煤炭机器人智能制样系统性能分析与试验研究
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摘要
煤炭机器人智能制样系统是以机器人为作业载体,将传统制样设备进行智能化和信息化升级,可实现自感知、自判断、自适应、自执行、自学习的完全无人值守的制样系统。介绍了煤炭机器人智能制样系统的结构组成、工作流程和有关应用性能,对入料和留样质量、出料粒度及过筛率、精密度、灰分偏倚等指标进行了试验,并对发热量指标进行了偏倚测定。研究结果表明,该系统的精密度和偏倚都达到了《煤样的制备方法》(GB 474-2008)和《煤炭机械化采样》(GB/T 19494.3-2004)标准规定的要求,并且与人工制样结果无显著性差异。该系统的成功推广应用可降低人工制样的劳动强度,可以有效地规避人为因素的影响,为智慧矿山建设奠定良好基础。
Taking the coal robot as operation carrier, coal intelligent robotic sample preparation system was a complete unattended sample preparation system which upgraded the traditional sample preparation equipment in intelle tualization and informatization and realized self-sensing, self-judgment, self-adaption, self-execution and self-learning. The structural composition, operation flow and relevant application performation of the coal intelligent robotic sample preparation system were introduced, and quality of feeding material and reserved sample, discharging granularity, sieving rate, precision degree, ash content bias and other indicators were tested, and bias determination of calorific value indicator was conducted. The research results showed that the system's precision and bias met the requirements of GB474-2008 and GB/T19494.3-2004, and the results were in line with the manual coal sample preparation. The successful application of the coal intelligent robotic sample preparation system reduced the labor intensity of manual coal sample preparation, effectively avoided the influence of human disturbance factors during the sample preparation process, and laid a good foundation for intelligent mine construction.
Taking the coal robot as operation carrier, coal intelligent robotic sample preparation system was a complete unattended sample preparation system which upgraded the traditional sample preparation equipment in intelle tualization and informatization and realized self-sensing, self-judgment, self-adaption, self-execution and self-learning. The structural composition, operation flow and relevant application performation of the coal intelligent robotic sample preparation system were introduced, and quality of feeding material and reserved sample, discharging granularity, sieving rate, precision degree, ash content bias and other indicators were tested, and bias determination of calorific value indicator was conducted. The research results showed that the system's precision and bias met the requirements of GB474-2008 and GB/T19494.3-2004, and the results were in line with the manual coal sample preparation. The successful application of the coal intelligent robotic sample preparation system reduced the labor intensity of manual coal sample preparation, effectively avoided the influence of human disturbance factors during the sample preparation process, and laid a good foundation for intelligent mine construction.
引文
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[3] 朱伟杰.煤炭采制样存在的问题与解决对策[J].云南化工,2017(6)
[4] 王晓强,袁晓鹰.机器人集成技术在煤炭制样中的应用[J].煤质技术,2016(4)
[5] 赵小锋,王兴无.全自动无人值守煤炭机械化智能制样系统在电力行业应用现状及分析[J].江西电力,2016(6)
[6] 赵忠辉,王阳阳.机械化采制样现状与发展趋势分析[J].煤炭工程,2018(7)
[7] 王晓强,魏加栋,马加存.工业机器人在焦炭制样系统中的应用[J].冶金自动化,2017(1)
[8] 胡言箭,张永森,詹田友等.制样设备的精密度测定和偏倚试验[J].广东电力,2013(3)
[9] 窦怀武,李建伟,陈云飞.浅析全自动制样系统较传统制样在煤炭制备过程中的优势[J].煤质技术,2018(1)
[10] 胡斌.燃料智能采制化系统方案探讨与实施[J].煤质技术,2018(4)
[11] 谭建军,张冬练.全自动制样系统在煤炭制样中的应用浅探[J].煤质技术,2014(S1)
[12] GB 474-2008,煤样的制备方法[S].2008
[13] GB/T 19494.3-2004,煤炭机械化采样第3部分:精密度测定和偏倚试验[S].2004
[14] MT/T 936-2005锤式破碎缩分联合制样机通用技术条件[S].2005
[15] 孙刚,杨华玉,武增礼等.煤炭制样偏倚试验有关问题的探讨[J].煤质技术,2012(5)