Prediction and Optimization of Roadheader Performance based on Characteristics and Geo-Mechanical Parameters of Rock (Case Study: TABAS Parvedeh coal mine No. 1)

Document Type : Research Article

Authors

1 Dept. of Mining Engineering, Zarand Higher Education Complex, Shahid Bahonar University of Kerman, Kerman, Iran

2 Dept. of Mining Engineering, Faculty of Engineering and Technology, Shahid Bahonar University of Kerman, Kerman, Iran

3 Dept. of Mining and Metallurgy Engineering, Yazd University, Yazd, Iran

10.22034/anm.2025.21296.1628

Abstract

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 aims 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 characteristics of Roadheaders 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 is 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 the output variable. 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.

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References

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History

  • Receive Date: 23 February 2024
  • Revise Date: 09 July 2025
  • Accept Date: 06 September 2025

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