Predicting the pyrite oxidation process within coal waste piles using multiple linear regression (MLR) and teaching-learning-based optimization (TLBO) algorithm
Paper
Paper/Presentation Title | Predicting the pyrite oxidation process within coal waste piles using multiple linear regression (MLR) and teaching-learning-based optimization (TLBO) algorithm |
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Presentation Type | Paper |
Authors | Entezam, Shima (Author), Shokri, Behshad Jodeiri (Author), Ardejani, Shaghayesh Doulati (Author), Mirzaghorbanali, Ali (Author), McDougall, Kevin (Author) and Aziz, Naj (Author) |
Editors | Aziz, Naj and Mirzaghorbanali, Ali |
Journal or Proceedings Title | Proceedings of the 2022 Resource Operators Conference |
Number of Pages | 8 |
Year | 2022 |
Place of Publication | Australia |
ISBN | 9781741283532 |
9781741283556 | |
Web Address (URL) of Paper | https://ro.uow.edu.au/coal/849/ |
Web Address (URL) of Conference Proceedings | https://ro.uow.edu.au/coal2022/ |
Conference/Event | 2022 Resource Operators Conference (ROC2022) |
Event Details | 2022 Resource Operators Conference (ROC2022) Event Date Feb 2022 Event Location Australia Event Venue University of Wollongong University of Southern Queensland |
Abstract | Coal mining often leads to significant environmental hazards and health concerns when sulfide minerals, particularly pyrite, are associated with coal waste. The oxidation of pyrite typically generates acid mine drainage, a significant problem. This paper presents two mathematical relationships using a teaching-learning-based optimization (TLBO) algorithm for predicting pyrite oxidation and pH changes within a coal waste pile from Alborz-Markazi in northern Iran. A dataset was built based on historical data to achieve this goal. Some influential parameters comprising the depths of the various samples, oxygen fraction, and bicarbonate concentrations were considered as input data, while the outputs were pyrite content and pH. Then, the best statistical relationships were suggested between input and output parameters employing curve and surface fitting methods. Afterward, two multiple linear regression (MLR) models were presented for predicting pyrite content and pH. Also, two relationships have been suggested for predicting the same outputs by applying the TLBO algorithm. Comparison of the results of the latter method with the results obtained using the statistical technique, including correlation coefficient and root mean squared error (RMSE), revealed that the TLBO could predict the outcomes better than the MLR. |
Keywords | pyrite oxidation process; coal waste piles |
ANZSRC Field of Research 2020 | 400502. Civil geotechnical engineering |
401902. Geomechanics and resources geotechnical engineering | |
401905. Mining engineering | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Civil Engineering and Surveying |
Charles University, Czech Republic | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q72xz/predicting-the-pyrite-oxidation-process-within-coal-waste-piles-using-multiple-linear-regression-mlr-and-teaching-learning-based-optimization-tlbo-algorithm
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