Toward the Optimization of Mining Operations Using an Automatic Unmineable Inclusions Detection System for Bucket Wheel Excavator Collision Prevention: A Synthetic Study

George Kritikakis (), Michael Galetakis (), Antonios Vafidis, George Apostolopoulos, Theodore Michalakopoulos, Miltiades Triantafyllou, Christos Roumpos, Francis Pavloudakis, Basileios Deligiorgis, Nikos Economou and Nikos Andronikidis
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George Kritikakis: School of Mineral Resources Engineering, Technical University of Crete Campus, 73100 Chania, Greece
Michael Galetakis: School of Mineral Resources Engineering, Technical University of Crete Campus, 73100 Chania, Greece
Antonios Vafidis: School of Mineral Resources Engineering, Technical University of Crete Campus, 73100 Chania, Greece
George Apostolopoulos: School of Mining and Metallurgical Engineering, National Technical University of Athens, Iroon Polytechniou 9 str., Zografou Campus, 15773 Athens, Greece
Theodore Michalakopoulos: School of Mining and Metallurgical Engineering, National Technical University of Athens, Iroon Polytechniou 9 str., Zografou Campus, 15773 Athens, Greece
Miltiades Triantafyllou: Mining Engineering and Closure Planning Department, Public Power Corporation, Chalkokondili 29 str., 10432 Athens, Greece
Christos Roumpos: Mining Engineering and Closure Planning Department, Public Power Corporation, Chalkokondili 29 str., 10432 Athens, Greece
Francis Pavloudakis: Department of Mineral Resources Engineering, School of Engineering, University of Western Macedonia, 50100 Kozani, Greece
Basileios Deligiorgis: School of Mineral Resources Engineering, Technical University of Crete Campus, 73100 Chania, Greece
Nikos Economou: School of Mineral Resources Engineering, Technical University of Crete Campus, 73100 Chania, Greece
Nikos Andronikidis: School of Mineral Resources Engineering, Technical University of Crete Campus, 73100 Chania, Greece

Sustainability, 2023, vol. 15, issue 17, 1-20

Abstract: This work introduces a methodology for the automatic unmineable inclusions detection and Bucket Wheel Excavator (BWE) collision prevention, using electromagnetic (EM) inspection and a fuzzy inference system. EM data are collected continuously ahead from the bucket wheel of a BWE and subjected to processing. Two distinct methodologies for data processing were developed and integrated into the MATLAB programming environment. The first approach, named “Simple Mode”, utilizes statistical process control to generate real-time alerts in the event of a potential collision involving the excavator’s bucket and hard rock inclusions. The advanced processing flow (“Advanced Mode”) requires accurate instrument positioning and data from successive EM scans. It incorporates techniques of local resistivity maxima detection (Position Prominence Index) as well as Neural Network-based Pattern Recognition (NNPR). A decision support process based on a Fuzzy Inference System (FIS) has been developed to assist BWE operators in avoiding collision when digging hard rock inclusions. The proposed methodology was extensively tested using synthetic EM data. Limited real data, acquired with a CMD2 (GF Instruments) EM instrument equipped with GPS, were used to control its efficiency. Increased accuracy in the automatic detection of unmineable inclusions was observed using the Advanced Mode. On the other hand, the Simple Mode processing technique offers the advantage of being independent of instrument positioning as well as it provides real-time inspection of the excavated mine slope. This work introduces a methodology for hard rock inclusion detection and can contribute to the optimization of mine operations by improving resource efficiency, safety, cost savings, and environmental sustainability.

Keywords: bucket wheel excavator; unmineable inclusions detection; electromagnetic inspection; fuzzy inference system; collision prevention (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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