Xu Xinran, Zhou Xiaojun
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
School of Automation, Central South University, Changsha 410083, China.
Sensors (Basel). 2023 Nov 16;23(22):9217. doi: 10.3390/s23229217.
Sintering is a commonly used agglomeration process to prepare iron ore fines for blast furnace. The quality of sinter significantly impacts the blast furnace ironmaking process. In the vast majority of sintering plants, the judgment of sintering quality still relies on the intuitive observation of the cross section at sintering machine tail by operators, which is susceptible to the external environment and the experience of operators. In this paper, we propose a new sintering state recognition method using deep learning based feature selection and ensemble learning. First, features from the infrared thermal images of sinter cross section at the tail of the sinterer are extracted based on ResNeXt. Then, to eliminate the irrelevant, redundant and noisy features, an efficient feature selection method based on binary state transition algorithm (BSTA) is proposed to find the truly useful features. Subsequently, an ensemble learning (EL) method based on group decision making (GDM) is proposed to recognize the sintering states. Novel combination strategies considering the varying performance of the base learners are designed to further improve recognition accuracy. Industrial experiments conducted at a steel plant verify the effectiveness and superiority of the proposed method.
烧结是一种常用的团聚工艺，用于为高炉制备铁精矿粉。烧结矿的质量对高炉炼铁过程有显著影响。在绝大多数烧结厂中，烧结质量的判断仍依赖于操作人员对烧结机尾部横截面的直观观察，这容易受到外部环境和操作人员经验的影响。本文提出了一种基于深度学习的特征选择和集成学习的新型烧结状态识别方法。首先，基于ResNeXt从烧结机尾部烧结矿横截面的红外热图像中提取特征。然后，为了消除无关、冗余和噪声特征，提出了一种基于二元状态转移算法（BSTA）的高效特征选择方法，以找到真正有用的特征。随后，提出了一种基于群体决策（GDM）的集成学习（EL）方法来识别烧结状态。设计了考虑基础学习器不同性能的新型组合策略，以进一步提高识别精度。在一家钢铁厂进行的工业实验验证了该方法的有效性和优越性。
