选煤厂火车车厢智能清扫技术研究与实践
Research and practice of intelligent cleaning technology for railway carriage in coal preparation plant
王怀 1靳涛1
作者信息
- 1. 焦作煤业(集团)有限责任公司煤质中心,河南焦作 454001
- 折叠
摘要
针对煤炭装车环节火车车厢残留物人工清扫效率低、劳动强度大、安全风险高等问题,以焦作煤业(集团)有限责任公司赵固二矿选煤厂为试点,分析了车厢清理及残留物现状,进行了智能清扫机器人系统实施方案、工作流程等的研究,介绍了系统的基本结构、技术特点.现场实践表明,智能清扫机器人用于火车车厢残留物清扫,能够代替人工对车厢底部、侧面、端面、车厢顶部进行全断面清扫,每节车厢平均清扫时间节约12 min,减少了车厢余料,可减少用工13人,每年可以节约人工费、材料费及货延费300余万元,主要清扫技术指标完全能够满足现场装车需要.系统运行稳定,自动化、智能化水平高,减轻了员工劳动强度,提高了车厢清扫质量及效率,降低了安全风险,为企业带来了一定的经济效益、环保效益和社会效益.
Abstract
Addressing the issues of low efficiency,high labor intensity,and high safety risks associated with manual cleaning of residual materials in train carriages during the coal loading process,Jiaozuo Coal Industry(Group)Co.,Ltd.took the Zhaogu No.2 Coal Mine as a pilot project,the current situation of carriage cleaning and residual materials was analyzed,the research on the implementation plan and workflow of intelligent cleaning robot system was conducted,and the basic structure and technical characteristics of the system were introduced.On-site practice shows that intelligent cleaning robot is used for cleaning residues in train carriages,which can replace man-ual cleaning of the bottom,sides,end faces,and top of train carriages for full section cleaning of residues.The average cleaning time for each carriage is saved by 12 minutes,reducing the amount of residual materials in the carriage and reducing labor by 13 people.This can save more than 3 million yuan in labor costs,material costs,and freight delay costs annually.The main cleaning technical indicators can fully meet the needs of on-site loading.The system operation is stable,the level of automation and intelligence is high,reducing em-ployee labor intensity,improving the quality and efficiency of carriage cleaning,reducing safety risks,and bringing certain economic,en-vironmental,and social benefits to the enterprise.
关键词
选煤厂/火车车厢/残留物/智能清扫机器人/智能化Key words
coal preparation plant/train carriage/residue/intelligent cleaning robot/intelligentization引用本文复制引用
基金项目
河南省科技攻关计划项目(232102321134)
出版年
2024
NETL
NSTL
万方数据