Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses

Abstract

UB affects not only the safety of the workforce and machinery, but is also severely detrimental to the viability of the operation throughout all of the mining stages. It directly downgrades the ore and causes unnecessary mucking, haulage, crushing, hoisting, and milling activities, thus decreasing productivity. Despite these effects, current UB management Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses by H. Jang*, E. Topal*, and Y. Kawamura* SynopsisUnplanned dilution and ore loss directly influence not only the productivity of underground stopes, but also the profitability of the entire mining process. Stope dilution is a result of complex interactions between a number of factors, and cannot be predicted prior to mining. In this study, unplanned dilution and ore loss prediction models were established using multiple linear and nonlinear regression analysis (MLRA and MNRA), as well as an artificial neural network (ANN) method based on 1067 datasets with ten causative factors from three underground longhole stoping mines in Western Australia. Models were established for individual mines, as well as a general model that includes all of the mine data-sets. The correlation coefficient (R) was used to evaluate the methods, and the values for MLRA, MNRA, and ANN compared with the general model were 0.419, 0.438, and 0.719, respectively. Considering that the current unplanned dilution and ore loss prediction for the mines investigated yielded an R of 0.088, the ANN model results are noteworthy. The proposed ANN model can be used directly as a practical tool to predict unplanned dilution and ore loss in mines, which will not only enhance productivity, but will also be beneficial for stope planning and design.

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

“…In the drilling and blasting items, air overpressure prediction [50,51], backbreak prediction [52][53][54], flyrock prediction [55][56][57][58], flyrock prediction and air overpressure prediction [59], ground vibration prediction [60][61][62][63][64][65][66][67][68][69], and rock fragmentation estimation [70] were performed to investigate the changes in the barometric pressure, vibration, monument, and earthquake created while drilling and blasting. A damage prediction [71,72] study was conducted, wherein the damage caused to the surrounding residential areas by a blasting from an open-pit mine was predicted [71].…”

Transformation of the Australian mining industry and future prospects

“…According to years of on-site verifications, the AHS truck system is invigorating the mining industry by improving the productivity and safety of mines and expanding new business areas related to mine Figure 7. Configuration of UBO and its application to underground stope production blasting (Jang et al 2015a(Jang et al , 2015b(Jang et al , 2016. automation and digitalization.…”

Drawpoint loading optimization strategies in block caving: A case study of Palabora Mining Company

“…Generally, when a mining block nears depletion, it becomes more challenging to effectively extract the mineralization from the block, particularly in the case of high-grade deposits (Duffy et al 2015;Jang, Topal, and Kawamura, 2015). This leads to lower recoveries, with more fines and dilution.…”

“…This challenge requires selective mining, which is difficult to accomplish in block caving. Causes of dilution include the integration of lower-grade material in the block, as well as the ingress of external sources that further dilute the existing high-grade blocks (Jang, Topal, and Kawamura, 2015). Although waste sorting is practical in narrow vein reefs, it poses a challenge in massive mines, except with proper application of draw control strategies.…”