Orthogonal Experimental Study on the Construction of a Similar Material Proportional Model for Simulated Coal Seam Sampling

Abstract

The development of similar materials is crucial for conducting simulated coal seam sampling experiments. These materials must comprehensively consider the similarity of mechanical properties with raw coal. To ensure a high degree of similarity between the simulated coal seam similar material and raw coal, cement and coal powder were selected as the main influencing factors. This study employed sensitivity analysis and analysis of variance methods to investigate the impact of various factors on the compressive strength, elastic modulus, Poisson’s ratio, and density of comparable materials. The influence of moisture content on the compressive strength and elastic modulus of these materials was also analyzed, as well as the effect of cement and coal powder on moisture content. The results showed that cement was the main controlling factor for the mechanical properties of similar materials. Moreover, the variation of tensile strength and elastic modulus of similar materials in response to the moisture content can be divided into three distinct stages. Based on the influence law, the ratio formula of coal powder and cement for the strength and moisture content of similar materials was obtained, and a proportional model for simulated coal seam sampling similar materials was constructed. The approximate ranges of various parameters that can be achieved by this model are as follows: compressive strength of 1~11.4 MPa, elastic modulus of 0.27~3.62 GPa, Poisson’s ratio of 0.229~0.357, and density of 1.044~1.341 g·cm−3. The error in mechanical parameters for this model has been verified to be within 20%. Finally, similar materials were created for simulated coal seam sampling by employing an appropriate ratio. A self-developed drilling test platform was utilized to successfully conduct an experiment, further demonstrating the reliability of the proportional model.

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“…An orthogonal design was used for conjoint analysis and was conducted using SPSS25 [48], generating 29 combinations. This design approach was chosen to maintain a manageable number of combinations for participant evaluation, presenting the optimum combination to determine the objective of the study.…”