sets of solutions were obtained through optimization that correlate various values of the process parameters to maximize the objective function. These solutions also confirmed the performance of soybean waste biomass in the removal of heavy metal ions from polluted aqueous effluents. The results were validated experimentally.

Automated summary

This study analyzed experimental data on the biosorption of heavy metal ions from aqueous media using soybean and soybean waste biomasses through modeling and optimization. The most probable mathematical relationship was established and optimized to maximize the efficiency of biosorption, with results confirming the performance of soybean waste biomass in removing heavy metal ions from polluted water.