During the steelmaking process, errors in endpoint judgment may lead to quality problems of steel, such as unqualified composition and substandard temperature. Here are some methods to reduce the quality risks caused by errors in steelmaking endpoint judgment:

1. Optimize Detection Technology

 

Adopt Advanced Sensors
Install high-precision temperature sensors and composition analyzers inside the steelmaking furnace. For example, use Laser-Induced Breakdown Spectroscopy (LIBS) sensors. This kind of sensor can analyze the chemical compositions in molten steel in real time and quickly, including the contents of main elements such as carbon, silicon, and manganese. Compared with traditional chemical analysis methods, the LIBS technique can complete the detection within a few seconds, greatly shortening the detection time and having relatively high accuracy.
For temperature measurement, an immersion thermocouple can be used, which can be directly inserted into the molten steel to accurately measure the temperature of the molten steel, with the error range controlled within ±5 °C, providing precise data support for the temperature control at the steelmaking endpoint.

Increase Detection Frequency
Increase the number of detections at key stages of the steelmaking process, such as in the later stage of blowing. For example, if the composition of molten steel was originally detected once every 5 minutes, it can be shortened to once every 2 - 3 minutes when approaching the endpoint. This can capture the changes in the composition and temperature of the molten steel more timely, so as to judge the steelmaking endpoint more accurately.

2. Establish Precise Process Control Models

 

Data Collection and Analysis
Collect a large amount of historical steelmaking data, including the information of raw materials of different steel grades and different heats, blowing process parameters (such as oxygen flow rate, blowing time, etc.), and endpoint detection data. Through data analysis software, such as data mining tools (for example, RapidMiner, etc.), conduct in-depth mining on these data to find out the key factors affecting the steelmaking endpoint and their interrelationships.
For example, through the analysis of a large amount of data, it is found that for a certain steel grade, there is a certain correlation between the carbon content and temperature at the endpoint under a specific combination of oxygen flow rate and blowing time. Based on this, corresponding mathematical models can be established to predict the steelmaking endpoint.

Model Establishment and Verification
According to the analysis results, establish process control models, such as a steelmaking endpoint prediction model based on neural networks. This kind of model can learn the patterns in historical data and predict the composition and temperature at the steelmaking endpoint. After establishing the model, use the data of new heats to verify and optimize the model to ensure its accuracy and reliability.
For example, after a certain steel plant applied the neural network model, the prediction error of the carbon content at the steelmaking endpoint was reduced from the original ±0.05% to ±0.02%, effectively improving the accuracy of endpoint judgment.

 

 

 

 

 

 

 

 

 

 

Specification we supply：

Thickness	3-200mm
Width	1500-4000mm
Length	5000-15000mm

 

 

 

 

 

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