A Dimensionality-Reducible Operational Optimal Control for Wastewater Treatment Process

Area of Science:

• Environmental Engineering
• Control Systems Engineering
• Data Science

Background:

• Operational optimal control (OOC) is crucial for wastewater treatment processes (WWTP).
• Traditional methods struggle with high-dimensional, nonlinear, and coupled control variables in WWTP.
• Operational variables often exist in an unknown low-dimensional space within a high-dimensional system.

Purpose of the Study:

• To propose a dimension-reducible, data-driven optimization control framework for WWTP.
• To address the challenge of optimizing complex control variables in industrial wastewater treatment.
• To develop a method for identifying and utilizing the underlying low-dimensional structure of control variables.

Main Methods:

• A neural network is employed to approximate the complex constraint relationships between control variables.
• Optimization is performed in the identified low-dimensional embedded space.
• Mathematical analysis is used to ensure the convergence of the optimization process.

Main Results:

• The proposed framework effectively reduces the dimensionality of the control problem.
• A data-driven approach successfully handles the nonlinear and coupled nature of WWTP control variables.
• Experimental simulations demonstrate the framework's effectiveness in achieving optimal control solutions.

Conclusions:

• The dimension-reducible, data-driven framework offers an effective solution for operational optimal control in WWTP.
• This approach overcomes limitations of traditional methods for complex industrial control systems.
• The study highlights the potential of integrating machine learning with control theory for environmental applications.