Modeling the Biosorption Process of Heavy Metal Ions on Soybean-Based Low-Cost Biosorbents Using Artificial Neural Networks

Pollution of the environment with heavy metals requires finding solutions to eliminate them from aqueous flows. The current trends aim at exploiting the advantages of the adsorption operation, by using some low-cost sorbents from agricultural waste biomass, and with good retention capacity of some heavy metal ions. In this context, it is important to provide tools that allow the modeling and optimization of the process, in order to transpose the process to a higher operating scale of the biosorption process. This paper capitalizes on the results of previous research on the biosorption of heavy metal ions, namely Pb(II), Cd(II), and Zn(II) on soybean biomass and soybean waste biomass resulting from biofuels extraction process. The data were processed by applying a methodology based on Artificial Neural Networks (ANNs) and evolutionary algorithms (EAs) capable of evolving ANN parameters. EAs are represented in this paper by the Differential Evolution (DE) algorithm, and a simultaneous training and determination of the topology is performed. The resulting hybrid algorithm, hSADE-NN was applied to obtain optimal models for the biosorption process. The expected response of the system addresses biosorption capacity of the biosorbent (q, mg/g), the biosorption efficiency (E, %), as functions of input parameters: pH, biosorbent dose (DS, mg/g), the initial concentration of metal in the solution (c(0), mg/L), contact time (t(c), h), and temperature (T, degrees C). Models were developed for the two output variables, for each metal ion, finding a high degree of accuracy. Furthermore, the combinations of input parameters were found which can lead to an optimal output in terms of biosorption capacity and biosorption efficiency.

Automated summary

This paper focuses on the modeling and optimization of the biosorption process of heavy metal ions using soybean biomass and soybean waste biomass. By applying a methodology based on Artificial Neural Networks (ANNs) and evolutionary algorithms (EAs), the authors developed optimal models for the biosorption process. The results show high accuracy and identify the input parameters that lead to optimal biosorption capacity and efficiency.