Compressed Air Control Using Predictive Load Sequencing
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Solution Overview
Problem
Existing control methods for compressed air or gas systems are suboptimal due to their reliance on current state data without considering future predictions, leading to inefficiencies and higher energy costs.
Innovation Solution
A method involving model predictive control that iteratively receives prediction data and characterizing data to determine continuously differentiable functions, selects an optimal sequence of component operations, and configures components to meet future pressure and airflow demands while minimizing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If control methods operate based on current state data only, then the control system is simple to implement, but the energy efficiency and system performance deteriorate
Solution Approach 1:
The control system performs preliminary actions by using prediction data to forecast future pressure and airflow demands before they occur. This allows the system to proactively adjust component configurations and operational sequences in advance, optimizing energy efficiency by avoiding reactive control adjustments that consume more energy.
Solution Approach 2:
The system dynamically adapts its control strategy by continuously updating the unique sequence of operation based on predicted future demands. The control approach transitions from static, rule-based control to dynamic, prediction-driven control, allowing the system to optimize energy efficiency in real-time while managing complexity through adaptive algorithms.
2Loss of energy
If control methods use prediction data and optimize operational sequences, then energy consumption is reduced, but the complexity of control algorithms increases
Solution Approach 1:
The control algorithm segments the compressed air system into individual controllable components and generates unique sequences of operation for each component. By dividing the complex optimization problem into component-specific operational sequences, the system reduces energy consumption while managing algorithmic complexity through structured segmentation of control tasks.
Solution Approach 2:
The system optimizes energy consumption by dynamically changing operational parameters such as pressure settings, airflow rates, and component activation sequences based on prediction data. These parameter changes are implemented through continuously differentiable functions that smoothly adjust system operation, reducing energy loss while maintaining controllable algorithm complexity.
3Productivity
If sequentially control compressors with independent local controllers, then the control system is simple and reliable, but the overall system efficiency and coordination deteriorate
Solution Approach 1:
The system merges independent local controllers into a coordinated control architecture that uses a unique sequence of operation for multiple components. This integration allows compressors and other components to work together efficiently based on predicted future demands, improving overall system productivity while managing complexity through unified control logic.
Solution Approach 2:
The control system implements feedback mechanisms where prediction data about future demands is continuously incorporated into the operational sequence adjustments. This feedback loop enables the system to learn from predicted patterns and optimize component coordination, improving productivity while maintaining manageable complexity through iterative refinement of control strategies.
4Loss of time
If control systems react to current demand only, then the response time is fast and simple, but the ability to optimize for future conditions is lost
Solution Approach 1:
The system performs preliminary optimization by using prediction data to determine the unique sequence of operation before future demand conditions occur. This advance planning allows the system to minimize optimization time loss by having control decisions ready in advance, while managing the complexity of prediction and planning capabilities through structured algorithms.
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.