Peanut Husk as an Efficient Biosorbent for Methylene Blue Removal from Wastewater: Kinetic, Isotherm, and Exploratory Artificial Neural Network Analysis

This study explores the adsorption performance of clean peanut husk (CPH) as a sustainable and low-cost biosorbent for removing methylene blue (MB) from aqueous solutions. Batch experiments investigated the effects of dye concentration, biosorbent dosage, pH, particle size, and temperature. FT-IR analysis confirmed the involvement of hydroxyl, carbonyl, and aromatic groups, with spectral changes indicating both physical and chemical adsorption mechanisms. Adsorption capacity increased with initial dye concentration and decreased with larger particle size or excessive adsorbent dosage. The process was highly pH-sensitive, favoring adsorption under neutral to alkaline conditions, while temperature had minimal influence, a finding compatible with a physisorption-dominated contribution to the overall process. Kinetic data were best described by the pseudo-second-order model, and equilibrium behavior was best described by the Langmuir isotherm; however, these mathematical fits are interpreted here as indicative of, rather than definitive proof of, the specific molecular mechanism, and the overall adsorption process is best described as involving a combination of physisorption and chemisorption contributions. The separation factor (RL) values indicated favorable adsorption. Furthermore, an exploratory artificial neural network (ANN) analysis, based on a limited dataset of 39 experimental observations, achieved a high goodness-of-fit (R2 = 0.99) in describing the nonlinear relationships between each individual operational parameter and the adsorption capacity; given the small sample size and limited validation strategy employed, these results should be regarded as a preliminary demonstration of feasibility rather than evidence of robust predictive generalization. The findings support the potential of CPH as an efficient biosorbent for dye-contaminated wastewater.