Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural...

Area of Science:

• Environmental Science
• Chemical Engineering
• Biotechnology

Background:

• 2,4,6-trinitrotoluene (TNT) is a hazardous nitrogenous pollutant from munitions industries, posing serious health risks.
• TNT-contaminated wastewater requires effective treatment methods to mitigate environmental and health impacts.

Purpose of the Study:

• To optimize the removal of 2,4,6-trinitrotoluene (TNT) from wastewater using an extended aeration activated sludge (EAAS) system.
• To investigate the application of artificial neural network modeling, specifically the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA), for optimizing TNT removal efficiency.

Main Methods:

• Utilized extended aeration activated sludge (EAAS) for TNT removal.
• Employed artificial neural network modeling, including ANFIS for data interpretation and GA for optimization.
• Investigated parameters such as chemical oxygen demand (COD), hydraulic retention time (HRT), and initial TNT concentration.

Main Results:

• The ANFIS model demonstrated high accuracy (97.93%) in analyzing TNT elimination data.
• Genetic algorithms identified optimal conditions for TNT removal.
• Under optimal conditions (10 mg/L TNT, 6h HRT), the EAAS system achieved an 84.25% TNT removal efficiency.
• The optimized EAAS system showed improved capacity for treating higher TNT concentrations compared to previous methods.

Conclusions:

• Artificial neural network-based optimization significantly enhances the effectiveness of EAAS for TNT removal.
• The enhanced EAAS system offers a promising solution for treating industrial wastewater contaminated with 2,4,6-trinitrotoluene.
• This approach can effectively manage wastewaters with higher TNT concentrations, addressing a critical environmental challenge.