Simulation-Based Compressed Air Station Control
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Solution Overview
Problem
Existing control methods for compressed air stations are reactive, failing to predict pressure changes early enough to initiate timely switching operations, leading to suboptimal energy usage and increased operational costs due to delayed reactions and heuristic parameterization limitations.
Innovation Solution
A method that uses an electronic system control with a simulation core to predict future pressure changes by simulating alternative switching strategies based on dynamic models of the compressed air station, selecting the most advantageous strategy to adapt to future operating conditions and account for non-linearities and dead times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If reactive control methods with predetermined pressure bands are used, then the control system is simple to implement, but pressure changes cannot be predicted early enough to initiate timely switching operations
Solution Approach 1:
The patent implements a simulation core that performs preliminary simulations of future pressure developments before actual pressure changes occur. The control system evaluates multiple alternative switching strategies through simulation to predict which actions will be most effective, allowing the system to take preliminary control decisions based on predicted future states rather than reacting to current conditions alone. This resolves the contradiction by enabling timely switching operations through advance simulation without requiring overly complex real-time control mechanisms.
Solution Approach 2:
The simulation core acts as an intermediary between the current system state and the control decisions. It receives current pressure and operational data, simulates various future scenarios, and provides predicted outcomes to the control system. This intermediary simulation layer enables the system to look ahead and predict pressure changes without directly complicating the actual control execution mechanism, thus maintaining relative simplicity while improving response timing.
2Ease of manufacture
If heuristic parameterization methods are used for control, then the control strategy is easy to implement, but energy consumption increases due to suboptimal switching decisions
Solution Approach 1:
The patent transitions from static heuristic parameterization to dynamic simulation-based optimization. The simulation core continuously evaluates switching strategies based on current and predicted system states, allowing the control parameters to adapt dynamically to changing conditions. This dynamic approach identifies energy-optimal switching moments by simulating future scenarios, reducing unnecessary compressor operation while maintaining ease of implementation through automated simulation-driven decisions.
Solution Approach 2:
The system changes the parameter optimization approach from fixed heuristic values to dynamically determined optimal parameters through simulation. By varying and evaluating multiple switching strategy parameters through simulation, the system identifies the energy-optimal configuration for each situation, thereby reducing energy consumption while keeping the implementation straightforward through automated parameter optimization.
3Loss of energy
If simulation-based predictive control is implemented, then energy consumption is reduced through optimal switching strategies, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the control system into distinct functional modules: a simulation core that handles predictive modeling, an evaluation module that assesses alternative strategies, and an execution module that implements decisions. This segmentation allows the complex simulation computations to be isolated and optimized separately from the control execution, managing computational complexity through modular architecture while maintaining energy optimization benefits.
Solution Approach 2:
The system performs simulations for multiple alternative switching strategies (excessive action) to ensure optimal energy efficiency, but implements only the single best strategy identified (partial action). This approach justifies the computational effort by evaluating more options than strictly necessary to guarantee optimal performance, while avoiding the need to implement all simulated strategies, thus managing computational complexity through selective implementation.
Data Source
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AI summary
Method for controlling and regulating a compressed air station (1) comprising at least a plurality of interconnected compressors (2), said method being able to implement circuit strategies in the compressed air station using an electronic system controller (3) that influences an amount of a pressurized fluid which is available at all times to one or more users of the compressed air station, the method also being able to adaptively adjust the amount of pressurized fluid which is available at all times to one or more users of the compressed air station according to future operating conditions of the compressed air station, said adaptive adjustment being relative to the amount of compressed fluid withdrawn from the compressed air station, wherein prior to the implementation of a circuit strategy, various circuit strategies are examined in a prior simulation process using a model of the compressed air station as a basis, and from the examined circuit strategies the most advantageous circuit strategy, relatively speaking, is selected according to at least one established quality criterion, and the selected circuit strategy is forwarded to the system controller for implementation in the compressed air station.