Water Infrastructure Control Scheduling With ML Disturbance Models
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Solution Overview
Problem
Traditional control systems for water resource infrastructures are cumbersome, computationally inefficient, and difficult to implement in real-time due to their reliance on complex physically-based numerical models, which require extensive resources and are not easily integratable with SCADA systems.
Innovation Solution
A control mechanism scheduler that receives operating and disturbance data to generate classes for disturbance signals, simulate water resource infrastructure, and produce schedules for control mechanisms to achieve predetermined objectives, using pattern recognition algorithms and machine learning to automate the control of infrastructure components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional physically-based numerical models are used to model water resource infrastructures, then model accuracy and reliability are improved, but device complexity and computational requirements increase significantly
Solution Approach 1:
The patent replaces complex physically-based numerical models with machine learning models that use pattern recognition and data-driven approaches. Instead of relying on detailed physical equations and extensive parameter calibration, the system uses historical operating data and disturbance data to train models that predict system behavior, thereby reducing model complexity while maintaining accuracy.
Solution Approach 2:
The patent creates simplified representations of the water resource infrastructure by training machine learning models on historical data. These models copy the essential behavior patterns of the complex physical system without requiring the full physical complexity, enabling faster simulation and control while preserving reliability.
2Measurement precision
If traditional physically-based numerical models are used, then model precision is improved, but computational efficiency deteriorates due to long simulation execution times
Solution Approach 1:
The patent substitutes computationally intensive physical numerical models with machine learning models that have been pre-trained on historical data. Once trained, these models execute simulations much faster while maintaining precision by leveraging learned patterns from the training data, thereby resolving the contradiction between precision and computational efficiency.
Solution Approach 2:
The system performs preliminary training of machine learning models using historical operating data and disturbance data before actual control operations. This preliminary action captures the essential system behavior patterns, enabling fast and precise simulations during real-time operations without requiring complex computational resources.
3Reliability
If traditional physically-based numerical models are used, then system reliability is improved, but ease of operation deteriorates due to manual control requirements
Solution Approach 1:
The patent implements automated control mechanisms that use machine learning models to determine optimal control actions without requiring manual intervention. The system autonomously analyzes operating data and disturbance data, predicts system behavior, and generates control schedules, thereby improving ease of operation while maintaining reliability through data-driven decision-making.
Solution Approach 2:
The system continuously monitors operating data and disturbance data, compares actual system behavior with predicted behavior from the machine learning models, and adjusts control actions accordingly. This feedback mechanism maintains system reliability while eliminating the need for manual control operations.
4Measurement precision
If traditional physically-based numerical models are used, then measurement precision is improved, but loss of time increases due to extensive calibration processes
Solution Approach 1:
The patent replaces the time-consuming calibration process of physical models with a machine learning training process that uses historical data. Instead of manually adjusting numerous parameters to match physical reality, the system automatically learns system behavior patterns from historical operating data and disturbance data, achieving the same precision goal with significantly reduced time investment.
Solution Approach 2:
The system performs comprehensive model training using historical data in advance, capturing system behavior patterns before actual operations begin. This preliminary training action eliminates the need for time-consuming calibration during operations, as the models are already tuned to match real system behavior through learning from historical examples.
5Reliability
If traditional physically-based numerical models are used, then reliability is improved, but extent of automation deteriorates due to difficulty in integration with SCADA systems
Solution Approach 1:
The patent designs machine learning models with universal interfaces that can integrate with standard SCADA systems and data acquisition platforms. The models accept standard input data formats (operating data and disturbance data) and produce control recommendations in formats compatible with existing control infrastructure, thereby enabling automation while maintaining reliability through proven integration capabilities.
Data Source
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.