Zhu Hongyu, He Bocun, Zhang Xinmin
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou310027, China.
ACS Omega. 2022 Nov 4;7(45):41296-41303. doi: 10.1021/acsomega.2c05029. eCollection 2022 Nov 15.
The blast furnace is an energy-intensive and extremely complex reactor in the ironmaking process. To reduce energy consumption, improve product quality, and ensure the stability of blast furnace operation, it is very important to predict the quality indicators of molten iron accurately and in real time. However, most of the existing product quality prediction models, such as the stacked autoencoder (SAE) model, use a single-channel stack structure. For such models, when the working conditions of the blast furnace ironmaking process change, a large prediction error will occur. To solve this issue, this paper develops a novel deep learning model, called the multi-gate mixture-of-experts stacked autoencoder (MMoE-SAE), for predicting the quality variable in the blast furnace ironmaking processes. The proposed MMoE-SAE model is constructed based on a multi-gate hybrid expert structure, in which a series of SAE networks are selected as experts. The MMoE-SAE model inherits the advantages of MMoE and SAE, which can not only extract the deep features of the data but also have better adaptability to the changes of working conditions in the blast furnace ironmaking process. To verify the effectiveness and practicability of the proposed MMoE-SAE model, it was applied to predict the silicon content of molten iron in the blast furnace ironmaking process. The experimental results demonstrate that the proposed MMoE-SAE model outperforms other prediction models in prediction accuracy.
高炉是炼铁过程中能源密集型且极其复杂的反应器。为降低能耗、提高产品质量并确保高炉运行的稳定性，准确实时地预测铁水质量指标非常重要。然而，大多数现有的产品质量预测模型，如堆叠自编码器（SAE）模型，采用单通道堆叠结构。对于此类模型，当高炉炼铁过程的工况发生变化时，会产生较大的预测误差。为解决这一问题，本文开发了一种新颖的深度学习模型，称为多门混合专家堆叠自编码器（MMoE-SAE），用于预测高炉炼铁过程中的质量变量。所提出的MMoE-SAE模型基于多门混合专家结构构建，其中一系列SAE网络被选作专家。MMoE-SAE模型继承了MMoE和SAE的优点，不仅能够提取数据的深层特征，而且对高炉炼铁过程中工况的变化具有更好的适应性。为验证所提出的MMoE-SAE模型的有效性和实用性，将其应用于预测高炉炼铁过程中铁水的硅含量。实验结果表明，所提出的MMoE-SAE模型在预测精度上优于其他预测模型。
