Reduced modeling and state observation of an activated sludge process

Area of Science:

• Environmental Engineering
• Biochemical Engineering
• Process Modeling

Background:

• Activated sludge processes are crucial for wastewater treatment.
• Standard models like ASM1 can be complex for real-time applications.
• Alternated aeration introduces dynamic challenges in modeling.

Purpose of the Study:

• To propose a reduced-order model for activated sludge processes with alternated aeration.
• To develop a robust estimation strategy for unmeasured state variables and influent concentrations.
• To simplify complex biochemical processes for practical engineering applications.

Main Methods:

• Biochemical considerations and linear approximations of nonlinear terms from ASM1.
• Development of separate aerobic and anoxic phase submodels.
• A two-step robust estimation strategy for state and parameter estimation, considering parameter uncertainty.

Main Results:

• A simplified activated sludge model with four state variables (substrate, ammonium/nitrite-nitrate, oxygen).
• Effective estimation of unmeasured state variables and influent ammonium nitrogen concentration.
• Successful incorporation of parameter uncertainty into the model dynamics and input matrices.

Conclusions:

• The proposed reduced model effectively captures the essential dynamics of alternated aeration activated sludge systems.
• The robust estimation strategy enhances the practical applicability of the model for monitoring and control.
• This work provides a valuable tool for optimizing wastewater treatment processes.