基于IGWO-DNN算法的铁水脱硫剂添加量预测模型

炼钢 ›› 2026, Vol. 42 ›› Issue (5): 32-42.

基于IGWO-DNN算法的铁水脱硫剂添加量预测模型

陈小艳¹,陈刚²,赵海杰³ʼ⁴,但斌斌³ʼ⁴,都李平³ʼ⁴

1.湖北开放大学人工智能学院,湖北武汉 430074;2.湖北开放大学机电工程学院,湖北武汉 430074;3.武汉科技大学冶金装备及其控制教育部重点实验室,湖北武汉 430081;4.武汉科技大学机械传动与制造工程湖北省重点实验室,湖北武汉 430081

出版日期:2026-09-28 发布日期:2026-09-28

Prediction model of hot metal desulfurizer addition based on IGWO-DNN algorithm

CHEN Xiaoyan¹, CHEN Gang², ZHAO Haijie³ʼ⁴, DAN Binbin³ʼ⁴, DU Liping³ʼ⁴

1.School of Artificial Intelligence, Hubei Open University, Wuhan 430074, China;2.School of Mechanical and Electrical Engineering, Hubei Open University, Wuhan 430074, China;3.Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China;4.Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China

Online:2026-09-28 Published:2026-09-28

摘要/Abstract

摘要： 为提高钢铁冶炼过程中KR工艺的脱硫效率,提出一种融合改进灰狼优化算法(IGWO)与深度神经网络(DNN)的脱硫剂添加量预测模型。首先,基于钢厂实际生产数据构建预测样本,采用随机森林(RF)评估特征重要性,筛选出与CaO脱硫剂目标变量显著相关的6项工艺参数;其次,通过引入佳点集策略与延迟非线性收敛因子机制,在标准灰狼优化算法(GWO)框架中平衡全局探索与局部开发阶段的能力;最后,应用IGWO对DNN的初始权值与阈值进行优化,从而提升网络收敛速度与预测精度。试验结果表明,所提IGWO-DNN模型在测试集上的决定系数(R2)达到0.93,平均绝对误差(MAE)和均方根误差(RMSE)分别为105.42和152.18。相较于传统模型,该方法在应对复杂工况与多维参数耦合时,可以有效捕捉脱硫过程中的离散和波动特性,显著提升对实际生产工况的适应性与预测精度。

关键词: 灰狼优化算法;深度神经网络;脱硫剂添加量;佳点集策略;收敛因子

Abstract: In order to improve the desulfurization efficiency of KR process in iron and steel smelting process, this paper proposes a prediction model of desulfurizer addition based on improved grey wolf optimization algorithm (IGWO) and deep neural network (DNN). Firstly, the prediction samples were constructed based on the actual production data of the steel plant, and the importance of features was evaluated by random forest (RF). Six process parameters significantly related to the target variable of CaO desulfurizer were selected. Secondly, a good point set strategy and a delayed nonlinear convergence factor are introduced into the standard grey wolf optimization algorithm (GWO) to balance the global exploration and local development capabilities of the algorithm. Finally, IGWO is used to optimize the initial weights and thresholds of DNN, so as to improve the convergence speed and prediction accuracy of the network. The experimental results show that the determination coefficient (R2) of the proposed IGWO-DNN model on the test set reaches 0.93, and the mean absolute error (MAE) and root mean square error (RMSE) are 105.42 and 152.18, respectively. Compared with the traditional model, this method can effectively capture the discrete and fluctuating characteristics in the desulfurization process when dealing with the coupling of complex working conditions and multi-dimensional parameters and significantly improve the adaptability and prediction accuracy of the actual production conditions.

Key words: grey wolf optimization algorithm; deep neural network; additive amount of desulfurizer; good point set strategy; convergence factor

. 基于IGWO-DNN算法的铁水脱硫剂添加量预测模型[J]. 炼钢, 2026, 42(5): 32-42.