Thermal Scanning AI for Refractory Vessel Degradation
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Solution Overview
Problem
Current methods for monitoring the condition of refractory-based vessels in manufacturing are inadequate, as they fail to reliably detect cracks, molten material penetration, and refractory degradation, leading to increased risks of leakage and premature vessel shutdown.
Innovation Solution
A system and method integrating thermal scanning with machine learning-based mathematical models to estimate the level of risk of operating manufacturing vessels by correlating external surface temperatures with operational and process parameters, thereby detecting refractory degradation and molten material penetration in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If thermal scanning devices and visual inspections are used to monitor vessel condition, then the cost of monitoring is reduced, but the reliability of detecting refractory degradation and cracks is insufficient
Solution Approach 1:
The patent combines multiple sensing technologies (thermal imaging cameras, infrared detectors, laser scanners, ultrasonic sensors) into an integrated monitoring system. This merging of different sensing modalities allows the system to overcome the limitations of individual methods and achieve both cost-effectiveness and high reliability in detecting refractory degradation, cracks, and molten material penetration.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary that processes data from multiple sensors and correlates it with operational parameters. This intermediary layer enables the system to reliably detect subtle signs of refractory degradation that would be missed by simple thermal scanning or visual inspection alone, while maintaining cost efficiency through automated analysis.
2Measurement precision
If laser scanning devices are used to detect refractory thickness and cracks, then measurement precision is improved, but the device complexity and cost increase
Solution Approach 1:
The patent segments the monitoring system into multiple specialized sensing components, each optimized for specific detection tasks. Laser scanners are used only where high precision is needed for crack detection, while other areas use simpler thermal or ultrasonic sensors. This segmentation reduces overall system complexity while maintaining high measurement precision where required.
Solution Approach 2:
The patent applies laser scanning selectively to critical areas of the vessel where crack detection is most important, rather than scanning the entire vessel surface. This partial action approach maintains high measurement precision for critical detections while reducing device complexity and operational burden.
3Reliability
If conservative shutdown decisions are made to reduce leakage risk, then operational safety is improved, but productivity decreases due to premature vessel shutdown
Solution Approach 1:
The patent implements continuous monitoring with real-time feedback from multiple sensors that track refractory condition and operational parameters. This feedback enables dynamic adjustment of operational decisions, allowing the vessel to remain operational when conditions are safe while providing early warning when degradation reaches critical levels, thus optimizing both safety and productivity.
Solution Approach 2:
The system performs preliminary detection of refractory degradation trends before they reach critical failure points. By identifying early signs of erosion, cracking, or molten material penetration, the system allows for planned maintenance scheduling that extends vessel uptime while maintaining safety, avoiding both premature shutdowns and catastrophic failures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides early warnings of operational risks, enabling manufacturers to plan maintenance effectively, extend the operational life of vessels, and reduce the likelihood of costly leaks and downtime.
Implementation Method 1
A thermal scanning subsystem to collect data for determining a temperature of an external surface of a manufacturing vessel
Data Source
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AI summary
Disclosed is a system and a method for estimating a level of risk of operation of a manufacturing vessels used in the formation of certain materials. The system and method are operative to determine a condition and level of degradation of the refractory material of the vessel to early warn a user of the operational risk of continuing operating the vessel, based on thermal scanning and the use of artificial intelligence. The system is capable of determining the presence of certain flaws within the refractory material and the remaining thickness of such material by correlating the results of processing thermal data corresponding to the external surface of the vessel with a machine learning-based mathematical model, according to a set of operational parameters related to the melting process and data from the user.