煤岩显微组分组图像自动识别系统与关键技术

Automatic image recognition system and key technologies of maceral group

  • 摘要: 煤化程度和显微组分组成决定着煤的物理化学性质和工艺性质。煤的镜质组反射率和显微组分的煤岩自动化测定,不仅可以减少传统人工测定产生的差异,而且速度快、效率高,使煤岩测定结果应用于煤炭分类、煤炭加工利用等领域成为可能。国内外在煤岩自动化测定方面开展过大量研究工作,实现了镜质组反射率自动测定。但是由于煤岩显微组成和煤化程度影响的复杂性,显微组分的自动识别和图像分析测定仍然面临诸多难题。针对以上问题:① 研制了煤岩显微图像自动采集硬件平台,具有显微镜自动聚焦、自动扫描和显微图像自动采集三大功能模块,建立了煤岩显微组分组图像自动识别工作流程;② 开发了显微图像去噪预处理技术,可实现黏结剂与壳质组有效分割、受下方煤颗粒反射影响变亮黏结剂等的有效剔除,形成了基于Prewitt算子的煤岩显微组分假边界图像剔除技术;③ 开发出基于K均值聚类的煤岩显微组分组图像自动分割和识别技术;④ 形成了烟煤的煤岩显微组分组图像自动识别系统。应用本文研发的技术,对我国不同变质阶段烟煤的代表性煤样进行煤岩显微图像自动采集和显微组分组自动识别测定,并将测定结果与国内资深煤岩专家人工鉴定的标准结果进行比对。结果表明,两种方法测定的45个样品镜质组、惰质组、壳质组的极差平均值分别仅有2.3%,2.3%,1.5%;按照国家标准GB/T18510—2001给出的准确度分析方法,获得3个显微组分组的统计量tc<tt。

     

    Abstract: Coalification degree and macerals determine the physical and chemical properties as well as coking proper- ties of coal. The automatic determination of vitrinite reflectance and macerals in coal can reduce the deviation from tra- ditional manual determination. Also,it is quick and efficient,which makes it possible for the determination results of coal petrology to be used in coal classification and the processing and utilization of coal. Numerous research has been done worldwide in the automatic determination of coal petrology. The automatic determination of vitrinite reflectance has been achieved. However,due to the complexity of maceral composition and the influence of coalification degree, there are still many difficulties in the automatic recognition of macerals and image analysis. In this paper,the research focuses on these problems. The following results have been obtained:① the microscope-based hardware platform for micro-image automatic collection is developed,which includes three functional modules:autofocus,automatic scanning and automatic collection of micro-image. The workflow of automatic image recognition of maceral group is established. ② Micro-image de-noising pretreatment techniques are developed for segmenting resin from liptinite group and the brightened resin affected by reflection of coal particles below. Based on Prewitt operator,a removal technique of false boundary of maceral is also developed. ③ A K-means clustering-based image automatic segmentation and recognition technology of maceral groups is developed. ④ Automatic image recognition system of maceral group of bituminous coal is developed. Using the image automatic recognition system developed in this research,the representative samples of bituminous coal from different ranks in China are automatically collected and their macerals are automatically recog- nized. The results are compared with the standard results of manual identification done by domestic authoritative ex- perts in coal petrology field. It is shown that the average deviation of the 45 samples in vitrinite,inertinite and liptinite group determined by the two methods are only 2. 3% ,2. 3% and 1. 5% respectively. According to the accuracy analy- sis method in national standard GB / T 18510—2001,the statistic tc is less than the tt of three maceral groups.

     

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