Central Plant Asset Model Adaptation for Equipment Derating
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Solution Overview
Problem
Central plant asset models used for optimizing operations often become outdated due to derating of equipment over time, leading to inaccurate predictions and control decisions, as they do not account for real-world operational data and efficiency changes.
Innovation Solution
A system and method that regularly updates asset models by comparing design curves with operational data, calculating a degradation factor, and generating an operational curve to reflect the actual performance of assets, such as chillers and cooling towers, allowing for more accurate control decisions and optimizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If asset models are updated regularly to reflect equipment derating, then prediction accuracy and control decision quality improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system implements feedback by continuously comparing operational data against design curves and updating asset models based on degradation factors. The control system uses updated operational curves to make informed control decisions, which then affect equipment operation, creating a closed-loop feedback system that progressively improves prediction accuracy while managing complexity through automated model adaptation.
2Measurement precision
If operational data is collected and analyzed to determine degradation factors, then model accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing operational data and establishing design curves during commissioning or baseline periods. Degradation factors are calculated by comparing current operational data against these pre-established design curves, allowing for rapid model updates without requiring extensive real-time computational resources for curve generation each time.
3Reliability
If design curves are derated based on degradation factors, then operational predictions become more realistic, but the complexity of curve generation and maintenance increases
Solution Approach 1:
The system applies dynamics by transitioning from static design curves to dynamic operational curves that automatically adapt to equipment degradation. The operational curves are continuously updated based on calculated degradation factors, allowing the model to evolve with equipment condition while maintaining a systematic approach to curve generation and maintenance through automated derating processes.
Data Source
AI summary
A system for controlling a subplant comprising one or more assets includes one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations including generating a design curve for a first asset included in the subplant based on an asset model, the design curve comprising a plurality of data points that define an operation of the first asset, obtaining operational data for the first asset, determining a degradation factor for the first asset by comparing the design curve and the operational data, generating an operational curve for the first asset by derating the design curve based on the degradation factor, and operating the subplant based on the operational curve.