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基于持续学习的高炉铁水质量指标多步预测方法

Multi-step prediction method for blast furnace hot metal quality indicators based on continuous learning

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高炉炼铁工艺在钢铁工业中具有重要意义.然而,由于高炉炼铁工艺流程复杂以及存在高温、高压、复杂物理化学反应等问题,建立有效的过程监控模型仍然是一个重大挑战.针对高炉炼铁过程中的铁水关键质量指标多步实时预测需求,参考仿人脑海马体与新皮质的持续学习(continuous learning,CL)理论,搭建基于多头自注意力机制、一维残差神经网络(residual neural network,ResNet)、长短期记忆网络(long short term memory,LSTM)与多层感知机(multi-layer perceptron,MLP)的持续学习时序数据预测建模框架,实现高炉炼铁过程铁水关键质量指标多步实时预测任务.实验结果表明,本文所提出的基于持续学习(CL-based)的建模方法优于传统的深度学习模型,取得了较高的预测精度,且随着预测时间步长的增加,所提出的模型呈现出较强的鲁棒性.
The blast furnace ironmaking process is of great significance in the iron and steel industry.However,due to the complexity of the blast furnace ironmaking process and the existence of high tem-perature,high pressure,and complex physical and chemical reactions,it is still a major challenge to establish an effective process monitoring model.Aiming at the requirement of multi-step real-time prediction of hot metal key quality indicators in the process of blast furnace ironmaking,this paper re-fers to the theory of continuous learning(CL)that simulates the human brain's hippocampus and neocortex,and constructs a CL-based time series data prediction modeling framework based on multi-head self-attention mechanism,one-dimensional residual neural network(ResNet),long short term memory network(LSTM)and multi-layer perceptron(MLP)in order to realize the multi-step real-time prediction task of hot metal key quality indicators of the blast furnace ironmaking process.The experimental results show that the proposed CL-based method is superior to the traditional deep learn-ing models,achieves higher prediction accuracy,and with the increase of prediction time step,the proposed CL-based model shows strong robustness.

blast furnace ironmakinghot metal quality indicator predictioncontinuous learningmulti-task predictionmulti-step prediction

李浩东、何柏村、张新民、宋执环

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浙江大学工业控制技术国家重点实验室,浙江 杭州 310027

浙江大学控制科学与工程学院,浙江 杭州 310027

湖州工业控制技术研究院,浙江湖州 313000

高炉炼铁 铁水质量指标预测 持续学习 多任务预测 多步预测

国家自然科学基金重点项目国家自然科学基金青年科学基金项目科技创新2030—"新一代人工智能"重大项目

61933013620033012022ZD0120003

2024

10.3969/j.issn.1000-7059.2024.05.005

冶金自动化
冶金自动化研究设计院

冶金自动化

影响因子：0.685
ISSN：1000-7059
年,卷(期)：2024.48(5)
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