Decision support system of unplanned dilution and ore-loss in underground stoping operations using a neuro-fuzzy system
MetadataShow full item record
Unplanned dilution and ore-loss are the most critical challenges in underground stoping operations. These problems are the main cause behind a mine closure and directly influencing the productivity of the underground stope mining and the profitability of the entire operation. Despite being aware of the significance of unplanned dilution and ore-loss, prediction of these phenomena is still unexplained as they occur through complex mechanisms and causative factors. Current management practices primarily rely on similar stope reconciliation data and the intuition of expert mining engineers. In this study, an innovative unplanned dilution and ore-loss (uneven break: UB) management system is established using a neuro-fuzzy system. The aim of the proposed decision support system is to overcome the UB phenomenon in underground stope blasting which provides quantitative prediction of unplanned dilution and ore-loss with practical recommendations simultaneously. To achieve the method proposed, an uneven break (UB) prediction system was developed by an artificial neural network (ANN) considering 1076 datasets covering 10 major UB causative factors collected from three underground stoping mines in Western Australia. In succession, the UB consultation system was established via a fuzzy expert system (FES) in reference to surveyed results of fifteen underground-mining experts. The UB prediction and consultation system were combined as one concurrent neuro-fuzzy system that is named the 'uneven break optimiser'. Because the current UB prediction systems in investigated mines were highly unsatisfactory with correlation coefficient (R) of 0.088 and limited to only unplanned dilution, the performance of the proposed UB prediction system (R of 0.719) is a remarkable achievement. The uneven break optimiser can be directly employed to improve underground stoping production, and this tool will be beneficial not only for underground stope planning and design but also for production management.
Showing items related by title, author, creator and subject.
Jang, Hyong Doo ; Topal, Erkan; Kawamura, Y. (2016)© 2016 Published by Elsevier B.V. on behalf of China University of Mining & Technology. One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break (UB: unplanned ...
Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analysesJang, Hyong Doo; Topal, Erkan; Kawamura, Y. (2015)Unplanned 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 ...
A Case Study of Prediction and Analysis of Unplanned Dilution in an Underground Stoping Mine using Artificial Neural NetworkJang, Hyong Doo; Yang, H. (2014)Stoping method has been acknowledged as one of the typical metalliferous underground mining methods. Notwithstanding with the popularity of the method, the majority of stoping mines are suffering from excessive unplanned ...