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A prediction model for specific energy required by point attack picks based on a hybrid evolutionary machine learning technique

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Abstract

Full-scale linear rock cutting experiment is the most effective method for determining specific energy. Collecting samples for full-scale experiment is usually time consuming, costly, and sometimes impossible. In order to deal with this disadvantage, many researchers have developed models for specific energy estimation. However, those models fail to include all of the cutting parameters that significantly contribute to the value of specific energy (i.e., depth of penetration, cut spacing, ratio of cut spacing to depth of cut, tip angle, attack angle, and skew angle). Investigating the relationships between intact rock properties, cutting parameters, and specific energy required by point attack picks in relieved cutting mode is the objective of this study. A relatively large database including records of uniaxial compressive strength, Brazilian tensile strength, the aforementioned cutting parameters, and specific energy was established. An evolutionary machine learning technique, which is a combination of gene expression programming and particle swarm optimization, was used to fit a non-linear function over the data. The values of coefficient of determination and mean squared error associated with the non-linear model were improved by almost 30% and 60% compared to those associated with the linear model, respectively. The non-linear model included all of the significant cutting parameters.

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Acknowledgements

The Institute of Engineering Research at Seoul National University (SNU) is acknowledged for providing research facilities.

Funding

This study was funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport in South Korea (Project No.: 20CTAP-C151926-02), and a grant from the Human Resources Development program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), funded by the Ministry of Trade, Industry, and Energy of the Korea Government (Project No.: 20204010600250).

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Authors and Affiliations

  1. Department of Energy Systems Engineering, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul, 08826, Republic of Korea

    Shahabedin Hojjati & Seokwon Jeon

  2. Research Institute of Energy and Resources, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul, 08826, Republic of Korea

    Seokwon Jeon

  3. Department of Energy Resources Engineering, Pukyong National University, 45 Yongso-Ro, Nam-Gu, Busan, 48513, Republic of Korea

    Hoyoung Jeong

  4. Daegyeong Regional Division, Korea Institute of Industrial Technology, Daegu, 42990, Republic of Korea

    Jung-Woo Cho

  5. Department of Mining Engineering, Istanbul Technical University, MaslakIstanbul, 34469, Turkey

    Deniz Tumac

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  1. Shahabedin Hojjati
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Correspondence to Seokwon Jeon.

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Responsible Editor: Zeynal Abiddin Ergule

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Hojjati, S., Jeong, H., Cho, JW. et al. A prediction model for specific energy required by point attack picks based on a hybrid evolutionary machine learning technique. Arab J Geosci 15, 993 (2022). https://doi.org/10.1007/s12517-022-10225-z

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