Furnace Flooding Detection via Steady-State Gain Comparison
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Solution Overview
Problem
Furnace flooding occurs when the combustion of fuel gas becomes unstable due to an imbalance in air and fuel gas flow ratios, leading to a loss of flame and potential explosion, making it difficult to detect using existing technologies.
Innovation Solution
An apparatus and method that identify steady-state gains in the relationship between furnace characteristics and setpoints using data collected during normal operation and compare these to baseline gains to detect actual or potential flooding, employing open-loop model identification and setpoint perturbations to differentiate between stable and unstable combustion conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing detection technologies are used, then the detection system is simple, but furnace flooding cannot be effectively detected
Solution Approach 1:
The system continuously monitors the steady-state gain of the furnace and compares it against baseline values, creating a feedback mechanism that automatically detects flooding conditions. When the gain deviates from expected ranges, the system generates alerts, enabling reliable detection without requiring complex manual intervention systems.
Solution Approach 2:
The detection method utilizes existing furnace operational data and control systems to self-diagnose flooding conditions. By analyzing the relationship between controller setpoints and actual furnace characteristics through steady-state gain calculations, the system performs self-monitoring without requiring separate dedicated sensors or complex additional hardware.
2Measurement precision
If steady-state gain analysis is performed continuously, then flooding detection accuracy improves, but computational resources increase
Solution Approach 1:
The system performs steady-state gain analysis at periodic intervals rather than continuously, calculating the gain by comparing controller setpoints with actual furnace characteristics at discrete time points. This periodic approach maintains detection accuracy while significantly reducing computational energy consumption compared to continuous real-time analysis.
3Productivity
If the furnace operates outside the operating envelope, then productivity increases, but combustion stability decreases leading to flooding
Solution Approach 1:
The steady-state gain analysis provides continuous feedback on combustion stability, allowing operators to maintain high productivity settings while receiving early warnings when the furnace approaches unstable operating conditions. The system detects gain deviations that precede actual flooding events, enabling corrective action before combustion instability compromises safety.
Data Source
AI summary
A method includes identifying a first steady-state gain associated with a relationship between a characteristic of a furnace and a setpoint used by a controller that is configured to control the characteristic of the furnace, The first steady-state gain is identified using data collected when the furnace is not suffering from flooding.The method also includes identifying a second steady-state gain associated with the relationship during operation of the furnace. The method further includes comparing the first and second steady-state gains and identifying actual or potential flooding of the furnace based on the comparison.