Predicting the overall equipment efficiency of core drill rigs in mining using ANN and improving it using MCDM

• Autorzy: Balakrishnan Kirubakaran , Mani Ilangkumaran , Durairaj Sankaran

Identyfikatory

DOI

10.17531/ein/169581

• Języki publikacji: EN

Abstrakty

EN

In this manuscript, an attempt has been made to predict and improve the overall equipment effectiveness of core drill rigs. A combined Box– Jenkins and artificial neural network model was used to develop a three parameter model (drill pushing pressure, drill penetration rate & average pillar drill pit cycle time) for predicting effectiveness. the overall equipment efficiency of core drill rigs. The values of mean average percentage error, root mean square error, normalized root mean square error, men bias error, normalized mean biased error and coefficient of determination values were found to be 9.462%, 17.378%, 0.194, 0.96%, 0.0014 and 0.923. Empirical relationships were developed between the input and output parameters and its effectiveness were evaluated using analysis of variance. For attaining 74.9% effectiveness, the optimized values of pushing pressure, penetration rate and average pillar drill pit cycle time were predicted to be 101.7 bar, 0.94 m/min and 272 min, which was validated. Interactions, perturbations and sensitivity analysis were conducted.

Słowa kluczowe

EN

artificial neural network; response surface methodology; core drill; overall equipment efficiency; Box-Jenkins

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 3
• Strony: art. no. 169581
• Opis fizyczny: Bibliogr. 111 poz., rys., tab., wykr.

Twórcy

autor

Balakrishnan Kirubakaran

• AVS College of Technology, Department of Mechanical Engineering, Chinnagoundapuram, Salem, Tamil Nadu, India

• https://orcid.org/0000-0002-9218-0664

autor

Mani Ilangkumaran

• Department of Mechanical Engineering, Knowledge Institute of Technology, NH544, Kakapalayam, Salem, India

• https://orcid.org/0000-0001-7378-6313

autor

Durairaj Sankaran

• Department of Electrical Engineering, Annaporana Engineering College, Salem, India

• https://orcid.org/0000-0001-6455-3913

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).