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Data-driven modeling based on volterra series for multidimensional blast furnace system.
Chuanhou Gao1, Ling Jian, Xueyi Liu
1Department of Mathematics, Zhejiang University, Hangzhou 310027, China. gaochou@zju.edu.cn
IEEE Transactions on Neural Networks
|December 1, 2011
Summary
This study develops data-driven Volterra series models to predict silicon content in blast furnaces. The linear Volterra filter effectively predicts silicon levels, showing potential for blast furnace automation.
Area of Science:
- Industrial process modeling
- Chemical engineering
- Data-driven systems
Background:
- Blast furnace systems are highly complex industrial processes.
- Challenges remain in silicon prediction and automation.
- Existing models may not fully capture system dynamics.
Purpose of the Study:
- To develop data-driven models for blast furnace silicon prediction.
- To apply Volterra series for modeling complex industrial systems.
- To enhance blast furnace automation through accurate prediction.
Main Methods:
- Utilized Volterra series for data-driven modeling.
- Designed three low-order Volterra filters.
- Implemented a sliding window technique for timely kernel updates.
Main Results:
- The linear Volterra predictor accurately described silicon sequence evolution.
- Achieved a high hit rate and low root mean square error.
- Demonstrated satisfactory confidence in future prediction reliability.
Conclusions:
- The sliding-window linear Volterra filter shows significant potential for blast furnace systems.
- Low computational complexity and high accuracy make it suitable for automation.
- Identified areas for future research in Volterra model construction.
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