Biosorption of Pb(II) Using Natural and Treated Ardisia compressa K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm

Abstract

This study explored the effects of solution pH, biosorbent dose, contact time, and temperature on the Pb(II) biosorption process of natural and chemically treated leaves of A. compressa K. (Raw-AC and AC-OH, respectively). The results show that the surface characteristics of Raw-AC changed following alkali treatment. FT-IR analysis showed the presence of various functional groups on the surface of the biosorbent, which were binding sites for the Pb(II) biosorption. The nonlinear pseudo-second-order kinetic model was found to be the best fitted to the experimental kinetic data. Adsorption equilibrium data at pH = 2–6, biosorbents dose from 5 to 20 mg/L, and temperature from 300.15 to 333.15 K were adjusted to the Langmuir, Freundlich, and Dubinin–Radushkevich (D-R) isotherm models. The results show that the adsorption capacity was enhanced with the increase in the solution pH and diminished with the increase in the temperature and biosorbent dose. It was also found that AC-OH is more effective than Raw-AC in removing Pb(II) from aqueous solutions. This was also confirmed using artificial neural networks and genetic algorithms, where it was demonstrated that the improvement was around 57.7%. The nonlinear Langmuir isotherm model was the best fitted, and the maximum adsorption capacities of Raw-AC and AC-OH were 96 mg/g and 170 mg/g, respectively. The removal efficiency of Pb(II) was maintained approximately after three adsorption and desorption cycles using 0.5 M HCl as an eluent. This research delved into the impact of solution pH, biosorbent characteristics, and operational parameters on Pb(II) biosorption, offering valuable insights for engineering education by illustrating the practical application of fundamental chemical and kinetic principles to enhance the design and optimization of sustainable water treatment systems.

Removing Heavy Metals: Cutting-Edge Strategies and Advancements in Biosorption Technology

“…The model efficiently predicted optimal pH, temperature, and contact time, which resulted in a significant increase in biosorption capacity, demonstrating the potential of these computational techniques in improving the efficiency and applicability of biosorption processes. For instance, these models were successfully adapted for the prediction of Pb(II) sorption using natural and treated Ardisia compressa K. leaves [ 85 ], while the efficacy of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques was explored for predicting the removal efficiency of heavy metal ions (lead and nickel) from the active sludge of an industrial wastewater treatment plant [ 86 ]. Experimental parameters, including the pH of the solution, contact time, initial ion concentration, and temperature, were analyzed to determine optimal values.…”

Highly Efficient Adsorption of Pb(II) by Functionalized Humic Acid: Molecular Experiment and Theoretical Calculation

“…The pseudo-first-order (Equation ( 8)) and pseudo-second-order (Equation ( 9)) kinetics models [6] were used to evaluate the kinetic parameters of Pb(II) adsorption on the OBC: [35] ln(q e − q t ) = ln q e − k 1 t (8)…”