氧转炉炼钢智能渣检测系统的设计

V. Trofimov, Yana Neudakhina
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引用次数: 0

摘要

本文提出了氧气转炉炼钢智能炉渣检测系统的原理图,以及基于人工神经网络和信息特征评价的炉渣检测算法。基于多结构方法实现了非接触式炉渣自动识别。在开发的系统中,从热成像和热成像摄像机获得数字图像。数字图像以RGB颜色模型表示。传统的摄像机被用来探测火焰和烟雾,因为红外摄像机在存在这种干扰时可能会出现故障。本文提出的实时渣识别系统扩展了现有自动转炉控制系统的功能,提高了炼钢质量。利用碱性氧炼钢过程的现场视频帧对渣识别过程进行了计算建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
About Designing an Intelligent System for Slag Detection in Oxygen Converter Steelmaking
In this paper, proposed are the schematic of the intelligent system for slag detection in oxygen converter steelmaking and the algorithm for slag detection based on artificial neural networks and evaluation of informative features. Automatic non-contact slag recognition is carried out based on a multi-structured approach. In the developed system, digital images are obtained from thermal imaging and thermographic video cameras. Digital images are presented in the RGB color model. A conventional video camera is used to detect flames and smoke, as infrared cameras can be faulty in the presence of such interference. The proposed system for real-time slag recognition expands the functionality of the existing automated converter control system and improves the quality of steel. The computational modelling of the slag recognition process was carried out using in situ video frames of the basic oxygen steelmaking process.
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