نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشکده فنی و مهندسی، دانشگاه شهید باهنر کرمان، کرمان، ایران
2 دانشگاه شهید باهنر کرمان
3 دانشجوی دکتری استخراج، دانشکده مهندسی معدن و متالورژی دانشگاه یزد
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Roadheader machines have good efficiency and flexibility in mechanized tunneling and underground mining. The application of Roadheaders increases the speed of excavation in the tunnels, which dramatically reduces the time and cost of the project. Considering the importance of this issue, this study has been done to predict and optimize the penetration rate and excavation speed of Roadheaders using the particle swarm algorithm in Parvadeh No.1 mechanized coal mine. Therefore, in this study, the Roadheader characteristics have been investigated in related studies. All of these studies were divided into two parts: field observations and laboratory tests. In this research, tunnel number one considered as the case study which is divided into 30 parts/sections, and in each section, rock core/sample preparation, the number of joints along the tunnel, excavation time, and volume of the excavated rock mass under the Roadheader machine operation were measured. In the laboratory studies section, the rock core was analyzed by the uniaxial compression strength (UCS) test and finally a database was provided based on the obtained results. In the following, nonlinear and linear regression models were used to select the best model for estimating the instantaneous cutting rate (ICR) of the Roadheader machine which expresses the advancing rate of excavation. In these models, parameters including rock quality designation (RQD) of rock mass, tensile strength (σt), UCS, rock mass brittleness index (RMBI), pick consumption index (PCI), pick consumption factor (PCf), and specific energy (SE) were selected as input variables, and ICR was selected as output variables. By comparing the results, the linear regression model had the highest determination coefficient and performance index and the lowest root mean square error. Therefore, this model was selected as the most suitable model. In order to optimize ICR, the relationship obtained from the linear regression model was implemented in the particle swarm algorithm. The results showed that to obtain the optimal limit of ICR in the considered case study with a UCS of 1.68 MPa and a RQD of 33.09%, ICR is equal to 33.11 cubic meters per hour.
کلیدواژه‌ها [English]
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