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<p>Predicting pellet metallurgical performance is critical for optimizing industrial smelting processes, yet traditional methods face computational bottlenecks when handling large-scale material datasets. This study proposes an HPC-optimized hybrid model integrating XGBoost and multilayer perceptron (MLP) architectures. By implementing batch-optimized memory hierarchies and cache-aware data partitioning, we efficiently process a large amount of feedstock ratio data and metallurgical performance metrics from industrial production cycles. Experimental results demonstrate superior accuracy in predicting RDI, ΔT, RI, and RSI indices compared to single-model approaches. The proposed framework provides a scalable solution for real-time performance prediction in smart manufacturing systems, reducing computational overhead through dynamic load balancing across HPC nodes.</p>

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HPC-optimized hybrid XGBoost-MLP model for large-scale pellet metallurgical performance prediction

  • 期刊论文

  • 发表日期：

    2025年11月17日

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  • Yunjie Bai,
  • Xuezhi Wu,
  • Aimin Yang

摘要

Predicting pellet metallurgical performance is critical for optimizing industrial smelting processes, yet traditional methods face computational bottlenecks when handling large-scale material datasets. This study proposes an HPC-optimized hybrid model integrating XGBoost and multilayer perceptron (MLP) architectures. By implementing batch-optimized memory hierarchies and cache-aware data partitioning, we efficiently process a large amount of feedstock ratio data and metallurgical performance metrics from industrial production cycles. Experimental results demonstrate superior accuracy in predicting RDI, ΔT, RI, and RSI indices compared to single-model approaches. The proposed framework provides a scalable solution for real-time performance prediction in smart manufacturing systems, reducing computational overhead through dynamic load balancing across HPC nodes.