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

Abstract

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 (c0, mg/L), contact time (tc, h), and temperature (T, °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.

Innovative Materials and Processes for Removal of Biopersistent Pollutants

“…As evidence of the high interest in the topics covered, to date, all the papers have been cited in other works, reaching 18 citations [5], 6 citations [6,7], 5 citations [3,10], 4 citations [4], 2 citations [1,8,9], and 1 citation [2].…”

“…In the second paper by Fertu et al [4], the results of the previous research based on heavy metal retention in soybean and soybean waste biomasses were capitalized. The data were processed by applying a methodology based on ANNs and evolutionary algorithms (EAs), the latter represented by the differential evolution (DE) algorithm.…”

“…A range of different but complementary topics were addressed, from environmental regulation [1] to the development of applications that can be useful in supporting decisions [2][3][4]. Furthermore, many studies analyzed how the modification of some materials can be effective for the reduction in pollutant dispersions [5][6][7][8][9] and the application of an innovative plant configuration [10].…”

Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon

“…Therefore, ANNs have a large area of applicability, being applied to many fields. Examples in the adsorption area include (i) ultrasonic-assisted adsorption [43], (ii) dye adsorption [44], and (iii) heavy metal biosorption [45]. However, despite their advantages and capabilities, ANNs suffer from several drawbacks related to the model type and hyperparameter tuning [46, 47], which depend on the problem’s characteristics.…”

Statistical Analysis and Optimization of the Brilliant Red HE-3B Dye Biosorption onto a Biosorbent Based on Residual Biomass

“…Different models and optimization algorithms have been applied to analyze and estimate biosorbent capacity (q) and biosorption efficiency (R, %). For commercial/industrial exploitation of biosorption, it is crucial to obtain efficient experimental models for biosorption process control and regulation, suitable for large-scale setups [ 10 , 11 , 12 , 13 , 14 , 15 ]. Moreover, it is essential to improve the technological and mathematical optimization [ 14 ], after the active mechanism, involved interactions (adsorbate-biosorbent), and biosorption isotherm, kinetic, and thermodynamic properties have been understood and validated.…”