Thermodynamic Process Monitoring Using Statistical Performance Evaluation
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Solution Overview
Problem
Current performance monitoring methods for thermodynamic devices and processes are outdated, relying on manufacturer design values rather than actual operating conditions, leading to inaccurate loss calculations and inefficient operation, particularly in fuel burning boilers where soot buildup affects heat transfer efficiency.
Innovation Solution
Implementing a statistical analysis-based system to evaluate achievable performance by collecting and analyzing real-time data, deriving correction functions from actual operating conditions, and using these to monitor and control thermodynamic processes, such as soot blowing in fuel burning boilers, to optimize efficiency and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manufacturer design values are used as reference for performance monitoring, then the monitoring system is simple to implement, but the accuracy of performance evaluation deteriorates due to device degradation and operational changes over time
Solution Approach 1:
The system implements feedback by continuously monitoring actual device performance and using statistical analysis to update reference values over time. This creates a self-correcting mechanism where the monitoring system adapts to device degradation and operational changes, maintaining accuracy without requiring complex manual recalibration or replacement of reference data.
Solution Approach 2:
The system dynamically changes the reference performance parameters from fixed manufacturer design values to statistically derived values that evolve with device operation. By using historical performance data to establish baseline parameters and continuously updating these based on actual operational statistics, the system maintains accuracy while keeping the implementation relatively simple.
2Measurement precision
If statistical analysis of real-time data is implemented to determine achievable performance, then performance monitoring accuracy improves, but data processing complexity increases
Solution Approach 1:
The system performs self-service by automatically collecting operational data, conducting statistical analysis, and generating performance evaluations without requiring external intervention. The automated statistical processing uses standard algorithms to derive achievable performance metrics from historical data, reducing the need for complex manual analysis while maintaining high accuracy.
Solution Approach 2:
The system replaces complex manual performance evaluation methods with automated statistical analysis. By using computer-based algorithms to process operational data and determine achievable performance, the system achieves high monitoring accuracy while the computational complexity is managed through standardized statistical techniques rather than complex mechanical or manual procedures.
3Reliability
If performance is monitored against theoretical design values, then the monitoring methodology remains consistent with original specifications, but the ability to detect actual performance losses deteriorates due to device aging and modifications
Solution Approach 1:
The system transitions from static reference values to dynamic performance baselines that adapt to changing operating conditions and device states. By continuously analyzing historical data under varying operational parameters, the system maintains reliable detection capability even as the device ages or undergoes modifications, effectively tracking achievable performance rather than fixed theoretical values.
Solution Approach 2:
The system performs preliminary statistical analysis of historical performance data to establish baseline achievable performance before comparing current operations. This advance preparation creates a dynamic reference framework that accounts for device aging and operational changes, enabling reliable detection of actual performance losses without requiring continuous theoretical recalculations.
Data Source
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AI summary
A method of controlling a thermodynamic process is disclosed. The method comprises operating the process according to a first operational state for a first period of time, determining performance parameter values of the process during the first period of time, determining a performance parameter statistical value from the performance parameter values, and evaluating the performance parameter statistical value to determine a change in an operating parameter of the first operational state.