Boiler Network Control for Early Abnormality Prediction
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Solution Overview
Problem
Boiler systems face challenges in predicting abnormalities proactively due to segregated data from various subcomponents, making it difficult to understand overall performance and prevent malfunctions.
Innovation Solution
A system that collects data from boiler subcomponents, compares it to historical data using probabilistic and deterministic networks, identifies abnormalities, and determines corrective actions based on predefined rules, which can be updated based on the effectiveness of the actions taken.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data is segregated by component for monitoring, then data collection is simplified, but understanding overall boiler performance becomes difficult
Solution Approach 1:
The patent combines segregated component data with system-level data into a unified monitoring framework. The boiler control system integrates data from individual components (burners, heat exchangers, pumps) with overall system performance metrics, allowing simultaneous component-level monitoring and system-level analysis without losing contextual relationships.
2Measurement precision
If data is evaluated after malfunction occurs, then diagnostic accuracy is improved, but preventive action capability is reduced
Solution Approach 1:
The system performs preliminary evaluation of boiler data against historical patterns and performance thresholds before malfunctions occur. By continuously comparing real-time data with established baselines and detecting deviations early, the system enables preventive maintenance actions that prevent malfunctions rather than merely diagnosing them after occurrence.
Solution Approach 2:
The patent implements a feedback mechanism where boiler performance data is continuously monitored, compared with historical data, and used to generate alerts or corrective actions. This closed-loop system provides ongoing diagnostic feedback that maintains accuracy while enabling timely preventive interventions based on trending analysis.
3Reliability
If proactive prediction systems are implemented, then boiler reliability is improved, but system complexity increases
Solution Approach 1:
The boiler control system performs self-diagnosis and self-monitoring by automatically comparing its own operational data against historical patterns and performance criteria. This self-service capability enables proactive prediction of potential failures without requiring external complex monitoring infrastructure, maintaining reliability improvement while limiting complexity growth.
Data Source
AI summary
Systems and methods for boiler regulation are disclosed. The system can receive boiler data from a boiler and compare the boiler data to a normal operating range to detect an abnormality. Based on a plurality of rules, the system can identify an anticipated root cause and at least one corrective action. Based on the at least one corrective action, the system can generate and/or output instructions for the boiler to perform the at least one corrective action. The system can display an indication of the abnormality and/or the at least one corrective action.