Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting...

Area of Science:

• Environmental Chemistry
• Materials Science
• Chemical Engineering

Background:

• Congo red (CR) is a widely used synthetic dye, posing environmental concerns due to its persistence and toxicity.
• Activated biochar derived from lignocellulosic waste offers a sustainable and cost-effective adsorbent material for dye removal.
• Haematoxylum campechianum waste presents an underutilized biomass resource for biochar production.

Purpose of the Study:

• To synthesize and characterize activated biochar from Haematoxylum campechianum waste for Congo red adsorption.
• To investigate the adsorption behavior, kinetics, and isotherms of Congo red onto the prepared activated biochar.
• To explore the application of machine learning for optimizing the dye removal process.

Main Methods:

• Activated biochar was prepared from Haematoxylum campechianum waste using phosphoric acid activation and pyrolysis.
• Adsorption experiments were conducted in batch mode, varying pH, temperature, adsorbent dose, and initial dye concentration.
• Characterization techniques included SEM/EDS, BET, XRD, and FTIR. Adsorption data were fitted to Langmuir and Freundlich isotherms.
• Machine learning models (Gradient Boosting with Bayesian optimization) were employed to predict and optimize removal efficiency.

Main Results:

• The activated biochar (ABHC) exhibited a maximum adsorption capacity (Qmax) of 114.8 mg/g for Congo red at 300.15 K, pH 5.4, and 1.0 g/L adsorbent dose.
• Adsorption followed the Langmuir isotherm model, indicating monolayer adsorption.
• Kinetic studies revealed efficient dye uptake within 48 hours.
• Machine learning optimization predicted a removal efficiency of approximately 90.47% under optimal conditions.

Conclusions:

• Activated biochar from Haematoxylum campechianum waste is a highly effective adsorbent for Congo red.
• The study demonstrates the potential of biochar-based materials for wastewater treatment.
• Machine learning offers a powerful tool for optimizing adsorption processes and advancing environmental remediation strategies.