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MODEL STRUCTURE AND PARAMETER IDENTIFICATION FOR CUTTING HORIZON PREDICTION OF SHEARER LOADERS

Mirjam Holm, Christian Bøhn

First published: 2023-10-01https://doi.org/10.5593/sgem2023/1.1/s03.44View metrics

Abstract

To reach autonomous control with good performance in shearer operated longwall mines, control algorithms have to be adapted to the system behaviour. Thus, one of the challenging tasks in this area is the identification of the behaviour, which is declared as complex and specific to each mining area. Therefore, it is necessary to determine a suitable model that reproduces the cut behaviour of a shearer loader and can be adapted to a real mining system. This paper shows and analyses possible model structures to predict the cutting path of a shearer loader in the direction of face advance, combined with the least squares method for parameter identification. Single-step, as well as multi-step prediction are considered. Especially results of multi-step prediction of the cutting path, compared to the reference path shows whether the model structure and the identified parameters are valid or not. Data source for the parameter identification and cutting path reference is a complex and iteratively working simulation tool developed in cooperation with a German mining company for the training of operators. Finally, a suitable, analytical model for parameter identification and prediction of the cutting horizon is pointed out.

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Publication details

Title
MODEL STRUCTURE AND PARAMETER IDENTIFICATION FOR CUTTING HORIZON PREDICTION OF SHEARER LOADERS
Authors
Mirjam Holm, Christian Bøhn
Proceedings
SGEM International Multidisciplinary Scientific GeoConference- EXPO Proceedings; 23rd International Multidisciplinary Scientific GeoConference Proceedings SGEM2023, Science and Technologies in Geology, Exploration And Mining, Vol 23, Issue 1.1
Publisher
STEF92 Technology
Year
2023
Pages
365-372
SWS Citekey
Holm20233365372
ISSN
1314-2704
ISBN
978-619-7603-56-9
Language
en
Publication type
Conference Paper
Proceedings contents
Open official contents
Keywords
References6
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  4. Li, J., Y. Liu, J. Xie, X. Wang, X. Ge. Cutting Path Planning Technology of Shearer Based on Virtual Reality. Applied Sciences 10, no. 3, 2020, p. 771 DOI: 10.3390/app10030771

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