Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Author: search.filters.author.Lawal, Abiodun IsmailSubject: search.filters.subject.Gene Expression Programming

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  • Article
    Citation - WoS: 14
    Citation - Scopus: 18
    Investigating the Effects of Feeding Properties on Rock Breakage by Jaw Crusher Using Response Surface Method and Gene Expression Programming
    (Elsevier, 2021-05) Koken, Ekin; Lawal, Abiodun Ismail
    The present study investigates the effects of feeding properties on rock comminution by a laboratory-scale jaw crusher. For this purpose, detailed crushability tests were carried out on four different rock types to assess their degree of rock crushability (DRC). Various feeding sizes (9.5 - 19 mm) and quantities (500 - 1500 g) were adopted to reveal the choke feeding intensity during crushing actions. The efficiency of feeding properties was investigated through the response surface methodology (RSM). The RSM results demonstrated that the characterized feeding size (F-80, mm) dominates the general size reduction, whereas the feeding quantity (m(f), g) is associated with the crushing energy consumption and product flakiness. Therefore, the choke feeding intensity has a direct relation to the m(f) and F-80. In addition, novel gene expression programming (GEP) models were employed to generate empirical formulations to predict the DRC parameters. The established GEP models have a satisfactory estimation capability. Therefore, the DRC of the investigated rocks can be optimized through the proposed GEP models based on the coupling variables of m(f) and F-80. (C) 2021 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.
  • Article
    Citation - WoS: 10
    Citation - Scopus: 9
    A Comparative Study on Power Calculation Methods for Conveyor Belts in Mining Industry
    (Taylor & Francis Ltd, 2021-07-29) Koken, Ekin; Lawal, Abiodun Ismail; Onifade, Moshood; Ozarslan, Ahmet
    This paper covers different methods to evaluate the power consumption of several conveyor belt systems (CBSs) used in the Turkish Mining Industry (TMI). Based on each CBS's operational features, the power consumption (P-c, kW) was measured directly on motorised head-pulleys. The P-c was investigated through several conventional, statistical, and machine learning methods. This study shows that the DIN 22,101 could be the most convenient conventional method for the investigated CBSs. On the other hand, based on the nonlinear regression (NLR) and genetic expression programming (GEP) models, two new approaches were suggested for the design and optimisation of the P-c.