Wastewater Network Control via Dynamic Actuator Presets
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Solution Overview
Problem
Existing wastewater network management systems are inefficient as they rely on predetermined actuator control instructions based on a nominal network state, leading to underperformance when the network is not in its nominal state due to unavailability or reduced capacity of structures or actuators.
Innovation Solution
A method that dynamically selects actuator control instructions based on the current state of the network, using an optimization algorithm to determine new sets of instructions for each type of rain, and updates the network model to reflect actual conditions, allowing for improved performance regardless of the network's state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined actuator control instructions based on nominal network state are used, then the control system is simple to implement, but the network performance deteriorates when structures or actuators are unavailable or operating at reduced capacity
Solution Approach 1:
The control system dynamically adapts actuator instructions based on the current network state. Instead of using fixed predetermined instructions, the system continuously monitors the actual state of structures and actuators, and adjusts control instructions in real-time to match the current operational conditions, thereby maintaining optimal performance even when components are unavailable or operating at reduced capacity
Solution Approach 2:
The system implements a feedback mechanism where the actual network state is continuously monitored and fed back to the control algorithm. This feedback loop enables the system to detect changes in network conditions (such as actuator unavailability or reduced capacity) and automatically adjust control instructions accordingly, resolving the contradiction between simplicity and reliability under varying conditions
2Measurement precision
If the network model is updated to reflect current state, then the accuracy of control instructions improves, but the computational complexity increases
Solution Approach 1:
The system updates the network model by changing key parameters that reflect the current state of structures and actuators. Instead of rebuilding the entire model, the algorithm adjusts specific parameters (such as actuator availability status, structure capacity factors) based on monitored conditions, thereby improving accuracy while minimizing computational complexity
Solution Approach 2:
The control algorithm is pre-configured with the nominal network model and predefined adjustment rules for various non-nominal conditions. When the network deviates from its nominal state, the pre-programmed algorithm automatically applies appropriate parameter adjustments without requiring complex real-time model rebuilding, thus balancing accuracy improvement with computational efficiency
Data Source
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AI summary
Method for controlling a waste water network, said network comprising actuators able to influence flow rates of water in the network, the behaviour of the actuators depending on presets, the method comprising: - a step of selecting a type of rain from a list of predetermined types of rain, as a function of a predicted or measured rain, - a step of selecting a set of presets from a list of predetermined sets of presets, as a function of the type of rain selected, and - a step of despatching presets of the selected set of presets to said actuators. The method comprises a step of obtaining first state information representative of a current state of the network, said set of presets being selected from the list of predetermined sets of presets as a function of the selected type of rain and of the first state information.