Compressor Surge Prediction via Standardized Efficiency Analysis
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Solution Overview
Problem
Existing turbomachines face challenges in predicting and preventing surge events in compressors, which can lead to decreased performance and potential damage, due to high costs associated with local sensors and controllers, and existing remote detection methods cannot completely prevent flow reversal.
Innovation Solution
A system and method using computer processors to analyze performance parameters, determine corrected values, and predict surge events by standardizing compressor efficiency based on historical data, employing machine learning techniques to categorize risk and provide recommendations for mitigating actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local sensors and controllers are used to monitor airflow and pressure rise to detect surge events in early stages, then surge detection accuracy is improved, but system cost increases
Solution Approach 1:
The patent introduces a mediator (centralized processing system with machine learning algorithms) that processes data from existing remote sensors to achieve accurate surge detection without requiring expensive local sensors and controllers at each compressor station. The intermediary system aggregates and analyzes data from multiple sources to compensate for the lack of local measurement capability.
Solution Approach 2:
Instead of installing physical local sensors at each compressor, the system creates a virtual model of compressor behavior using machine learning algorithms that replicate the functions of local surge detection. The AI model copies the detection capability of physical sensors through software-based analysis of available remote data.
2Measurement precision
If remote detection methods are used to determine surge events at early stage, then detection capability is improved, but ability to prevent flow reversal deteriorates
Solution Approach 1:
The system performs preliminary action by detecting surge events at their earliest stages using machine learning analysis of performance parameters, enabling preventive measures to be taken before flow reversal occurs. The AI model identifies subtle changes in compressor efficiency and performance parameters that precede actual surge events, allowing operators to take corrective action in advance.
Solution Approach 2:
The system implements continuous feedback monitoring of compressor performance parameters, comparing real-time data against learned patterns from historical data to detect early signs of surge. The feedback mechanism provides ongoing assessment of surge risk, enabling timely intervention to prevent flow reversal while maintaining reliable operation.
3Use of energy by moving object
If compressor operates at high pressure ratio to achieve higher efficiency, then energy efficiency is improved, but likelihood of surge event increases
Solution Approach 1:
The machine learning system performs preliminary detection of conditions that lead to surge events, analyzing performance parameters to identify when the compressor is approaching surge conditions during high-pressure ratio operation. By detecting early warning signs in the performance data, the system enables preventive action before surge occurs, allowing the compressor to operate efficiently at high pressure ratios with reduced surge risk.
Data Source
AI summary
Systems and methods for predicting a surge event in a compressor of a turbomachine are provided. According to one embodiment of the disclosure, a system may include one or more computer processors associated with the turbomachine. The one or more computer processors may be operable to receive a plurality of performance parameters of the compressor and analyze the plurality of performance parameters to determine corrected performance values of the performance parameters. Based at least partially on the corrected performance values, a compressor efficiency may be determined. The processor may be further operable to standardize the compressor efficiency for a standard mode of operation, ascertain historical performance data associated with the standard mode of operation, and analyze the compressor efficiency based at least partially on the historical performance data. Based on the analysis of the compressor efficiency, a surge event may be selectively predicted.