Prediction of Roadheader Bit Consumption Rate in Coal Mines Using Artificial Neural Networks
- Adel Asadi (Islamic Azad University) | Amin Abbasi (Islamic Azad University) | Amirhossein Bagheri (University of Tehran)
- Document ID
- International Society for Rock Mechanics and Rock Engineering
- ISRM 3rd Nordic Rock Mechanics Symposium - NRMS 2017, 11-12 October, Helsinki, Finland
- Publication Date
- Document Type
- Conference Paper
- 2017. Finnish Association of Civil Engineers RIL. Permission to distribute - International Society for Rock Mechanics and Rock Engineering
- Roadheader Performance Prediction, Bit Consumption Rate, Artificial Neural Networks
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- 52 since 2007
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Undoubtedly, Roadheaders are one of the most versatile excavation machine types operated in soft and medium strength rock formations’ tunneling and mining. An essential aspect of a successful roadheader application is definitely the performance prediction which is basically concerned with machine selection, production rate and also bit consumption. Evolving a new roadheaders’ performance prediction model in various operational conditions and also different material is the primary intention of this research. Investigation on previous works revealed that three main features have great influences on the bit wear of a roadheader. Brittleness which can be utilized as a cuttability factor in mechanical excavation perspective is actually one of some parameters which is absolutely in relation with breakage properties. In addition to the rock brittleness, rock quality designation (RQD) and instantaneous cutting rate are employed as input parameters for the prediction of pick (bit) consumption rate (PCR). For the purpose of this paper, using previously published field datasets, a new prediction model using the application of artificial neural networks as an artificial intelligence technique is developed, trained and tested to estimate PCR based on data of brittleness, RQD and instantaneous cutter rate. Results demonstrated that PCR is highly correlated to the input parameters, and the ANN model could produce acceptable predictions.
In recent years, mining business has been under the influences of global trends, environmental limitations, and variant market requirements to be more and more productive and profitable. Utilizing mechanical miners like roadheaders, continuous miners, impact hammers and tunnel boring machines for ore extraction and excavation of development drivages, increases profitability. The mentioned miners result in continuous operations and consequently, the mechanization of mines with mechanical miners is presumed to make mining projects more productive, more competitive, and less costly. As a result, ordinary drill and blast technique could be avoided. Roadheaders which are applicable in tunnelling, mine development, and mine production of rock types of soft to medium strength, are very adaptable excavation facilities. The efficiency of roadheader application is rudimentary related to machine selection, production rate and bit consumption (Ebrahimabadi et al., 2011).
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