Water Infrastructure Setpoint Scheduling for Disturbance Control
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Solution Overview
Problem
Traditional control systems for water resource infrastructures are cumbersome, expensive, and computationally inefficient, requiring significant staff resources and being difficult to integrate with real-time SCADA systems.
Innovation Solution
A control mechanism scheduler that receives operating and disturbance data to generate classes for disturbance signals, simulate water resource infrastructure operations, and produce schedules of setpoints for control mechanisms to achieve predetermined objectives, such as improving reliability and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional physically-based numerical models are used to model water resource infrastructures, then measurement precision and reliability are improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent creates simplified copies of the complex physical model by training machine learning algorithms on historical data from the physically-based numerical model. These ML models replicate the behavior of the complex model but with dramatically reduced computational requirements, enabling real-time applications while maintaining acceptable accuracy.
Solution Approach 2:
The invention transforms the model representation from continuous physical equations with numerous parameters to discrete data-driven models with optimized parameter sets. By changing the mathematical representation and using dimensionality reduction techniques, the model complexity is reduced while preserving essential system behavior.
2Measurement precision
If traditional physically-based numerical models are used, then measurement precision is improved, but productivity decreases due to long simulation execution times
Solution Approach 1:
The system performs preliminary training of machine learning models using historical data from the complex physical model. This preliminary action creates pre-computed knowledge that can be rapidly applied to new scenarios without re-running the full physical simulation, enabling fast predictions for real-time control and analysis.
Solution Approach 2:
The patent creates simplified copies of the complex physical model by training machine learning algorithms on historical data from the physically-based numerical model. These ML models replicate the behavior of the complex model but with dramatically reduced computational requirements, enabling real-time applications while maintaining acceptable accuracy.
3Measurement precision
If traditional physically-based numerical models are used, then measurement precision is improved, but loss of time increases due to extensive design, calibration, and tuning processes
Solution Approach 1:
The system enables models to self-calibrate by automatically adapting to new data and conditions without requiring manual intervention. The machine learning algorithms continuously learn from incoming data, automatically adjusting parameters and maintaining accuracy without the extensive manual calibration and tuning that traditional physical models require.
Solution Approach 2:
The invention transforms the model representation from continuous physical equations with numerous parameters to discrete data-driven models with optimized parameter sets. By changing the mathematical representation and using dimensionality reduction techniques, the model complexity is reduced while preserving essential system behavior.
4Reliability
If traditional physically-based numerical models are used, then reliability is improved, but ease of operation deteriorates due to difficulty in integration with SCADA systems
Solution Approach 1:
The patent creates simplified copies of the complex physical model by training machine learning algorithms on historical data from the physically-based numerical model. These ML models replicate the behavior of the complex model but with dramatically reduced computational requirements, enabling real-time applications while maintaining acceptable accuracy.
Data Source
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AI summary
A control mechanism scheduler for a water resource infrastructure receives operating data and disturbance data, the operating data describing infrastructure components of the water resource infrastructure, the disturbance data comprising a disturbance signal describing a disturbance expected to disturb the water resource infrastructure. The control mechanism scheduler generates classes for disturbance signals, generates simulations of the water resource infrastructure, and generates schedules of setpoints for control mechanisms actuable to control the infrastructure components of the water resource infrastructure in accordance with approaching a predetermined objective.