Statistical analysis by using soft computing methods for seawater biodegradability using ZnO photocatalyst

Area of Science:

• Environmental Science
• Water Resource Management
• Materials Science (Photocatalysis)

Background:

• Water quality is crucial for effective water resource management.
• Photocatalysis using ZnO is a sustainable and eco-friendly method for removing persistent pollutants from seawater.
• Seawater contamination poses challenges for desalination and resource utilization.

Purpose of the Study:

• To investigate the degradation of seawater contaminants using ZnO as a photocatalyst under natural sunlight.
• To evaluate the impact of photocatalyst dosage, reaction time, and pH on pollutant removal efficiency.
• To model and optimize the photocatalytic process using Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS).

Main Methods:

• Experimental batch reactor setup for photocatalytic degradation of seawater contaminants.
• Measurement of key water quality parameters: Total Organic Carbon (TOC), Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), and biodegradability (BOD/COD ratio).
• Application of Box-Behnken Design (RSM-BBD) and ANFIS for process modeling and optimization.

Main Results:

• Maximum experimental removal efficiencies achieved: TOC (45.6%), COD (65.4%), BOD (20.01%), and biodegradability (0.038).
• RSM-BBD and ANFIS models showed high accuracy with determination coefficients (R²) of 0.959 and 0.99, respectively.
• ANFIS model provided superior prediction for process optimization, with predicted maximum removal efficiencies: TOC (46.5%), COD (65.4%), BOD (20.4%), and BOD/COD (0.040).

Conclusions:

• ZnO-based photocatalysis is a viable and effective method for treating contaminated seawater.
• The ANFIS model demonstrates superior predictive capability for optimizing photocatalytic processes compared to RSM-BBD.
• Improved biodegradability and reduced fouling characteristics are key benefits for desalination processes.