Fluid Network Dynamic Control for Overflow and Spillage Prevention
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Solution Overview
Problem
Fluid networks, particularly sanitation networks, face risks of spillage and overflow due to inadequate control, necessitating an optimal operating configuration to limit investments while enhancing performance.
Innovation Solution
A dynamic control method for fluid networks that includes real-time operation prediction, online calibration, and strategic control instruction application using metrological data, with features like data validation, priority sorting, and near-real-time updating, coupled with hydrological and hydraulic modeling to stabilize calculations and reduce calculation times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time dynamic control is implemented to optimize fluid network operations, then spillage and overflow are limited and performance is enhanced, but calculation time and processing complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating hydraulic models and preparing control strategies in advance before real-time operation. The parametric hydraulic models are pre-configured with network topology and equipment characteristics, allowing rapid evaluation during runtime without full recalculation, thus reducing real-time computation time while maintaining reliable spillage prevention
Solution Approach 2:
The system implements dynamic control by continuously adjusting operating configurations based on real-time metrological data and forecasted weather conditions. The control strategy adapts dynamically to changing conditions (rainfall, flow rates, equipment status) to optimize network operation, balancing reliability improvement with acceptable calculation time through incremental updates rather than complete recalculations
2Measurement precision
If comprehensive metrological data collection and online calibration are performed, then model accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The system implements feedback mechanisms through online calibration that uses actual metrological measurements (flow rates, water levels) to continuously adjust and refine model parameters. This feedback loop improves model accuracy by comparing predicted versus actual values and automatically recalibrating, while managing complexity through automated algorithms that process data incrementally rather than requiring comprehensive manual analysis
Solution Approach 2:
The system manages data processing complexity by focusing on key parametric variables in the hydraulic models rather than processing all possible data parameters. Online calibration adjusts critical parameters (roughness coefficients, demand patterns, infiltration rates) based on selected metrological data, achieving improved model accuracy through targeted parameter optimization rather than exhaustive data processing
3Measurement precision
If detailed hydrological and hydraulic modeling is implemented, then operational forecasting accuracy is improved, but calculation time increases
Solution Approach 1:
The system applies partial action by implementing modeling at appropriate levels of detail for different network segments and time scales. Parametric models use aggregated representations for routine forecasting while allowing more detailed local modeling only where necessary (e.g., critical overflow points or complex hydraulic structures), achieving sufficient forecasting accuracy without the computational burden of uniformly detailed modeling across the entire network
Solution Approach 2:
The system manages the accuracy-time tradeoff through parametric modeling that uses simplified parameter relationships and assumptions (e.g., Manning's equation for flow, empirical rainfall-runoff relationships). These parametric approaches provide sufficiently accurate forecasts for operational decision-making while maintaining calculation speeds suitable for near-real-time control, avoiding the need for computationally intensive physics-based models
Data Source
Figure 1
AI summary
The invention relates to a method for dynamically controlling a fluidic network with a supervising module, said method comprising: - an operation-forecasting step that generates a forecast datum relating to operation of said fluidic network; - a step of selecting a control strategy for the fluidic network on the basis of a metrological datum and/or a meteorological datum and/or the forecast datum; - a step of generating setpoints intended for an actuator of a unit for regulating said fluidic network, on the basis of the selected control strategy, the metrological datum or the meteorological datum or the forecast datum; and - transmitting the regulation setpoint to said actuator.