Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm...

Area of Science:

• Environmental Engineering
• Wastewater Treatment Technologies
• Intelligent Control Systems

Background:

• Papermaking wastewater treatment requires precise control of dissolved oxygen (DO) for efficiency.
• Traditional control methods struggle with the complex dynamics of anaerobic/anoxic/oxic (A/O) processes.
• Optimizing DO is crucial for effluent quality and energy management.

Purpose of the Study:

• To develop an on-line hybrid intelligent control system for DO in A/O wastewater treatment.
• To utilize a fuzzy wavelet neural network software sensor evolved by a genetic algorithm (GA).
• To achieve real-time control of DO, improve effluent quality, and minimize energy demand.

Main Methods:

• Implementation of an on-line hybrid intelligent control system.
• Utilizing a fuzzy wavelet neural network software sensor.
• Employing a genetic algorithm (GA) for system evolution and optimization.
• Integration of neural network self-learning, fuzzy logic uncertainty handling, wavelet transform detail analysis, and GA global search.

Main Results:

• The proposed system achieved optimal dissolved oxygen (DO) control in real-time.
• Effective forecasting and control performances were demonstrated.
• Stable effluent quality was maintained at or below desired levels.
• The system proved robust and effective in extracting dynamic behaviors and interrelationships.

Conclusions:

• The hybrid intelligent control system is a robust and effective tool for DO management in papermaking wastewater treatment.
• The approach ensures adequate effluent quality while minimizing energy consumption.
• The system is easily integrated into global monitoring for cost management.