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Zhangjie Dai, Ye Sun, Wei Liu, Shufeng Yang, and Jingshe Li, Smelting stage recognition for converter steelmaking based on the convolutional recurrent neural network, Int. J. Miner. Metall. Mater., 32(2025), No. 9, pp.2152-2163. https://doi.org/10.1007/s12613-024-3086-2
Zhangjie Dai, Ye Sun, Wei Liu, Shufeng Yang, and Jingshe Li, Smelting stage recognition for converter steelmaking based on the convolutional recurrent neural network, Int. J. Miner. Metall. Mater., 32(2025), No. 9, pp.2152-2163. https://doi.org/10.1007/s12613-024-3086-2
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基于卷积递归神经网络的转炉炼钢冶炼阶段识别

摘要: 转炉炼钢过程是钢铁冶金生产中的关键环节,其中炉口火焰特征能够间接反映炉内的冶炼阶段。如何有效识别和预测冶炼阶段,是工业生产中面临的重要挑战。传统的基于图像的方法通常依赖单帧静态火焰图像作为输入,识别精度较低,且难以充分提取冶炼阶段的动态变化特征。为解决这一问题,本文提出了一种创新性的火焰识别模型:首先对炉口火焰视频序列进行预处理,然后利用卷积循环神经网络(Convolutional Recurrent Neural Network, CRNN)提取时空特征并输出识别结果。此外,本文引入特征层可视化技术验证模型有效性,并通过结合贝叶斯优化算法进一步提升模型性能。研究结果表明,在卷积层中引入卷积块注意力机制(Convolutional Block Attention Module, CBAM)的ResNet18网络展现出优异的图像特征提取能力,其识别准确率达到90.70%,曲线下面积(Area Under Curve, AUC)为98.05%。进一步构建的贝叶斯优化(Bayesian optimization,BO)卷积循环神经网络(BO-CBAM-CRNN)在综合性能上实现了显著提升,准确率达到97.01%,AUC达到99.85%。同时,模型的平均识别时间、计算复杂度和参数量(平均识别时间:5.49 ms;浮点运算次数:18260.21 M;参数量:11.58 M)均显示出优越性能。通过在真实数据集上的大量重复实验验证,所提出的CRNN模型能够快速、准确地识别转炉冶炼阶段,为转炉炼钢终点控制提供了一种新的有效技术途径。

 

Smelting stage recognition for converter steelmaking based on the convolutional recurrent neural network

Abstract: The converter steelmaking process represents a pivotal aspect of steel metallurgical production, with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage. Effectively identifying and predicting the smelting stage poses a significant challenge within industrial production. Traditional image-based methodologies, which rely on a single static flame image as input, demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage. To address this issue, the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network (CRNN) to extract spatiotemporal features and derive recognition outputs. Additionally, we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm. The results indicate that the ResNet18 with convolutional block attention module (CBAM) in the convolutional layer demonstrates superior image feature extraction capabilities, achieving an accuracy of 90.70% and an area under the curve of 98.05%. The constructed Bayesian optimization-CRNN (BO-CRNN) model exhibits a significant improvement in comprehensive performance, with an accuracy of 97.01% and an area under the curve of 99.85%. Furthermore, statistics on the model’s average recognition time, computational complexity, and parameter quantity (Average recognition time: 5.49 ms, floating-point operations per second: 18260.21 M (1 M = 1 × 106), parameters: 11.58 M) demonstrate superior performance. Through extensive repeated experiments on real-world datasets, the proposed CRNN model is capable of rapidly and accurately identifying smelting stages, offering a novel approach for converter smelting endpoint control.

 

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