Fuzzy Logic Approach for Modeling of Heating and Scale Formation in Industrial Furnaces

Area of Science:

• Materials Science
• Process Engineering
• Computational Intelligence

Background:

• Steel heating is crucial for charge formation but highly energy-intensive.
• Minimizing scale formation is key to reducing heat consumption during steel processing.
• Industrial re-heating furnaces are critical for hot rolling mill operations.

Purpose of the Study:

• To develop a fuzzy logic-based model for predicting heating and scale formation in industrial re-heating furnaces.
• To minimize excess energy consumption during the steel charge heating process.
• To provide a tool for predicting final surface temperature and scale thickness.

Main Methods:

• Application of fuzzy logic to model the complex interactions of heating and scale formation.
• Utilizing experimental data on initial charge temperature, heating time, excess air coefficient, and initial scale thickness as model inputs.
• Validation of the model using data from walking beam furnaces in hot rolling mill departments.

Main Results:

• A fuzzy logic-based heating and scale formation (HSF) model was successfully developed.
• The model accurately predicts the final surface temperature of the steel charge.
• The model effectively predicts the final thickness of the scale layer formed during heating.
• Model accuracy was confirmed through comparison with measured results.

Conclusions:

• Fuzzy logic provides an acceptable approach for modeling heating and scale formation in industrial furnaces.
• The developed HSF model can aid in optimizing heating processes to reduce energy consumption.
• Accurate prediction of temperature and scale thickness is achievable, supporting efficient steel production.