Refractory Lining State Determination Using Data-Driven Modeling
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Solution Overview
Problem
Current methods for determining the service life and maintenance needs of refractory linings in metallurgical vessels are limited by inaccurate detection of wear mechanisms, restricting mathematical determination and requiring manual decisions.
Innovation Solution
A method that collects and stores data on refractory lining thickness, material properties, operational use, and wear patterns, generating a calculation model to optimize service life and maintenance by evaluating these data sets and providing automated decision-making for repairs and usage adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If mathematical models are used to determine refractory lining service life, then automated decision-making is enabled, but accurate detection of wear mechanisms is insufficient
Solution Approach 1:
The patent implements feedback by continuously monitoring actual wear patterns through measurement devices and comparing them against mathematical model predictions. The model parameters are adjusted based on actual wear data, creating a closed-loop system that improves measurement precision while maintaining automation. This allows the system to learn from actual wear mechanisms and refine its predictions.
Solution Approach 2:
The patent replaces manual mechanical measurement and inspection methods with automated mathematical modeling and computational analysis. By substituting mechanical wear detection with digital modeling that incorporates multiple data sources (temperature, pressure, material properties), the system achieves both automation and improved precision in wear mechanism detection.
2Ease of operation
If manual methods are used to determine refractory lining service life, then flexibility in decision-making is maintained, but productivity is reduced due to manual intervention
Solution Approach 1:
The patent implements dynamics by creating a flexible mathematical model that can adapt to different operating conditions, material types, and wear patterns. The model dynamically adjusts its predictions based on real-time data from temperature sensors, pressure measurements, and wear monitoring, enabling automated decisions that remain flexible and adaptable to changing circumstances.
Solution Approach 2:
The system performs self-service by automatically monitoring its own performance, detecting wear patterns, and making maintenance decisions without human intervention. The mathematical model self-updates using collected data, and the system autonomously determines optimal maintenance timing and requirements, significantly improving productivity while maintaining operational flexibility.
3Measurement precision
If comprehensive measurement of refractory lining is performed, then accurate wear assessment is achieved, but measurement time and complexity increase
Solution Approach 1:
The patent applies preliminary action by continuously collecting and storing operational data (temperature profiles, pressure variations, material composition) during normal vessel operation. This pre-collected data is then used by the mathematical model to predict wear patterns without requiring extensive measurement time during actual wear assessment, as the model can process historical data efficiently.
Solution Approach 2:
The patent uses partial action by measuring and monitoring only the most critical parameters and locations that have the greatest impact on refractory wear. Instead of comprehensive measurement of all surfaces and conditions, the system focuses on key wear indicators and uses the mathematical model to extrapolate overall lining condition, significantly reducing measurement time while maintaining assessment accuracy.
Data Source
AI summary
Method for determining the state of a fire-resistant lining of a vessel containing molten metal in particular in which maintenance data, production data, and wall thicknesses at least at locations with the highest degree of wear are measured or ascertained together with additional process parameters of at least one identical/similar vessel after the vessel has been used. The data is collected and stored in a data structure. A calculating model is generated from at least some of the measured or ascertained data or parameters, and the data or parameters are evaluated using the calculating model using calculations and subsequent analyses. Thus, related or integral ascertaining processes and subsequent analyses can be carried out, on the basis of which optimizations relating to both the vessel lining as well as the complete process of the molten metal in the vessel are achieved.