Automating process design by coupling genetic algorithms with commercial simulators: a case study for hybrid MABR...

Area of Science:

• Environmental Engineering
• Artificial Intelligence
• Wastewater Treatment

Background:

• Traditional wastewater treatment design relies on manual, experience-based methods.
• Complex models and novel processes challenge conventional design approaches.
• Automated optimization is needed for efficient and effective wastewater treatment design.

Purpose of the Study:

• To integrate artificial intelligence (AI) with simulation software for automated wastewater treatment process design.
• To evaluate the performance of genetic algorithms (GAs) in optimizing a hybrid membrane aerated biofilm reactor (hybrid MABR).

Main Methods:

• Coupling genetic algorithms (GAs) with the SUMO commercial simulator for automated design tasks.
• Utilizing eleven typical measurements to estimate five influent wastewater fractions.
• Designing a hybrid MABR upgrade from a conventional Modified Ludzack-Ettinger (MLE) process.

Main Results:

• GAs accurately estimated influent wastewater fractions with minimal errors.
• The optimized hybrid MABR design reduced footprint by 17%, aeration by 57%, and pumping by 57%.
• Significantly improved effluent nitrogen quality was achieved (TN < 3 mg-N/L).

Conclusions:

• AI tools, specifically GAs, enhance efficiency in wastewater treatment design, optimization, and control.
• Automated, intelligent searching by AI replaces less favorable manual trial-and-error methods.
• This methodology accelerates process knowledge acquisition and learning for new wastewater treatment technologies.