HRSG Reliability Modeling for Real-Time SIS Risk Assessment
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Solution Overview
Problem
Existing systems for reliability operations of heat recovery steam generators (HRSG) in turbomachine systems face challenges in predicting and maintaining safety instrumented system (SIS) reliability due to complexity and the need for time-consuming historical data analysis, which hinders efficient maintenance and operation.
Innovation Solution
A model-based reliability system (MRS) is introduced, utilizing a library of reusable component models and a dynamic risk calculation engine (DRCE) to analyze and optimize SIS performance, incorporating Markov models, Fault Tree Analysis, and other techniques for real-time risk assessment and maintenance scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional historical data analysis methods are used for SIS reliability prediction, then comprehensive reliability assessment can be achieved, but the process becomes extremely time-consuming and complex
Solution Approach 1:
The system pre-generates multiple simulated historical datasets with known reliability characteristics before actual analysis is needed. When reliability prediction is required, the engine can quickly query pre-computed results from these simulated datasets rather than performing time-consuming analysis of actual historical data, significantly reducing prediction time while maintaining accuracy
Solution Approach 2:
Instead of analyzing complex actual historical SIS data directly, the system creates simplified copies or representations of historical data through simulation. These simulated datasets replicate the essential characteristics and patterns of real data but in a controlled, computationally efficient format that enables rapid reliability assessment
2Reliability
If detailed component-level modeling is performed for the entire SIS, then accurate reliability prediction is achieved, but the system complexity increases significantly
Solution Approach 1:
The SIS is divided into discrete functional components and subsystems, each with its own simplified reliability model. The dynamic risk calculation engine combines these segmented component models to predict overall system reliability, allowing accurate assessment without requiring a single complex monolithic model of the entire system
Solution Approach 2:
The system uses probabilistic parameters and statistical distributions to represent component behaviors and failure modes. By changing from deterministic modeling to probabilistic parameter-based modeling, the system achieves more accurate reliability predictions while using standardized, manageable parameter sets for each component type
3Reliability
If frequent proof testing is performed to maintain safety, then safety integrity is improved, but system downtime and operational disruption increase
Solution Approach 1:
The proof test scheduling system dynamically adjusts test intervals based on real-time risk assessments and predicted reliability trends. Instead of fixed periodic testing, the system optimizes test timing to maintain safety integrity while minimizing disruptions to system availability and productivity
Solution Approach 2:
The system continuously monitors SIS performance and reliability metrics, using this feedback to adjust proof test schedules. When reliability is high, test intervals can be extended; when reliability decreases or risk increases, testing frequency is increased, creating an adaptive balance between safety and productivity
Data Source
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AI summary
A system for improved reliability operations of a system with a heat recovery steam generator (HRSG) are presented. The system includes a processor configured to execute a model library to model a safety system. The model library (22) includes a plurality of subsystem models (24, 26, 28, 30, 32, 34) each configured to derive a reliability measure. The system also includes the HRSG and an HRSG advisory system (39). The HRSG advisory system (102) may be executed by the processor and is configured to receive one or more condition monitoring algorithm results, receive one or more measurements; and determine a probability of failure for the HRSG based at least in part on the one or more condition monitoring algorithm results, the one or more measurements, the model library (22), or a combination thereof. Moreover, determining the probability of failure includes determining a most likely state of a plurality of states of the HRSG.