Developing process descriptor for biological nitrogen removal in wastewater treatment

Area of Science:

• Environmental Engineering
• Biotechnology
• Process Control

Background:

• Intelligent control in wastewater treatment often lacks integration with fundamental process mechanisms.
• Biological nitrogen removal (BNR) processes are crucial for wastewater treatment efficiency.

Purpose of the Study:

• To develop a computational framework bridging intelligent control and first-principle mechanisms in BNR.
• To create a process descriptor for analyzing BNR configurations and optimizing control.

Main Methods:

• Developed a matrix equation based on nitrogen mass conservation to encode stoichiometric causality in BNR.
• Established a computational framework defining relationships between nitrogen flow distribution (NFD), process structure, and BNR performance.
• Validated the framework with experimental data for various BNR configurations (PN, PD, A).

Main Results:

• Identified four basic observable BNR processes (PN, PD, PN/A, PD/A) at the stoichiometric level.
• Provided an analytical method for online sensor placement to ensure process observability.
• Created a deterministic mapping space reducing parameter uncertainty for intelligent control models.

Conclusions:

• The developed matrix equation serves as a transformative tool for BNR process analysis, observability design, and optimal control.
• This framework enhances the integration of intelligent control with mechanistic understanding in wastewater treatment.
• The approach offers significant potential for optimizing wastewater treatment plant operations.