Compressed Air Control Using Predictive Demand Sequencing
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Solution Overview
Problem
Current control methods for compressed air or gas systems are suboptimal as they operate solely based on the current state, failing to consider predicted demands, leading to suboptimal control and higher energy costs.
Innovation Solution
A computer-implemented method that iteratively receives prediction data and characterizing data to determine continuously differentiable functions representing optimal sequences of operation for components in the compressed air distribution system, configuring them to meet predicted 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 solely based on current state, then system simplicity is maintained, but energy consumption increases and control optimality deteriorates
Solution Approach 1:
The control system performs preliminary actions by using prediction data to forecast future pressure and airflow demands before they occur. The controller determines optimal sequences of operation in advance, configuring components proactively to meet anticipated demands rather than reacting to current conditions alone, thereby reducing energy consumption through forward-looking optimization.
Solution Approach 2:
The control system transitions from static, rule-based control to dynamic model predictive control. The controller continuously updates the model of the compressed air system and recalculates optimal operation sequences based on real-time measurements and prediction data, allowing the control strategy to adapt dynamically to changing system conditions and demand patterns.
2Productivity
If model predictive control with prediction data is implemented, then control optimality and energy efficiency improve, but computational complexity and data processing requirements increase
Solution Approach 1:
The control problem is segmented into manageable components: the system model is divided into subsystem models for different components (compressors, storage tanks, distribution networks), and the prediction period is divided into discrete time steps. This segmentation allows the complex optimization problem to be solved through iterative calculation of smaller sub-problems, reducing overall computational complexity.
Solution Approach 2:
A model of the compressed air system serves as an intermediary between raw prediction data and control decisions. The model translates prediction data into optimal operation sequences through systematic calculation, mediating the complex relationship between future demands and current control actions while structure the computation in a tractable manner.
3Loss of energy
If components are controlled simultaneously based on prediction data, then system-wide energy consumption is minimized, but control coordination complexity increases
Solution Approach 1:
The control method merges the control of multiple components into a unified model predictive control framework. Instead of controlling compressors, storage tanks, and distribution elements separately, the system creates an integrated model that considers all components simultaneously, determining coordinated operation sequences that minimize total system energy consumption while satisfying demand requirements.
Solution Approach 2:
The controller performs multiple functions through a single unified control algorithm: it predicts future demands, optimizes operation sequences for multiple components, coordinates their interactions, and generates control signals. This universal approach eliminates the need for separate control systems for different components and reduces overall control coordination complexity.
Data Source
AI summary
A computer implemented method for controlling a finite set of components which are fluidly connected to a common compressed air distribution system includes 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.