Wastewater Treatment Control Using SA-MTPSO Set-Point Optimization
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Solution Overview
Problem
The challenge of balancing effluent quality and energy consumption in wastewater treatment processes is complex, making it difficult to achieve online optimal control, particularly in municipal wastewater treatment plants, where existing methods struggle to determine optimal set-points efficiently.
Innovation Solution
A data-driven multi-task optimization model using a self-adjusting multi-task particle swarm optimization algorithm to solve for optimal set-points of nitrate nitrogen and dissolved oxygen, combined with a PID controller for tracking these set-points, to reduce energy consumption while maintaining effluent quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control methods are used to ensure effluent quality, then effluent quality is maintained, but energy consumption increases
Solution Approach 1:
The patent implements dynamic adjustment of control parameters (aeration flow rate,回流 ratio) based on real-time effluent quality monitoring. The control system transitions from static to dynamic operation, allowing the system to adapt aeration and回流 rates according to actual nitrogen and phosphorus concentrations, thereby reducing energy consumption while maintaining effluent quality standards.
Solution Approach 2:
The patent changes key operational parameters (aeration flow rate, internal回流 rate, external回流 rate) from fixed values to dynamically adjustable parameters. By continuously optimizing these parameters based on effluent quality feedback, the system achieves better energy efficiency while ensuring compliance with discharge standards.
2Measurement precision
If complex mechanism models are established for wastewater treatment process, then model accuracy improves, but model determination difficulty increases
Solution Approach 1:
The patent replaces complex mechanical/mathematical mechanism models with a data-driven soft sensing model. Instead of establishing complex mass balance and kinetic equations, the system uses neural network algorithms to directly map operational parameters to effluent quality, significantly reducing model determination difficulty while maintaining prediction accuracy.
Solution Approach 2:
The patent creates a virtual copy of the wastewater treatment process through soft sensing models and digital twins. These computational models replicate the behavior of the physical system, allowing for virtual optimization and prediction without requiring complex physical modeling, thereby reducing the difficulty of model determination.
3Use of energy by moving object
If optimization algorithms are used to find optimal set-points, then energy consumption reduces, but solution time increases
Solution Approach 1:
The patent pre-calculates and stores optimal control strategies in the form of lookup tables or trained neural network weights. When operational conditions change, the system quickly retrieves or interpolates pre-computed solutions rather than performing time-consuming real-time optimization calculations, thus reducing solution time while maintaining energy optimization benefits.
Solution Approach 2:
The patent implements real-time feedback loops where effluent quality measurements are continuously fed back to the control system. This feedback mechanism enables the system to make incremental adjustments based on actual performance, achieving energy optimization through iterative refinement rather than requiring complete recalculation, thereby reducing solution time.
Data Source
AI summary
An optimal control method for wastewater treatment process (WWTP) based on a self-adjusting multi-task particle swarm optimization (SA-MTPSO) algorithm belongs to the field of WWTP. To balance the relationship between the effluent water quality (EQ) and energy consumption (EC) and achieve optimization online quickly, the invention establishes a data-based multi-task optimization model for WWTP to describe the relationship between the control variables and EQ, EC. Then, the SA-MTPSO algorithm is adopted to solve the optimal set-points of the nitrate nitrogen and dissolved oxygen concentration for WWTP. The PID controller is used to track the optimal set-points, so as to reduce EC while ensuring EQ, and realize the online optimal control of WWTP.