Ladle Furnace Refractory Temperature Prediction Without Runtime CFD
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Solution Overview
Problem
The challenge in maintaining an optimal superheat level for steel casting in ladle furnaces lies in accurately predicting refractory temperature losses, which are difficult to measure due to high operational temperatures and instability of sensors at refractory points, with refractory temperature loss estimation being the most challenging aspect.
Innovation Solution
A processor-implemented method using Computational Fluid Dynamics (CFD) modeling and Artificial Neural Networks (ANN) to predict the preheat refractory temperature profile by generating simulated data and transforming it into a reduced form, allowing for the training of an ANN model to accurately predict refractory temperature profiles for ladle furnace operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature sensors are used to measure refractory temperature profile, then measurement data can be obtained, but sensor stability and placement become very difficult due to high operational temperatures around 1600°C
Solution Approach 1:
The patent creates a virtual copy of the refractory temperature profile through CFD simulation rather than using physical sensors. The CFD model replicates the thermal behavior of the refractory lining, providing measurement data without requiring physical contact with the high-temperature environment. This virtual copying approach eliminates sensor stability issues while maintaining measurement capability.
2Measurement precision
If CFD simulations are used to predict refractory temperature profile, then accurate prediction can be achieved, but computational resources and time are heavily consumed
Solution Approach 1:
The patent performs CFD simulations in advance during the preheating phase to establish the initial refractory temperature profile before steelmaking operations begin. By conducting the computationally intensive CFD analysis beforehand rather than in real-time during production, the system achieves accurate temperature predictions while minimizing impact on production time. The pre-computed results are then used for subsequent process optimization.
3Manufacturing precision
If tight range of ladle furnace outlet temperature is maintained, then superheat level can be optimized, but prior prediction of refractory temperature loss is required which is very challenging
Solution Approach 1:
The patent introduces CFD simulation as an intermediary tool between the known geometric/process parameters and the unknown refractory temperature loss. The CFD model acts as a virtual mediator that computes the thermal interaction between the refractory lining and the molten steel, providing predicted temperature profiles that inform the steelmaking process control without requiring direct measurement or complex analytical models.
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
This approach enables accurate prediction of refractory temperature profiles, overcoming the limitations of sensor instability and resource-intensive CFD simulations, thereby maintaining a tight temperature range for efficient steel casting by compensating for temperature losses during ladle furnace operations.
Implementation Method 1
refractory temperature loss includes loss to the surrounding from the refractory walls and heat absorbed by the refractory itself
Implementation Method 2
loss to the surrounding from the refractory walls
Data Source
AI summary
The present disclosure addresses the technical problem of prediction of a preheat refractory temperature profile of a ladle furnace. Operational temperature of the ladle furnace, stability of sensors and placement make sensors not feasible. Computational Fluid Dynamics (CFD) simulations require large computation time and cannot be used for runtime applications in plants. The method and system of the present disclosure uses CFD modeling to carry out parametric study to generate data which is further processed to train an Artificial Neural Network (ANN) model that serves as a prediction model for predicting the preheat refractory temperature profile for at least a portion of the side refractory and at least a portion of the bottom refractory layer separately for which a new set of input data is obtained. The trained prediction model of the present disclosure provides a quick runtime prediction in plants.