Acid Yellow 36, Methyl Red, and Methylene Blue adsorption using ammonia-modified red algae biochar: isotherm, kinetic, regeneration, and ANN studies

Abstract This paper examined the removal of Acid Yellow 36 (AY36), Methyl Red (MR), and Methylene Blue (MB) dyes using a novel Ammonia-decorated Red Algae Biochar (RAB-A) synthesized from red algae ( Pterocladia capillacea ) via a reflux technique in the presence of 25% ammonium hydroxide (NH 4 OH). The physicochemical properties of the synthesized RAB-A, including its surface area, morphology, functional groups, elemental composition, and thermal stability, were comprehensively characterized through Brunauer–Emmett–Teller (BET) analysis, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) integrated with energy-dispersive X-ray (EDX) analysis, and thermogravimetric analysis (TGA). RAB-A demonstrated a low specific surface area (3.262 m 2 /g) and a monolayer adsorption capacity of 0.7495 cm 3 (STP)/g. The adsorbent demonstrated an overall pore volume of 0.011 cm³/g, accompanied by an average pore diameter of 13.648 nm. Thermogravimetric analysis revealed an overall mass loss of 40.84% for RAB-A, demonstrating enhanced thermal stability relative to RAB, which showed a weight loss of 51.05%. FTIR analysis confirmed the presence of diverse functional moieties on the surface of RAB-A. Adsorption experiments targeting Acid Yellow 36 (AY36), Methyl Red (MR), and Methylene Blue (MB) were conducted in batch mode by independently adjusting the initial dye concentration (100–200 mg/L), contact time (5–180 min), solution pH (2–12), and adsorbent dosage (0.5–1.5 g/L). The adsorption equilibrium behavior was best described by the Langmuir isotherm, which indicated maximum uptake capacities of 222.22 mg/g for AY36, 192.31 mg/g for MR dye, and 833.33 mg/g for MB dye. Kinetic analyses revealed that the adsorption of all examined dyes was best described by a pseudo-second-order model, thereby demonstrating the high suitability of the synthesized RAB-A for the efficient elimination of dyes from aqueous solutions. Additionally, adsorption was predicted and adjusted utilizing artificial neural networks (ANN).