LF Refining Slagging Lime Prediction Model
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Solution Overview
Problem
Existing steel plants face challenges in accurately controlling the addition amount of slagging lime during ladle furnace (LF) refining due to complex physicochemical reactions, leading to unstable molten steel quality and inefficient production processes, which hinders the advancement of intelligent manufacturing technologies.
Innovation Solution
A method and system that predict the addition amount of slagging lime using a combination of mechanism analysis and historical data, calculating the actual sulfur distribution ratio and final slag mass to determine the required lime addition, thereby stabilizing molten steel components and improving production efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If continuous sampling and operational experience are used to control slagging material addition, then the control method is simple and easy to operate, but the component fluctuation is large and steel quality is unstable
Solution Approach 1:
The patent replaces the manual operational experience-based control method with an automated mathematical model system. The model uses input parameters (initial sulfur content, target sulfur content, molten steel mass, slag mass, lime CaO content) to automatically calculate the required lime addition amount, substituting human judgment with a deterministic calculation system that eliminates subjective variability and improves consistency.
Solution Approach 2:
The control system enables the refining process to self-determine the optimal lime addition amount through the mathematical model, rather than relying on external operator expertise. The model independently processes the input parameters and generates the control recommendation, making the system self-sufficient in determining the addition amount without requiring continuous operator intervention or experience.
2Device complexity
If continuous sampling and operational experience are used to control slagging material addition, then the control process is simple, but material waste occurs and production efficiency is affected
Solution Approach 1:
The mathematical model calculates and determines the optimal lime addition amount before the actual addition process occurs. By pre-calculating the required amount based on initial parameters and target specifications, the system avoids trial-and-error additions and unnecessary material waste, ensuring that the correct amount is added in one step, thereby improving production efficiency and reducing material waste.
Solution Approach 2:
The model incorporates feedback from the refining process by using actual measured parameters (initial sulfur content, target sulfur content, masses of steel and slag) to adjust and optimize the lime addition calculation. This feedback mechanism ensures that the control system adapts to actual process conditions rather than following fixed procedures, improving both accuracy and efficiency.
3Device complexity
If a single metallurgical mechanism model is used for sulfur removal, then the model is simple to construct, but it is difficult to achieve effective control due to complex and uncontrollable reactions
Solution Approach 1:
The patent transforms the complex metallurgical control problem into a manageable calculation by changing the approach from modeling complex chemical reactions to using a practical mass balance equation based on measurable parameters. The model uses sulfur distribution ratio and material conservation principles with actual process parameters (sulfur content, masses, CaO content) to determine lime addition, bypassing the need to model complex reaction mechanisms while achieving reliable 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
The method achieves accurate prediction of lime addition with a deviation of within 20%, enhancing production efficiency, reducing material waste, and advancing intelligent manufacturing technologies by stabilizing molten steel components and improving process accuracy.
Implementation Method 1
calculating an actual sulfur distribution ratio in combination with a Kungliga Tekniska Högskolan (KTH) model and a least square method by using LF refining parameters
Implementation Method 2
calculating a mass of final slag according to a principle of sulfur mass conservation by using the LF refining parameters and the actual sulfur distribution ratio obtained in S1
Implementation Method 3
calculating, according to a principle of material conservation during LF refining, an addition amount of slagging lime during the LF refining by using the LF refining parameters and the mass of the final slag obtained in S2
Data Source
AI summary
A method and system for predicting an addition amount of slagging lime during ladle furnace (LF) refining, and an LF refining method are provided. The method includes: S1: calculating an actual sulfur distribution ratio in combination with a Kungliga Tekniska Högskolan (KTH) model and a least square method by using LF refining parameters; S2: calculating, according to a principle of sulfur mass conservation, a mass of final slag by using the LF refining parameters and the actual sulfur distribution ratio obtained in S1; and S3: calculating, according to a principle of material conservation during LF refining, an addition amount of slagging lime during the LF refining by using the LF refining parameters and the mass of the final slag obtained in S2, thereby predicting the addition amount of the required slagging lime.