Compressed Air Control With Demand-Predictive Compressor Sequencing
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Solution Overview
Problem
Existing methods for controlling compressed air or gas systems are suboptimal due to their reliance on current system states without considering future predictions, leading to inefficient energy use.
Innovation Solution
A method that incorporates prediction data and characterizing data to determine continuously differentiable functions for optimal compressor operation, using state machines and branch & bound algorithms to minimize energy consumption while meeting future demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional control methods based on current system state are used, then the control implementation is simple, but the energy consumption is high and control optimality is poor
Solution Approach 1:
The patent applies preliminary action by determining a future sequence of operation for compressor components based on predicted future demand. The model predictive controller calculates optimal control actions in advance for a prediction horizon, allowing the system to proactively adjust to anticipated demand changes rather than reactively responding to current conditions. This enables energy optimization by pre-positioning compressors in optimal states before demand increases occur.
Solution Approach 2:
The patent implements dynamics by using a dynamic model of the compressed air system that captures time-varying behavior. The model predictive controller continuously updates predictions and control sequences based on changing system conditions and demand forecasts. The control strategy adapts dynamically by recalculating optimal sequences at each control interval, allowing the system to respond flexibly to varying operating conditions while maintaining optimality.
2Productivity
If model predictive control with future demand prediction is implemented, then energy efficiency is improved, but the computational complexity and control system sophistication increase
Solution Approach 1:
The patent applies segmentation by dividing the control problem into discrete time steps within a prediction horizon. The future sequence of operation is determined as a series of discrete control actions at different time intervals. This temporal segmentation allows the complex optimization problem to be broken down into manageable segments that can be solved systematically using dynamic programming or other optimization techniques, making the computational task more tractable.
Solution Approach 2:
The patent replaces traditional mechanical control approaches with a computational model-based control system. Instead of using simple pressure-switch-based mechanical control, the system uses a digital model of the compressed air system to predict future behavior and calculate optimal control sequences. This substitution of mechanical control with computational intelligence enables more sophisticated optimization while the model structure is designed to keep computations efficient through appropriate simplifications and assumptions.
Data Source
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AI summary
The present invention is directed to a computer implemented method for controlling a finite set of components which are fluidly connected to a common compressed air distribution system, the method comprising iteratively repeating the steps of: - receiving prediction data for said compressed air distribution system; - receiving characterising data for each component of said set of components; - determining one or more sets of continuously differentiable functions, wherein each of said sets of functions represents a unique sequence of operation of the components in said set of components; - selecting an optimal set of functions from said one or more sets of continuously differentiable functions; wherein the unique sequence of operation represented by said optimal set meets said prediction data; - deriving configuration data for said set of components from said optimal set of functions; - configuring each component of said set of components based on said configuration data.