Energy consumption model for wastewater treatment process control.

Area of Science:

• Environmental Engineering
• Artificial Intelligence in Water Treatment
• Wastewater Treatment Process Optimization

Background:

• Wastewater treatment plants (WWTPs) face dual challenges of meeting discharge regulations and minimizing operating costs (OC).
• Energy consumption (EC) and effluent quality (EQ) are key performance indicators for WWTPs, with a complex interrelationship.
• Existing models often struggle to accurately capture the dynamic relationship between EC and EQ.

Purpose of the Study:

• To develop and evaluate an advanced model for predicting energy consumption in WWTPs.
• To establish a direct link between energy consumption and effluent quality using a novel approach.
• To enhance the control and operational efficiency of wastewater treatment processes.

Main Methods:

• An extended Elman neural network-based energy consumption model (EENN-ECM) was developed.
• The self-adaptive nature of the EENN was leveraged to ensure high modeling accuracy.
• The EENN-ECM's performance was validated against the benchmark simulation model No.1 (BSM1).

Main Results:

• The EENN-ECM demonstrated a superior ability to predict energy consumption in WWTPs.
• The model accurately reflected the relationship between energy consumption and effluent quality.
• Comparative analysis showed the EENN-ECM's effectiveness over the BSM1 for EC modeling.

Conclusions:

• The proposed EENN-ECM offers a more effective solution for modeling energy consumption in wastewater treatment.
• This advanced modeling approach can contribute to optimizing WWTP operations for reduced costs and improved environmental compliance.
• The study highlights the potential of neural networks for intelligent control in environmental engineering applications.