Forthcoming and Online First Articles

International Journal of Mining and Mineral Engineering

International Journal of Mining and Mineral Engineering (IJMME)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Mining and Mineral Engineering (2 papers in press)

Regular Issues

  •   Free full-text access Open AccessActive Sensing in Froth Flotation
    ( Free Full-text Access ) CC-BY-NC-ND
    by Mikko Salo, Teijo Juntunen, Risto Ritala 
    Abstract: The idea of active sensing is to embed sensor systems with intelligence to require less human interaction. Accurate but limited main measurement systems are complemented with broadband auxiliary measurements that gather data and alert the main measurement to focus on certain area. This is similar with the way that our eyesight works in context of gathering data from our surroundings. The purpose of this study is to introduce and test a control architecture that could improve the operation of froth flotation process. An active sensing architecture based on Linear Quadratic Gaussian control is developed and tested in a simulation environment based on plant data for froth flotation with X-ray fluorescence and visible and near-infrared measurements. The architecture is tested in cases where external disturbances or auxiliary measurement model drifting go unnoticed by the main measurement. In both scenarios, the anomalies are successfully corrected by the active sensing architecture.
    Keywords: Active sensing; Sensor management; LQG; Froth flotation; Mahalanobis distance; POMDP.
    DOI: 10.1504/IJMME.2024.10063891
     
  • Predictive Model Using Machine Learning to Determine Fuel Consumption in CAT-777F Mining Equipment   Order a copy of this article
    by Marco Antonio Cotrina Teatino, Jairo Jhonatan Marquina Araujo, Jose Nestor Mamani Quispe, Solio Marino Arango Retamozo, Joe Alexis Gonzalez Vasquez, Johnny Henrry Ccatamayo Barrios, Eduardo Manuel Noriega Vidal 
    Abstract: The main objective of the research was to determine fuel consumption in CAT-777F equipment considering parameters such as speed, resistance and payload of the equipment, using a machine learning neural network algorithm. The methodology used was exploratory level, quantitative approach, applied type and non-experimental cross-sectional design. The results obtained when performing the EDA showed a high relationship of 84% between the payload of the equipment and the fuel consumption, indicating that keeping the payload of the truck constant, the fuel consumption will be lower. When validating the model, the result of the ANN metric indicates that the coefficient of determination (R2) obtained a value greater than or equal to 68%. Likewise, using the random forest regressor algorithm to determine the percentage of incidence in each of the parameters, it was obtained that the payload has a percentage of incidence of 87%. Finally, it is concluded that the use of neural networks can predict fuel consumption in CAT-777F equipment.
    Keywords: fuel consumption; neural networks; payload; resistance; truck speed.
    DOI: 10.1504/IJMME.2024.10064834
     

 

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