Chiller System Neural Network Control for Dynamic Energy Optimization
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Solution Overview
Problem
Existing chiller systems for data centers and controlled environments face challenges in efficiently maintaining the required cooling capacity while minimizing energy consumption, especially as cooling demands fluctuate with ambient temperature changes.
Innovation Solution
A system of chiller devices with a supervisory control device that dynamically adjusts the outlet temperature and flow rate setpoints of active chiller devices, and activates or deactivates chiller devices to optimize energy consumption while maintaining the target temperature range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the chiller system increases cooling capacity by ramping up compressors or activating additional compressors, then the required cooling load is met, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the operating parameters (flow rate and outlet temperature) of active chillers based on real-time cooling load requirements and environmental conditions. The supervisory control device continuously monitors system state and optimizes the configuration of active chillers, allowing the system to adapt its cooling capacity dynamically without necessarily activating additional compressors, thereby meeting cooling demands while minimizing energy consumption.
2Reliability
If the chiller system operates with multiple active chiller devices, then cooling capacity is increased, but system complexity and energy consumption increase
Solution Approach 1:
The supervisory control device optimizes the operating parameters (flow rate and outlet temperature setpoints) of active chiller devices to match the actual cooling load requirements. By adjusting these parameters dynamically, the system can maintain adequate cooling capacity with an optimized number of active chillers, reducing unnecessary system complexity while still meeting the required cooling demand.
3Ease of operation
If the chiller system uses fixed setpoints for outlet temperature and flow rate, then control is simple, but energy efficiency decreases under varying cooling demands
Solution Approach 1:
The supervisory control device implements a feedback mechanism that continuously monitors the environmental state (cooling load, ambient temperature) and the current configuration of active chillers. Based on this feedback, the control device dynamically adjusts the outlet temperature and flow rate setpoints of active chillers to optimize energy efficiency while meeting the required cooling load, eliminating the need for fixed setpoints and improving energy efficiency under varying cooling demands.
Data Source
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AI summary
A system and method for optimizing management of a system of multiple chiller devices for circulating a chilled medium within an indoor environment determines a current state of the environment (e.g., ambient temperature, temperature of the medium entering and leaving the environment, inlet flow rate of chilled medium, target cooling load) and a current configuration of the chiller system, e.g., flow and outlet temperature setpoints of each active device and total energy consumption of the chiller system. Based on this information the chiller system controller solves (either offline or online) for an optimal chiller system configuration (and steps for achieving this configuration by adjusting chiller device flow and outlet temperature setpoints) for providing the required cooling load to the indoor environment while minimizing total energy consumption across the chiller system. If an optimal solution is found, the controller monitors its implementation.