Sewage Treatment Control Model With Simulation-Based State Estimation
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Solution Overview
Problem
Existing sewage treatment systems face challenges in efficiently managing operational costs and energy consumption while maintaining high reliability and accuracy in treating wastewater, particularly in controlling processes like aeration and sludge management.
Innovation Solution
A support system and method utilizing a computational model to calculate manipulated variables for sewage treatment processes, integrating sensors for data acquisition, a computation unit for model-based calculations, and a simulation unit for state estimation, enabling optimized control of aeration and sludge processes to minimize power costs and enhance operational reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based control is implemented to improve treatment efficiency, then operational reliability and accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
A simulation unit acts as an intermediary between the control system and the actual sewage treatment process. The simulation unit calculates expected process states based on current operations and compares them with actual sensor readings, enabling model-based control without requiring direct complex model implementation in the main control system. This mediator approach improves reliability while managing system complexity.
Solution Approach 2:
The system creates a virtual copy of the sewage treatment process through simulation. Instead of directly implementing complex control models on the physical system, a digital twin/simulation model replicates the process behavior, allowing for safe testing, prediction, and control optimization before applying changes to the actual system, thereby improving reliability without proportionally increasing physical device complexity.
2Measurement precision
If comprehensive process monitoring and simulation are implemented to maintain high treatment accuracy, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The system implements simulation and monitoring at selective critical points rather than continuously across the entire process. The simulation unit calculates expected states for key parameters and compares them with actual measurements, providing sufficient measurement precision for control decisions without the excessive computational energy consumption of comprehensive continuous monitoring of all process variables.
Solution Approach 2:
The system uses feedback from sensor measurements to validate simulation predictions and adjust operations. By comparing expected states (from simulation) with actual states (from sensors), the system achieves high measurement precision only where critical for control, reducing overall computational energy consumption while maintaining accuracy where it matters most for treatment effectiveness.
Data Source
AI summary
A support system is provided which includes an acquisition unit which acquires a measured value in a sewage treatment system; a computation unit which uses a model for controlling the sewage treatment system which is generated based on the measured value and a manipulated variable given to the sewage treatment system to calculate a manipulated variable to be given to the sewage treatment system depending on the measured value newly acquired; and a simulation unit which calculates a state of the sewage treatment system depending on the manipulated variable calculated in the computation unit by simulation.