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Abstract
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A magnetic nanocomposite material was synthesized by combining Chaetomorpha Algae with a Fe–Ni alloy. The
dry algae, Fe, and Ni were mechanically alloyed using a ball milling technique in an argon atmosphere. The
resulting Algae-Fe-Ni Magnetic Nanocomposite (AFNMN) was characterized using various analytical techniques,
including particle size distribution (PSD) analysis, Brunauer-Emmett-Teller (BET) surface area analysis, scanning
electron microscopy (SEM), differential scanning calorimetry (DSC), vibrating sample magnetometry (VSM),
atomic absorption spectroscopy (AAS), and Fourier transform infrared (FTIR) spectroscopy. Multiple isotherm
models were employed to study the adsorption of Pb(II) ions onto the AFNMN material. Density functional theory
(DFT) was applied to predict the most stable amino acids for the removal of Pb(II) pollutants from a set of 14
amino acids. To optimize the Pb(II) uptake performance, three key experimental parameters were fine-tuned: pH,
temperature, and reactor vessel geometry. Four different optimization methods were utilized, including response
surface methodology (RSM), fuzzy logic, adaptive network-based fuzzy inference system (ANFIS), and artificial
neural network genetic algorithms (ANN-GA). Additionally, a heat transfer simulation was conducted to determine
the optimal vessel positions for achieving the desired temperature variations within the reactor during the
adsorption process.
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