Estimation of deformation modulus of coals using artificial neural networks (ANN)

Ekin Köken

doi:10.14513/actatechjaur.00661

Authors

Ekin Köken Nanotechnology Engineering Department, Abdullah Gul University, 38100, Kayseri, Turkey

DOI:

https://doi.org/10.14513/actatechjaur.00661

Keywords:

Coal, Deformation properties, Young modulus, Artificial neural networks

Abstract

In this study, the Young modulus (E) of different coals was investigated using artificial neural networks (ANN). For this purpose, a comprehensive literature survey was carried out to compile such datasets available for the ANN analyses. As a result of the literature survey, a database composed of 81 datasets was formed. In the ANN analyses, uniaxial compressive strength (UCS) and dry density (ρ_d) of coals were adopted as input parameters. The ANN analysis results demonstrated that the predictive model established in this study could be reliably used to estimate the E for different coals. The correlation of determination value (R²) for the developed model is 0.85, which shows its relative success. In this context, this study can be declared a case study showing the applicability of ANN for the evaluation of E for a wide range of coal types. However, the number of samples and independent variables should be increased to obtain more comprehensive models in future studies.

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