| 引用本文: | 刘进进,周平,温亮.高炉铁水质量均方根误差概率加权集成学习建模[J].控制理论与应用,2020,37(5):987~998.[点击复制] |
| LIU Jin-jin,ZHOU Ping,WEN Liang.Root mean square error probability weighted integrated learning based modeling for molten iron quality in blast furnace ironmaking[J].Control Theory and Technology,2020,37(5):987~998.[点击复制] |
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| 高炉铁水质量均方根误差概率加权集成学习建模 |
| Root mean square error probability weighted integrated learning based modeling for molten iron quality in blast furnace ironmaking |
| 摘要点击 1552 全文点击 663 投稿时间：2019-02-16 修订日期：2019-10-11 |
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| DOI编号 10.7641/CTA.2019.90088 |
| 2020,37(5):987-998 |
| 中文关键词 高炉炼铁 多元铁水质量 灰色关联分析法 核密度估计方法 均方根误差概率加权 数据驱动建模 |
| 英文关键词 blast furnace ironmaking multivariate molten iron quality grey correlation analysis method kernel density estimation method root mean square error probability weighted data driven modeling |
| 基金项目 国家自然科学基金项目(61890934, 61473064, 61790572), 中央高校基本科研业务项目(N180802003)资助. |
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| 中文摘要 |
| 高炉炼铁是一个物理化学反应复杂、多相多场耦合的大滞后、非线性动态密闭系统, 其关键质量指标―铁
水温度、Si含量、P含量、S含量难以直接在线检测, 且离线化验过程滞后严重. 针对这一实际工程难题, 本文提出一
种基于均方根误差概率加权集成学习建模算法, 用于高炉多元铁水质量的预测建模. 首先, 为了提高建模数据质量,
对高炉原始数据进行时间粒度的统一、数据归一化等数据预处理操作; 为了提高建模效率和降低计算复杂度, 采用
灰色关联分析法提取与多元铁水质量指标关联度最强的关键变量作为建模输入变量. 然后, 为了提高建模的精度,
提出一种均方根误差概率加权集成随机权神经网络(RVFLNs)算法. 该算法采用具有快速建模速度的RVFLNs为子
模型, 使用核密度估计方法估计出子模型的均方根误差概率密度函数曲线, 进而求出每个子模型的均方根误差概率
并作为自身权重进行加权求和, 得到最终的均方根误差加权集成RVFLNs模型. 最后, 数值仿真验证和工业试验表
明: 所提算法能够根据实时输入数据的变化对多元铁水质量进行快速准确的预测. |
| 英文摘要 |
| Blast furnace ironmaking is a large hysteresis and nonlinear dynamic closed system with complex physical
and chemical reactions and multi-phase and multi-field coupling. Its key quality indicators―molten iron temperature,
Si content, P content and S content are difficult to be directly detected online. There is serious hysteresis in the offline
testing process. Aiming at solving this engineering problem, this paper proposes an integrated learning modeling algorithm
based on root-mean-square error probability weighting, which is for predictive modeling of multivariate molten iron in
blast furnace. Firstly, in order to improve the quality of the modeling data, data pre-processing such as time granularity
unification and data normalization; in order to improve the modeling efficiency and reduce the computational complexity,
the gray correlation analysis method is used to extract the key variables with the strongest correlation with the quality index
of multivariate molten iron as the input variables of the modeling. Secondly, in order to improve the accuracy of modeling,
a root mean square error probability weighted integrated RVFLNs algorithm is proposed. The algorithm uses random vector
functional-link networks as a sub-model. The kernel density estimation method is used to estimate the root mean square
error probability density curve of these sub-models. The root mean square error probability of each submodel is weighted
and summed as its own weight, and the final root mean square error weighted integrated RVFLNs model is obtained. Finally,
numerical simulation and industrial experiments show that the proposed algorithm can quickly and accurately predict the
quality of multivariate molten iron based on changes in real-time input data. |
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