Dynamic Control Signals for Water Drainage Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current water drainage networks face challenges in automatically responding to unexpected situations during heavy rain events, leading to flooding and untreated water discharges due to overloaded capacities, as existing control methods rely on static models that cannot adapt to varying conditions.
Innovation Solution
A computer-implemented method generates dynamic control signals for actuators in water drainage networks by receiving data on network topology, rain intensity, and water levels, using optimization techniques such as lexicographic or weighted sum methods to optimize operational goals, allowing for real-time adaptation and response to non-expected situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static control models are used in water drainage networks, then device complexity is reduced, but adaptability to varying rain intensity and water level conditions deteriorates
Solution Approach 1:
The patent implements dynamic optimization models that continuously adjust control strategies based on real-time sensor data regarding rain intensity and water levels. The system transitions from static pre-programmed control to dynamic adaptive control, where objective functions and constraints are updated continuously to reflect current network conditions, thereby resolving the contradiction between model complexity and adaptability.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting control signals to actuators (gates, pumps, tanks) based on optimized parameters derived from the mathematical model. The objective functions and constraints are modified in real-time according to measured conditions, allowing the system to adapt to varying rain intensity and water levels without requiring a fundamentally more complex infrastructure.
2Extent of automation
If automated real-time optimization is implemented, then response to unexpected situations improves, but device complexity and computational requirements worsen
Solution Approach 1:
The control system performs self-service by automatically generating and optimizing control strategies without human intervention. The mathematical model with objective functions and constraints autonomously processes sensor data, computes optimal control actions, and sends signals to actuators, enabling the drainage network to respond independently to unexpected situations while managing computational complexity through efficient algorithm design.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor water levels and rain intensity, feed this information to the optimization model, which then adjusts control signals accordingly. This closed-loop feedback mechanism enables automatic real-time response while distributing computational tasks across the control architecture, managing overall system complexity.
3Reliability
If infrastructure storage capacity is increased to prevent overflows, then reliability improves, but device complexity and cost worsen
Solution Approach 1:
The system performs preliminary actions by proactively managing water storage in tanks and adjusting gate positions before overflow conditions occur. The optimization model predicts future water levels based on current rain intensity and network state, allowing advance control actions that prevent overflows without requiring excessive storage capacity, thereby maintaining reliability while limiting infrastructure complexity.
Solution Approach 2:
Rather than increasing static storage capacity, the system uses dynamic control of existing infrastructure elements (tanks, gates, pumps) to adaptively manage water volumes. The optimization model continuously adjusts the operational state of these elements to maximize overflow prevention capability without adding physical infrastructure, resolving the contradiction between reliability and device complexity.
Data Source
AI summary
The inventions comprises a computer implemented method for generating control signals adapted to be sent to actuators, such as gates and pumps, in a water drainage network DN in an area, said method comprising —receiving DN data comprising one or more of DN topology of the area, rain intensity measures, water level measures, from the sensors or from an external source, —generating or receiving objective functions to optimize, —receiving a selection of a multi-objective optimization method, this multi-objective optimization preferably comprising lexicographic method or weighted sum method, —generating an optimization problem, —solving the optimization problem thereby generating the strategies to be sent to actuators in the water drainage network DN.