董倩倩, 胡帅杰, 黎敏, 于艳, 谷茂强. 基于改进自编码器的转炉炼钢工艺模式提取方法[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.08.01.002
引用本文: 董倩倩, 胡帅杰, 黎敏, 于艳, 谷茂强. 基于改进自编码器的转炉炼钢工艺模式提取方法[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.08.01.002
DONG Qianqian, HU Shuaijie, LI Min, YU Yan, GU Maoqiang. Process model extraction method of converter steelmaking based on improved autoencoder[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.08.01.002
Citation: DONG Qianqian, HU Shuaijie, LI Min, YU Yan, GU Maoqiang. Process model extraction method of converter steelmaking based on improved autoencoder[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.08.01.002

基于改进自编码器的转炉炼钢工艺模式提取方法

Process model extraction method of converter steelmaking based on improved autoencoder

  • 摘要: 转炉炼钢吹炼过程的控制主要包括供氧、造渣和底吹等工艺操作,吹炼过程控制的稳定性直接影响着终点钢水的质量. 传统的静态控制模型以物料平衡和热平衡为基础获得吹炼过程工艺操作模式,未考虑以原料为主的标量型数据和以工艺参数为主的时序型数据之间的强耦合关系,导致传统静态模型的可靠性不高,需要依靠人工经验来调整工艺参数. 为解决上述问题,提出一种基于改进自编码器的转炉炼钢工艺模式提取方法,该方法以自编码器为基础结构,使用全连接模块、长短期记忆网络模块、一维卷积模块和批量K-Means模块建立聚类模型,并联合聚类损失函数和重构损失函数实现模型的训练,获得原始高维数据在低维特征空间所对应的隐藏向量;在此基础上,利用隐藏向量完成聚类;最后,在属于不同聚类类别的数据中,寻找离各个聚类中心最近的样本,将最近样本的供氧、造渣和底吹工艺操作作为该类样本的工艺操作模式. 利用转炉炼钢生产过程实际数据验证了所提方法的有效性,使用标量型数据和提取的工艺模式数据预测终点碳温,终点碳的质量分数在±0.02%误差范围内的平均命中率为95.06%,终点温度在±20 ℃误差范围内的平均命中率为91.48%,在终点碳的质量分数±0.02%、温度±20 ℃误差范围内的平均双命中率为90.80%.

     

    Abstract: The blowing process in converter steelmaking at the blowing stage mainly includes oxygen supply, slag discharge, and bottom blowing. The stability of the blowing process directly affects the quality of the molten steel at the end. The traditional static control method derived from the blowing process model based on material and heat balances ignores the strong coupling relationship between raw materials and process parameters, resulting in its low reliability. Furthermore, data types for raw materials and process parameters are scalar and time series, respectively. Therefore, to extract the features of the abovementioned complex mixed data, this paper proposes a process model extraction method for converter steelmaking based on an improved autoencoder (IAE). The IAE method is based on an autoencoder that includes a fully connected long short-term memory network, one-dimensional convolution, and batch K-means modules. In the encoder, fully connected modules, long short-term memory networks, and one-dimensional convolutional modules extract nonlinear features of scalar data, long-term dependent features of time series, and local features of time series, respectively. Hence, the hidden vector is obtained by mapping the original high-dimensional data to a low-dimensional feature space using the encoder. To update the cluster center and calculate the clustering loss, the hidden vector is input to the batch K-Means module. Thus, the decoder reconstructs the hidden vector back to the original space to yield reconstructed data, which is then used to calculate the reconstruction loss. The IAE model is trained jointly with clustering and reconstruction losses. Finally, the cluster center of the original data and cluster category of each sample are obtained. The closer the sample is to the cluster center, the better the process parameters are controlled. Additionally, samples within the same cluster category are closer during the process operation. Therefore, the oxygen supply, slagging, and bottom-blowing processes of the closest samples are considered the process models for this type of sample. The effectiveness of the IAE model is evaluated using the endpoint quality index of real data from converter steelmaking. The average hit rate for the endpoint carbon mass fraction within the error range of ±0.02% is 95.06%, the average hit rate for the endpoint temperature within the error range of ±20 ℃ is 91.48%, and the average double hit rate within the error range of ±0.02% carbon mass fraction and ±20 ℃ temperature is 90.80%. Therefore, the results show that the process model extraction method improves the endpoint hit rate.

     

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