Decolourization of bromocresol green dye solution by acid functionalized rice husk: Artificial intelligence modeling, GA optimization, and adsorption studies

• Published in: Journal of hazardous materials advances 2023-02, Vol.9, p.100224, Article 100224
• Main Authors: Onu, Chijioke Elijah, Ekwueme, Benjamin Nnamdi, Ohale, Paschal Enyinnaya, Onu, Chiamaka Peace, Asadu, Christian O., Obi, Christopher Chiedozie, Dibia, Kevin Tochukwu, Onu, Ogochukwu Onyinye
• Format: Article
• Language: English
• Subjects: Adsorption; Bromocresol green; Genetic algorithm; Modeling; Optimization; Rice husk
• ISSN: 2772-4166; 2772-4166
• DOI: 10.1016/j.hazadv.2022.100224

•Bromocresol green dye (BCG) was successfully removed from solution.•Waste rice husk was modified via acid functionalization and used as the adsorbent.•Intelligence models like ANFIS, ANN and RSM were used to model the process.•Comparative analysis of the models showed ANFIS as the most proficient.•The adsorption process was endothermic and spontaneous with increased randomness. Novel application and comparison of intelligent models such as adaptive neuro-fuzzy inference systems (ANFIS), artificial neural network (ANN), and response surface methodology (RSM) in adsorptive removal of bromocresol green (BCG) dye via acid functionalized rice husk (AFRH) was the crux of this work. Tetraoxophosphate V acid was used in preparing the AFRH. The effects of process parameters such as initial concentration, solution pH, adsorbent dosage, temperature, and contact time were investigated. The synthesized AFRH was characterized via Scanning Electron Microscope (SEM) and Fourier Transform Infrared (FTIR) spectrophotometer. Genetic algorithm (GA) optimization tool was used in optimizing the process parameters. The result showed that temperature was the most influential parameter in the removal of BCG dye. The predictive modeling of the RSM, ANN and ANFIS models demonstrated good correlation with R2 of 0.9325, 0.9797 and 0.9987 respectively. Low values of calculated error functions of RMSE (ANFIS=0.0025; RSM=0.01899 and ANN=0.01028) and HYBRID (ANFIS=0.0006; RSM=0.03216 and ANN=0.00943) indicated good harmony between experimental values and models’ predictions. Validated GA optimization yielded maximum adsorption capacities of 139.23, 136.29, and 137.14 mg/g for ANFIS-GA, RSM-GA, and ANN-GA respectively. The result showed that the order of the models’ effectiveness for BCG removal is: ANFIS > ANN > RSM. Isotherm study revealed that Freundlich isotherm with R2 of 0.999 best described the equilibrium modeling while the kinetic analysis indicated that pseudo second order (R2 = 0.998) and Elovich (R2 = 0.995) models best accounted for the kinetics of the experimental data. Thermodynamics study denoted that the adsorption process was endothermic and spontaneous. A point of zero charge of 4.65 was obtained. The results of this present work highlighted the potential of the synthesized AFRH in effectively treating BCG dye contaminated wastewater using the optimized conditions. [Display omitted]