Improved neural network with least square support vector machine for wastewater treatment process.

Area of Science:

• Environmental Science
• Water Quality Management
• Machine Learning Applications

Background:

• Biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N) are critical wastewater quality indicators.
• Accurate prediction of these parameters is essential for effective wastewater treatment and environmental surveillance.
• Existing methods may lack the precision or efficiency required for real-time water quality monitoring.

Purpose of the Study:

• To develop and validate a novel predictive model for wastewater discharge indicators, specifically BOD and NH3-N.
• To enhance the accuracy and efficiency of water quality forecasting in wastewater treatment plants.
• To identify key influencing factors for BOD and NH3-N using grey correlation analysis.

Main Methods:

• Data pre-processing and grey correlation analysis to identify significant features impacting BOD and NH3-N.
• Development of an optimized enhanced feed-forward neural network (IFFNN) using machine learning algorithms.
• Prediction of BOD/NH3-N effluent using the IFFNN model, incorporating influent quality, flow rate, and operational data.

Main Results:

• The proposed IFFNN methodology achieved high accuracy in predicting BOD and NH3-N levels.
• The model demonstrated a mean error of less than 10% and an R-squared value of 90%.
• The computational duration was significantly limited compared to existing algorithms.

Conclusions:

• The developed IFFNN model offers a superior approach for wastewater quality prediction.
• The methodology provides a reliable tool for water quality management and surveillance in treatment plants.
• The findings indicate a significant advancement in the accuracy and efficiency of water pollutant forecasting.