Accurate data prediction by fuzzy inference model for adsorption of hazardous azo dyes by novel algal doped magnetic chitosan bionanocomposite

A bionanocomposite comprising of magnetic chitosan doped with algae isolated from native habitat was fabri-cated and utilized as an efficient adsorbent for the removal of hazardous azo dyes, namely, Direct Red 31 (DR31) and Direct Red 28 (DR28). The algal doped magnetic chitosan (Alg@mCS) was comprehensively characterized by Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray Analysis (EDAX), Fourier Transform Infrared Spectroscopy (FTIR), X-ray diffraction analysis (XRD), and Brunauer-Emmett-Teller (BET). On the sorption of dyes, the influence of various process variables such as pH, adsorbent dosage, contact time, temperature, and initial dyes concentration were addressed. The adsorbent demonstrated maximal removal of DR31 and DR28 at pH 5 and 3, respectively. The maximum adsorption capacity of DR31 and DR28 was observed at Alg@mCS dose of 0.6 g L-1 and 7 g L-1 in 10 and 20 min, respectively. The Redlich Peterson isotherm model was shown to be appropriate for dye adsorption, indicating monolayer coverage of the dyes on the adsorbent surface (R-2 > 0.99). The adsorption process followed pseudo-second-order kinetics (R-2 > 0.99). Based on 320 experimental datasets from batch studies and interpolated data, adaptive neuro-fuzzy inference system (ANFIS) models were utilized to estimate dye elimination (percent). A number of parameters were calculated to validate the model's applica-bility. The Alg@mCS was proven to be a useful adsorbent for eliminating toxic and harmful azo dyes from aqueous solutions.