Supply Grid Pressure Prediction for Faster Pump and Valve Control
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Solution Overview
Problem
Existing pressure control methods in supply grids face challenges in achieving rapid actuation of pumps and valves due to the time-consuming nature of creating simulation models, especially in complex grid topologies or when insufficient information is available, leading to delayed pressure adjustments and increased energy consumption.
Innovation Solution
A method utilizing a self-learning system, such as an artificial neural network, to predict pressure at unmonitored locations in a supply grid based on measured flow rates and pressures from sensors, allowing for rapid pressure control without the need for additional sensors at those locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation models are created for pressure control in complex grid topologies, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent creates a simplified copy (simulation model) of the complex supply grid that replicates the essential pressure-flow relationships. This virtual model allows rapid pressure prediction at any location without requiring complex physical measurements or full-scale simulations, thus achieving accurate pressure control predictions quickly.
Solution Approach 2:
The simulation model is created in advance (offline) before actual pressure control operations. By pre-processing the grid topology and hydraulic characteristics into the model, the system eliminates time-consuming calculations during real-time operations, enabling rapid pressure predictions when control decisions are needed.
2Reliability
If continuous high pressure is provided by water towers or pumps, then reliability of water supply is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts pump operation based on real-time pressure predictions from the simulation model. Instead of continuous high-pressure operation, the pump runs only when and where needed to maintain minimum required pressures, adapting to varying consumption patterns and grid conditions to minimize energy consumption while ensuring supply reliability.
Solution Approach 2:
The system uses measured pressure values from the grid as feedback to the simulation model, which then predicts pressure distributions and identifies where pressure support is needed. This closed-loop control enables the pump to respond only to actual pressure deficiencies rather than operating continuously at high pressure.
3Use of energy by moving object
If pressure management zones are created with selective valve control, then energy consumption is reduced, but device complexity increases
Solution Approach 1:
The simulation model serves multiple functions: it predicts pressure distributions, identifies optimal valve positions, determines pump operation requirements, and evaluates different control strategies. This single multi-functional tool replaces the need for separate complex control systems for each function, managing complexity while enabling comprehensive pressure management.
4Adaptability or versatility
If online pressure measurements are used for dynamic pressure control, then adaptability is improved, but measurement and computing requirements increase
Solution Approach 1:
The simulation model acts as an intermediary between the limited online pressure measurements and the pressure control decisions. Instead of directly controlling based on complex real-time calculations from multiple sensors, the model processes the measurement data and provides simplified pressure predictions and control recommendations, reducing the complexity of the measurement and computing system while maintaining adaptability.
Data Source
AI summary
Methods, devices, and assemblies for controlling pressure in a supply grid are provided. The supply grid is suitable for supplying fluid to loads. The supply grid has first sensors for measuring the flow and/or the pressure of the fluid at first locations in the supply grid and a pump for pumping the fluid or a valve for controlling the flow of the fluid. The method includes: measuring the flow and/or pressure of the fluid at the first locations in the supply grid by the first sensors; predicting the pressure at the second location in the supply grid using a self-learning system based on the measured flows or pressures, wherein the self-learning system is trained to predict the pressure at a specified location in the supply grid; and actuating the pump or the valve at least also based on the pressure predicted by the trained system at the second location.