Molten Metal Carbon Estimation via Dynamic Model Switching
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Solution Overview
Problem
The existing method for determining the timing of sub-lance measurement in the steel refining process is based on a static model, which can lead to inaccuracies due to measurement errors or disturbances, potentially resulting in sub-optimal dynamic control and decreased oxygen efficiency in decarburization, especially when the carbon concentration exceeds the critical level.
Innovation Solution
A molten metal component estimation device that uses measurement information from the refining facility, including optical characteristics and model expressions, to accurately estimate carbon concentration by switching between static and dynamic models based on the intensity change rate of the spectrum from iron oxide reduction reactions, ensuring precise control of oxygen feed during the blowing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the timing of sub-lance measurement is determined based on a static model, then the control process is simple, but measurement errors or disturbances can cause inaccuracies in carbon concentration estimation and insufficient time for dynamic control
Solution Approach 1:
The patent transitions from a static model-based measurement timing determination to a dynamic model-based approach. The dynamic model continuously adapts to changing process conditions (carbon concentration, oxygen efficiency, measurement errors, disturbances) to determine the optimal timing for sub-lance measurement. This dynamic adaptation ensures accurate carbon concentration estimation while providing sufficient time for dynamic control, resolving the contradiction between operational simplicity and measurement precision.
2Duration of action of moving object
If dynamic control is performed from a state where carbon concentration is higher than critical carbon concentration, then sufficient time is ensured for dynamic control, but oxygen efficiency in decarburization decreases due to iron oxide reduction in the slag
Solution Approach 1:
The patent applies preliminary action by using the dynamic model to predict and determine the optimal timing for sub-lance measurement before the carbon concentration reaches the critical level. This advance determination ensures that measurement and control actions are taken at the most effective moment, maximizing oxygen efficiency in decarburization while ensuring sufficient time for dynamic control. The dynamic model anticipates the critical carbon concentration point and schedules measurement accordingly, preventing the need to operate from higher carbon concentrations where efficiency deteriorates.
3Ease of operation
If the static model is used to determine measurement timing, then the system is easier to operate, but errors in static model calculation can prevent sub-lance measurement at the target carbon concentration
Solution Approach 1:
The patent implements feedback by continuously monitoring actual process parameters (carbon concentration, oxygen efficiency) and using the dynamic model to compare predicted values with actual measurements. This feedback mechanism allows the system to adjust measurement timing dynamically based on actual process conditions, ensuring accurate carbon concentration estimation. The feedback loop compensates for measurement errors and disturbances, maintaining reliability of measurement timing while keeping the system easy to operate through automated adaptive control.
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 allows for accurate estimation of carbon concentration in molten metal, particularly at the final stage of the blowing process, ensuring high yield and desired component concentrations in the manufactured molten metal.
Implementation Method 1
measurement results regarding an optical characteristic at a furnace throat in the refining facility during a blowing process
Implementation Method 2
intensity change rate of a spectrum resulting from an iron oxide reduction reaction in a slag
Data Source
AI summary
A molten metal component estimation device including: an input device configured to receive measurement information about a refining facility including measurement results regarding an optical characteristic; a model database that stores model expressions and model parameters, regarding a blowing process reaction, including a model expression and model parameters representing a relation between the oxygen efficiency in decarburization and a carbon concentration in a molten metal in the refining facility; and a processor configured to: estimate component concentrations of the molten metal including the carbon concentration in the molten metal by using the measurement information, the model expressions and the model parameters; estimate the carbon concentration in the molten metal based on the measurement results; and determine the model expression and the model parameters to be used when estimating the component concentrations of the molten metal, based on the estimation result of the carbon concentration in the molten metal.