Chiller system with intelligent control
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Solution Overview
Problem
Chiller systems in semiconductor fabrication facilities consume a significant amount of power, accounting for up to 20% of total energy usage, posing challenges for reducing power consumption and achieving net zero carbon emissions.
Innovation Solution
A control system utilizing machine learning techniques to monitor and adjust operating conditions of chiller systems by sensing temperatures, pressures, and cooling loads, predicting power consumption, and optimizing the number of chiller systems and operational parameters for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chiller systems operate continuously to meet cooling demands in semiconductor fabrication facilities, then cooling reliability is maintained, but power consumption increases significantly
Solution Approach 1:
The system dynamically adjusts chiller operation by transitioning between different operational states (active, standby, hibernate) based on real-time cooling demands and environmental conditions. This dynamic state management allows the system to maintain cooling reliability when needed while reducing power consumption during lower-demand periods through intelligent state transitions and predictive modeling.
2Device complexity
If traditional control methods are used to manage chiller systems, then system simplicity is maintained, but energy efficiency deteriorates
Solution Approach 1:
The system implements multi-layer feedback mechanisms including real-time sensor data collection, predictive modeling of cooling demands, and continuous optimization of operational parameters. The feedback loop analyzes environmental conditions, equipment status, and power consumption patterns to automatically adjust chiller operation, thereby improving energy efficiency while managing system complexity through automated control algorithms.
Solution Approach 2:
The patent replaces traditional mechanical control systems with machine learning-based predictive models and intelligent algorithms. These software-based systems analyze historical and real-time data to predict cooling demands and optimize chiller operation, substituting complex mechanical control mechanisms with computational intelligence that achieves superior energy efficiency.
3Power
If the number of chiller systems is increased to meet peak cooling demands, then cooling capacity is improved, but power consumption and system complexity increase
Solution Approach 1:
The system enables individual chiller units to perform multiple functions across different operational states (active cooling, standby readiness, hibernate power-saving mode). This multi-functionality allows a smaller number of versatile chillers to replace multiple specialized units, maintaining peak cooling capacity while reducing overall power consumption and system complexity through intelligent state management.
Data Source
AI summary
A chiller system provides cooling for a semiconductor fabrication facility. The chiller system includes a control system. The control system utilizes one or more analysis models trained with a machine learning process to intelligently assist in reducing the power consumption and enhancing the efficiency of the chiller system.