马帅印, 高丽丽, 贺锦峰, 殷磊, 张茜, 胥军. 基于SSA-LSTM的转炉炼钢终点锰含量预测[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.10.18.004
引用本文: 马帅印, 高丽丽, 贺锦峰, 殷磊, 张茜, 胥军. 基于SSA-LSTM的转炉炼钢终点锰含量预测[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.10.18.004
MA Shuaiyin, GAO Lili, HE Jinfeng, YIN Lei, ZHANG Qian, XU Jun. Prediction of manganese content at the end point of converter steelmaking based on SSA-LSTM[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.10.18.004
Citation: MA Shuaiyin, GAO Lili, HE Jinfeng, YIN Lei, ZHANG Qian, XU Jun. Prediction of manganese content at the end point of converter steelmaking based on SSA-LSTM[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.10.18.004

基于SSA-LSTM的转炉炼钢终点锰含量预测

Prediction of manganese content at the end point of converter steelmaking based on SSA-LSTM

  • 摘要: 锰是钢铁中重要的合金元素,加入适量锰元素能提高钢铁的性能. 在转炉炼钢过程中锰元素的含量直接影响钢铁质量,锰元素加入过少会导致钢铁产品的硬度和强度不足,锰元素加入过量会导致钢铁过脆和生产成本增加. 因此,合适的锰元素添加量对提升钢铁质量与减少冶炼成本具有重要意义. 转炉炼钢过程中锰元素的添加量主要通过终点锰预测的结果来确定,然而,终点锰含量多少受到多个因素的综合影响,其中包括氧化反应进程和合金中其他元素的添加量,影响因素呈现非线性、高耦合的特征,导致终点锰预测难度大. 针对转炉炼钢终点锰预测过程中数据有含噪声、强耦合性等问题,提出了一个转炉炼钢终点锰含量预测研究架构,基于长短期记忆网络(Long Short-term memory, LSTM)预测模型,引入奇异谱分析(Singular spectral analysis, SSA)方法去除终点锰预测过程中非线性、非平稳序列的高频噪声,提出了一种基于SSA-LSTM的终点锰含量预测方法. 利用河北敬业钢铁有限公司转炉炼钢生产数据验证所提预测方法的平均绝对误差为1.19%,均方根误差为1.48%. 结果表明,该方法能够解决数据存在较多噪声及非线性等问题;与已有的时间序列预测方法相比,经过SSA处理的预测误差均有所减小,证明了该方法的有效性,为实际生产过程中精准加入合金提供了依据.

     

    Abstract: Manganese is an important alloying element in iron and steel. Adding the appropriate amount of manganese can enhance the properties of steel. The manganese content directly influences steel quality in the converter steelmaking process. Too little manganese results in insufficient hardness and strength of steel products, whereas excessive manganese leads to increased embrittlement and production costs. Therefore, determining the appropriate amount of manganese is crucial for improving steel quality and reducing smelting costs. The quantity of manganese added during converter steelmaking primarily depends on the predicted final manganese content. However, this content is influenced by various factors, such as the oxidation reaction process and the addition of other alloying elements. These factors exhibit nonlinear effects on the manganese content, and the factors are highly interconnected, making accurate prediction of manganese content at the end point challenging. In response to the challenges posed by noise and strong coupling in predicting manganese content at the end point of converter steelmaking, a research framework was developed to address these issues and facilitate accurate predictions. Key influencing factors in the smelting process were identified through Pearson correlation coefficient analysis and mechanistic analysis. Subsequently, the relationship between these influencing factors and end-point manganese content was modeled using the long short-term memory network (LSTM). To mitigate the effects of high-frequency noise in nonlinear and nonstationary sequences, singular spectral analysis (SSA) was employed during the prediction process. This led to the development of a method known as SSA-LSTM for predicting end-point manganese content. The effects of different test sets and the number of neurons on the prediction results were investigated using converter steelmaking production data from Hebei Jingye Iron & Steel Co., Ltd. The proposed method achieved minimal prediction error when the test set comprised 10% of the data and the number of neurons was set to 85. At these parameters, the mean absolute error of the prediction method for end-point manganese was 1.19%, with a root-mean-square error of 1.48%. These results demonstrate that the proposed method effectively addresses issues related to large noise and nonlinear data. Moreover, compared with existing time series prediction methods, the proposed method, particularly after SSA treatment, showed reduced prediction errors. This validates the effectiveness of the method and provides a basis for accurate alloy addition in actual production processes.

     

/

返回文章
返回