Arsenate removal using chitosan-coated bentonite via fixed-bed system: a process integration by fuzzy optimization

Area of Science:

• Environmental Science
• Water Treatment Technologies
• Adsorption Processes

Background:

• Groundwater contamination by arsenate poses a global public health and environmental risk.
• Sustainable water treatment necessitates high removal efficiency at minimal cost, particularly for adsorption-based methods.
• Chitosan-coated bentonite (CCB) is explored as a potential adsorbent for arsenate removal.

Purpose of the Study:

• To investigate the adsorption of arsenate from groundwater using CCB in a fixed-bed column.
• To apply fuzzy multi-objective optimization to determine optimal process conditions (flow rate, initial concentration, CCB dosage).
• To analyze the trade-offs between adsorption capacity, energy consumption, and operational costs.

Main Methods:

• Fixed-bed column adsorption experiments.
• Fuzzy multi-objective optimization using LINGO 20 software.
• Box-Behnken design for empirical modeling and Pareto frontier analysis using the ε-constraint method.

Main Results:

• Empirical models were developed to predict adsorption capacity, energy consumption, and cost based on process variables.
• The Pareto frontier illustrated the trade-off between adsorption capacity at breakthrough (12.34–12.96 μg/g) and total operating cost (955.83–1106.32 USD/kg).
• A compromise solution yielded an adsorption capacity of 12.90 μg/g and a total operating cost of 1052.96 USD/kg with 35.46% overall satisfaction.

Conclusions:

• Fuzzy optimization effectively identified optimal conditions for arsenate adsorption using CCB.
• The study provides a strategic decision-making framework for stakeholders implementing fixed-bed adsorption systems.
• This approach balances performance and economic viability for sustainable groundwater remediation.