System and method for controlling multi-zone vapor compression system and non-transitory computer readable storage medium
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Solution Overview
Problem
Current multi-zone vapor compression systems face challenges in efficiently controlling operations across various configurations while enforcing constraints on temperature and pressure, leading to suboptimal performance and energy consumption due to reactive constraint management strategies.
Innovation Solution
Implementing a predictive control method using model predictive control (MPC) that formulates and parameterizes optimization problems based on the system configuration, ensuring constraint enforcement and stability across all possible configurations without the need for manual specification of optimization problems for each configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive constraint management is used to enforce safety constraints, then equipment safety is ensured, but system performance is sacrificed and energy consumption increases
Solution Approach 1:
The controller proactively identifies and addresses potential constraint violations before they occur by predicting future system states. The optimization problem forecasts temperature and pressure trajectories and takes corrective action in advance, rather than reacting after violations occur. This preliminary action allows the system to operate closer to constraint boundaries safely, improving performance while maintaining reliability.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the controller continuously monitors system state, predicts future violations using the optimization model, and adjusts control inputs accordingly. This feedback loop enables the system to maintain optimal performance while proactively preventing constraint violations, resolving the trade-off between safety and performance.
2Reliability
If conservative thresholds are used for corrective action to account for likely violations, then equipment safety is maintained, but the operating regime of highest performance is sacrificed
Solution Approach 1:
By predicting future constraint violations before they occur, the system can take corrective action at optimal moments rather than using conservative thresholds that force operation away from high-performance regions. This reduces unnecessary energy consumption while maintaining reliable constraint enforcement.
Solution Approach 2:
The optimization problem dynamically adjusts control parameters based on predicted system trajectories and constraint proximity. Instead of using fixed conservative thresholds, the system adapts parameters in real-time to maintain safety while operating in high-performance regions, thereby reducing energy consumption.
3Reliability
If manual specification of optimization problems is performed for each configuration, then constraint enforcement is accurate, but device complexity and control difficulty increase
Solution Approach 1:
The patent implements a universal optimization framework that handles all possible system configurations through a single standardized formulation. The controller uses configuration indicators to automatically adapt the optimization problem to the current state, eliminating the need for manual specification for each configuration. This universal approach maintains accurate constraint enforcement while significantly reducing controller complexity.
Solution Approach 2:
The system automatically determines the appropriate optimization problem formulation based on configuration indicators without requiring manual intervention. The controller self-adapts to different configurations (such as active/inactive heat exchangers) by interpreting configuration indicators and adjusting constraints and objectives accordingly, thereby reducing operational complexity while maintaining accuracy.
4Reliability
If the system operates away from constraints to ensure safety, then reliability is improved, but the regions of highest performance are sacrificed
Solution Approach 1:
The optimization controller proactively predicts constraint violations and takes corrective action before the system reaches dangerous operating regions. This allows the system to operate closer to constraint boundaries with confidence, maximizing performance while maintaining safety through predictive rather than reactive control.
Solution Approach 2:
The closed-loop optimization framework continuously monitors system state and adjusts control inputs to keep the system operating near optimal performance regions while preventing constraint violations. This feedback mechanism enables the system to safely operate in high-performance regions that would be avoided by conservative rule-based controllers.
Data Source
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AI summary
A system controls a multi-zone vapor compression system (MZ-VCS). The system includes a controller to control a vapor compression cycle of the MZ-VCS using a set of control inputs determined by optimizing a cost function including a set of control parameters. The optimizing is subject to constraints, and wherein the cost function is optimized over a prediction horizon. The system also includes a memory to store an optimization function parameterized by a configuration of the MZ-VCS defining active or inactive modes of each heat exchanger, the optimization function modifies, according to a current configuration, values of the control parameters of the cost function determined for a full configuration that includes all heat exchangers in the active mode. The system also includes a processor to determine the current configuration of the MZ-VCS and to update the cost function by submitting the current configuration to the optimization function.