Heat Transfer Device Performance Monitoring via Anomaly Detection
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Solution Overview
Problem
Existing monitoring systems for heat transfer devices in power plants are reactive and unable to predict impending failures, leading to inefficiencies and increased maintenance costs due to fouling, slagging, and corrosion, which reduce the performance of components like boilers and air preheaters over time.
Innovation Solution
A system comprising an analysis module to compute performance indicators and predict performance degradation, using a knowledge-based network to diagnose probable causes and estimate time to failure, and applying anomaly reduction media to prevent failures, thereby isolating the effects of process parameters and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing reactive monitoring systems are used to detect boiler failures, then system simplicity is maintained, but failure prediction capability is lost and maintenance costs increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing operational data (temperatures, pressures, fuel consumption) to detect early signs of fouling and slagging before they cause failures. The analysis module computes performance indicators and compares them against baseline values to predict impending failures, enabling proactive maintenance scheduling.
Solution Approach 2:
The monitoring system is segmented into distinct functional modules: data collection from sensors, performance indicator computation, baseline comparison, anomaly detection, and maintenance recommendation. This modular architecture allows the complex prediction capability to be built from simpler, manageable components that can be implemented and maintained separately.
2Productivity
If boiler operation continues without intervention, then productivity is maintained, but fouling and slagging accumulate causing efficiency loss
Solution Approach 1:
The system establishes a feedback loop where operational data is continuously monitored, performance indicators are computed and compared against baselines, and maintenance recommendations are generated when degradation thresholds are exceeded. This feedback mechanism enables continuous operation while systematically managing fouling accumulation through timely interventions.
Solution Approach 2:
The monitoring system dynamically adjusts its analysis based on varying operational conditions. It adapts baseline comparisons and anomaly detection thresholds according to actual operating parameters, allowing the system to maintain sensitivity to degradation signs while accommodating normal operational variations that affect productivity.
3Reliability
If chemicals are added to fuel to reduce fouling and slagging, then component performance is protected, but operational cost increases
Solution Approach 1:
The system enables self-service by using the boiler's own operational data and performance indicators to detect degradation and trigger maintenance actions. This self-monitoring approach replaces or reduces the need for chemical additives, as the system autonomously identifies when cleaning or maintenance is required based on actual performance degradation rather than preventive chemical treatment.
4Loss of time
If reactive maintenance is performed after failure detection, then system simplicity is maintained, but downtime and maintenance costs increase
Solution Approach 1:
The system performs preliminary maintenance actions by predicting failures before they occur. The analysis module identifies degradation trends and schedules maintenance during planned outages rather than after unexpected failures, reducing unplanned downtime and allowing for more efficient maintenance planning and resource allocation.
Data Source
AI summary
A method is provided for the monitoring of heat transfer devices. The method includes the acts of receiving data from a heat transfer device, and computing a performance indicator indicative of an incipient anomaly condition of the heat transfer device based upon the received data, and/or computing a normalized efficiency of the heat transfer device. The normalized efficiency represents a corrected efficiency that isolates effects of a process parameter on performance of the heat transfer device. The data represents a measurable process parameter or a change in a measurable process parameter in the heat transfer device. The method receives the data and computes a performance indicator to predict performance degradation of the heat transfer device over time based upon the received data.