Related Experiment Video
Updated: Aug 5, 2026
10:03
Uncoupling Coriolis Force and Rotating Buoyancy Effects on Full-Field Heat Transfer Properties of a Rotating Channel
Published on: October 5, 2018
RF-KNN-Assisted Local Gaussian Process Regression for Heat Transfer Coefficient Prediction in Hot Strip Coiling
Dong Chen1, Zhenlei Li1, Jian Kang1
1State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China.
Materials (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a new method combining Random Forest and K-nearest neighbor with Gaussian Process Regression for accurate heat transfer coefficient prediction in hot strip rolling. This improves coiling temperature control, especially during frequent condition changes.
Area of Science:
- Materials Science and Engineering
- Mechanical Engineering
- Process Control
Background:
- Accurate heat transfer coefficient prediction is crucial for coiling temperature control in hot strip rolling.
- Conventional methods face challenges like boundary discontinuities and limited data for new conditions.
Purpose of the Study:
- To develop an improved framework for predicting the heat transfer coefficient in hot strip rolling.
- To enhance the adaptability and online compensation of controlled cooling temperature models.
Main Methods:
- A novel framework integrating Random Forest (RF), K-nearest neighbor (KNN), and Gaussian Process Regression (GPR).
- RF for key variable selection and similar-case retrieval guidance.
- KNN for local sample space construction based on historical data.
- GPR for modeling nonlinear relationships within the local sample space.
Main Results:
- The proposed RF-KNN-GPR method outperforms conventional KNN-based weighting techniques across various metrics and conditions.
- Industrial validation demonstrates coiling temperature control within ±20 °C for over 96.5% of coil length.
- Significant improvement in prediction accuracy and control performance.
Conclusions:
- The proposed framework effectively addresses limitations of conventional methods in heat transfer coefficient prediction.
- Enhanced adaptability and online compensation capabilities for controlled cooling temperature models.
- Provides a robust solution for precise temperature control in dynamic hot strip rolling environments.
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