| dc.contributor | Glass, Hylke | |
| dc.contributor | UNIVERSITY OF EXETER | |
| dc.creator | Jeraldo Garrido, Cristian Jeraldo | |
| dc.date | 2019-01-28T19:54:23Z | |
| dc.date | 2022-08-16T18:22:35Z | |
| dc.date | 2019-01-28T19:54:23Z | |
| dc.date | 2022-08-16T18:22:35Z | |
| dc.date | 2017 | |
| dc.date.accessioned | 2023-08-22T22:39:55Z | |
| dc.date.available | 2023-08-22T22:39:55Z | |
| dc.identifier | 73161554 | |
| dc.identifier | https://hdl.handle.net/10533/232932 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8347899 | |
| dc.description | Drilling Specific Energy (DSE) is defined as the energy applied per a unit volume of material removed during rotational drilling. The advantages of using DSE technology include low cost, a large amount of data generated and robustness. Carmen de Andacollo (CDA) is an open pit situated in the north-central part of Chile. From 2012, the CDA three drill rigs have incorporated the system Thunderbird®, which allows the determination of DSE for every blast hole. This research assessed the precision of DSE data comparing different both drill rigs and operators. The research found that the geological attributes at CDA exercise a strong control over DSE values. This is used to define DSE domains through exploratory data analysis and additionally, demonstrates the coherence of the CDA geological model. The CDA grade control block model contains the DSE values estimated via inverse distance. Nonetheless, because blast hole is only available for grade control, DSE values for the 5-year-plan are currently estimated at CDA as an average by geological domain. In this research, three regression models were generated using data included in the grade control block model with the purpose of improving the predictability of DSE values for 5-year-plan. The data was divided into a train group to build the model and a test group for statistical validation. The results of the validation show that two of the models, the regression tree and the multiple linear regression, display a similar or worse performance than the current average by domain model. However, the results of the artificial neural networks model developed here exceed the average by domain model results. Therefore, the former is used to predict the DSE for the 5-year-plan block model. CDA operative difficulties include low grades and the proximity to Andacollo town. Using the artificial neural networks model might allow better mine planning, thus improving CDA operations. In operative terms, the model could permit a better determination of the power factor and tool consumption. Additionally, but no less important, it would allow reducing dust emissions (PM10) from blasting and, consequently, would improve the CDA relationship with the community and local and national authorities. This is key since Andacollo town was declared PM10 saturated zone in 2009. The results of the research presented here demonstrate the potential of using DSE in the light of the financial and environmental complexities of the current and future mining industry. | |
| dc.description | PFCHA-Becas | |
| dc.description | PFCHA-Becas | |
| dc.format | application/pdf | |
| dc.relation | instname: Conicyt | |
| dc.relation | reponame: Repositorio Digital RI2.0 | |
| dc.relation | info:eu-repo/grantAgreement//73161554 | |
| dc.relation | info:eu-repo/semantics/dataset/hdl.handle.net/10533/93488 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Chile | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.subject | Ciencias Naturales | |
| dc.subject | Ciencias de la Tierra y del Medio Ambiente | |
| dc.subject | Minería y Procesamiento de Minerales | |
| dc.title | Predictive Model for Drilling Specific Energy at Carmen de Andacollo, Chile | |
| dc.type | Tesis Magíster | |
| dc.type | info:eu-repo/semantics/masterThesis | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | Tesis | |
