Computational intelligence-based optimisation of wastewater treatment plants.

Area of Science:

• Environmental Engineering
• Artificial Intelligence in Water Treatment

Background:

• Wastewater treatment plants face challenges in optimizing processes for efficiency and compliance.
• Classical control methods often struggle with dynamic influent conditions and complex treatment goals.

Purpose of the Study:

• To evaluate the effectiveness of computational intelligence (CI) controllers in optimizing municipal wastewater treatment.
• To compare the performance of CI controllers against traditional control methods.

Main Methods:

• Implementation and long-term operation of CI controllers (fuzzy control, neuronal networks) in two municipal wastewater treatment plants.
• Monitoring and analysis of key performance indicators, including effluent quality, energy consumption, and load handling.
• Comparative analysis with historical data from conventional control systems.

Main Results:

• Significant improvements in plant performance and effluent quality, consistently meeting regulatory standards.
• Effective management of peak influent loads without compromising effluent concentrations.
• Sustained operational efficiency and compliance over several years of continuous CI controller operation.

Conclusions:

• Computational intelligence controllers offer a cost-effective solution for enhancing municipal wastewater treatment performance.
• CI-based control is a sustainable approach for achieving superior operational outcomes and environmental compliance.