Predictive Compressor Room Control for Energy and Wear Reduction
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Solution Overview
Problem
Current compressor control methods operate based solely on the current state of the compressed air or gas system, failing to consider predictive elements, leading to suboptimal control and increased energy costs.
Innovation Solution
A method that estimates the current state of the compressed air or gas system, predicts future process variables, samples these predictions based on system volume, and uses model predictive control to generate action and state profiles over defined time horizons, instructing compressors to perform optimized actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If compressors are controlled based solely on current system state, then control simplicity is maintained, but energy efficiency deteriorates
Solution Approach 1:
The control method performs preliminary actions by predicting future compressed air consumption and compressor availability, then proactively scheduling compressor operations in advance. This allows the system to anticipate future states and make optimal control decisions before actual consumption patterns materialize, thereby improving energy efficiency without significantly increasing control complexity.
2Use of energy by moving object
If predictive control is implemented, then energy efficiency improves, but control complexity increases
Solution Approach 1:
The control method dynamically adapts to changing system conditions by continuously updating predictions of compressed air consumption and compressor availability. The controller adjusts its scheduling decisions in real-time based on predicted future states, making the control system flexible and responsive while maintaining manageable complexity through structured prediction models.
Solution Approach 2:
The control method incorporates feedback mechanisms where actual consumption data and compressor performance are continuously monitored and fed back into the prediction model. This closed-loop approach allows the system to learn from past performance and improve future predictions, enhancing energy efficiency while keeping control complexity manageable through iterative refinement.
3Use of energy by moving object
If compressor scheduling is optimized, then energy consumption reduces, but machine wear increases due to frequent switching
Solution Approach 1:
The control method performs preliminary scheduling of compressor operations based on predicted future consumption patterns. By planning compressor start-stop sequences in advance rather than reacting to immediate demands, the system can optimize for both energy efficiency and mechanical reliability, reducing frequent switching and associated wear while maintaining effective energy management.
Data Source
AI summary
A method for controlling a compressed air or gas system is disclosed including the steps of estimating a current state, predicting a future process variable profile based on the current state, sampling the future process variable profile by a sampling method having sampling frequencies based on a volume of the compressed air or gas system, transforming by a model predictive control, MPC, method the sampled future process variable profile and the current state into an action profile and a state profile, and instructing the compressors to perform the actions in accordance with the action profile thereby controlling the compressed air or gas system.