Chilled Water System Predictive Modeling for Set Point Optimization
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Solution Overview
Problem
Current cooling systems, particularly chilled water systems in data centers, lack the ability to model and predict how varying equipment set points affect performance parameters such as total gallons per minute, temperature differential, and supply chilled water temperature, and how changes in one component's set points impact other components in the system.
Innovation Solution
A comprehensive system and method that includes modules for calculating and optimizing the performance of various components of a chilled water system, allowing for predictive modeling and optimization of set points to achieve cost-effective and efficient operation by considering user-defined inputs and ambient conditions, using data sheets and flowcharts to analyze and adjust equipment staging and set points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If comprehensive predictive modeling of cooling system components is implemented, then energy efficiency and cost savings are improved, but system complexity and computational requirements increase
Solution Approach 1:
The cooling system is divided into discrete components (chillers, cooling towers, pumps, heat exchangers) with individual performance models. Each component has its own set of performance curves and parameters that can be independently analyzed and optimized, allowing comprehensive system modeling without overwhelming complexity.
Solution Approach 2:
Performance data and curves for all system components are collected and stored in advance in a database. This preliminary data gathering enables the predictive modeling system to quickly evaluate different operating scenarios without requiring real-time complex calculations, reducing computational burden during operation.
2Measurement precision
If detailed performance curves and parameters for all components are collected and analyzed, then prediction accuracy is improved, but data collection and processing time increase
Solution Approach 1:
All necessary performance data, curves, and parameters for system components are collected and stored in a database during the design and commissioning phase. This preliminary data collection eliminates the need for extensive real-time data gathering when predictions are needed, significantly reducing processing time while maintaining high accuracy.
Solution Approach 2:
Instead of collecting actual performance data in real-time, the system uses manufacturer-provided performance curves and characteristic data as copies of actual component behavior. These representative curves capture essential performance characteristics without requiring extensive field measurements.
3Productivity
If the system optimizes set points for multiple components simultaneously, then overall system performance is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The optimization process addresses each system component separately using individual performance models and set of performance curves. By segmenting the optimization into discrete component-level analyses, the system can evaluate different set points for chillers, cooling towers, and pumps independently, then integrate results to determine overall system optimization without requiring complex simultaneous multi-variable calculations.
4Measurement precision
If real-time feedback from actual equipment performance is used, then model accuracy is improved, but system responsiveness and processing speed may be reduced
Solution Approach 1:
The system incorporates feedback mechanisms where actual performance data from operating equipment is used to update and refine the predictive models. This feedback loop allows the system to learn from real-world performance and improve accuracy over time while maintaining the ability to provide timely predictions through the use of pre-established performance curves.
Data Source
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AI summary
A method is disclosed for analyzing performance of a chilled water (CW) system having a plurality of CW components. The method may consider a collection of at least one of allowable operating points, allowable operating ranges or allowable operating conditions, for each one of the CW components. A user set or system measured ambient wet bulb (WB) temperature may be considered for an environment in which at least a subplurality of the CW components are located. Equivalent loop conditions may be calculated for each of the CW components covering a load being thermally managed by the CW system. For each one of the calculated equivalent loop conditions, a processor may generate information for balancing the CW components to meet load requirements, and then analyze and select a balance condition that yields the user preferable optimization.