Blast Furnace Hot Metal Temperature Prediction via Drift Coefficient
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Solution Overview
Problem
Conventional physical models for hot metal temperature prediction in blast furnaces struggle to accurately account for non-uniform gas flow, leading to inaccurate temperature predictions and increased risk of furnace cooling accidents.
Innovation Solution
A method and apparatus that adjust parameters in a physical model to account for gas drift within the furnace, specifically the void ratio, to reduce deviations between calculated and measured reaction amounts, enabling more accurate hot metal temperature predictions and providing operation guidance to maintain stable furnace conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional physical models assume a packed layer with small variation in void ratio, then the model structure remains simple, but the model cannot reproduce non-uniform gas flow and hot metal temperature decrease caused by gas drift
Solution Approach 1:
The patent applies local quality by introducing a drift coefficient that varies locally within the blast furnace model to represent non-uniform gas flow. Instead of assuming uniform packed layer conditions throughout, the model allows the void ratio to vary locally through the drift coefficient, enabling accurate reproduction of gas drift phenomena in specific regions while maintaining the overall model structure.
Solution Approach 2:
The patent changes the parameter representation by introducing a drift coefficient as an additional parameter in the physical model. This parameter modification allows the model to account for non-uniform gas flow and gas drift effects, improving hot metal temperature prediction accuracy without fundamentally changing the model's mathematical framework.
2Reliability
If the physical model does not account for gas drift, then the model remains computationally simple, but the prediction accuracy of hot metal temperature deteriorates
Solution Approach 1:
The patent improves prediction reliability by modifying the physical model parameters to include a drift coefficient that accounts for gas drift. This parameter change enables the model to reproduce non-uniform gas flow patterns and their impact on hot metal temperature, significantly improving prediction reliability for furnace cooling accident prevention.
3Ease of operation
If conventional models use uniform packed layer assumption, then the ease of operation is maintained, but the ability to predict furnace cooling accidents is reduced
Solution Approach 1:
The patent maintains ease of operation by implementing local quality through a drift coefficient that can be applied to the existing packed layer model framework. This approach allows the model to capture non-uniform gas flow effects in critical regions without requiring complete reformulation of the operational model, preserving usability while improving accident prediction reliability.
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 method achieves high accuracy in predicting hot metal temperatures and provides effective operation guidance to prevent furnace cooling accidents by adjusting parameters in the physical model to account for non-uniform gas flow, thereby improving productivity and safety.
Implementation Method 1
a physical model that takes into account reactions and heat transfer phenomena inside the blast furnace
Implementation Method 2
calculating a deviation between the reaction amount calculated using the physical model and a measured reaction amount; adjusting a parameter of the physical model that causes drift in a gas inside the blast furnace
Data Source
AI summary
A hot metal temperature prediction method includes a reaction amount calculation step (S1) of calculating a reaction amount inside a blast furnace using a physical model that takes into account reactions and heat transfer phenomena inside the blast furnace, a deviation calculation step (S2) of calculating a deviation between the reaction amount calculated using the physical model and a measured reaction amount, a model parameter adjustment step (S3) of adjusting a parameter of the physical model that causes drift in a gas inside the blast furnace, so that the calculated deviation is reduced, and a hot metal temperature prediction step (S4) of predicting a future hot metal temperature using the physical model for which the parameter was adjusted.