TY - GEN
T1 - Predictive Model of Rock Fragmentation Using the Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) to Estimate Fragmentation Size in Open Pit Mining
AU - Vergara, Betty
AU - Torres, Maria
AU - Aramburu, Vidal
AU - Raymundo, Carlos
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.
PY - 2021
Y1 - 2021
N2 - The objective of this research is to generate a predictive model to estimate rock fragmentation size using the Neuro-Diffuse Inference System (ANFIS) in combination with Particle Swarm Optimization (PSO). To build the predictive model, 92 blasting events were investigated and the rock fragmentation values were chosen, as well as three effective parameters on rock fragmentation, that is, burden, burden / spacing ratio, overdrilling and power factor. Likewise, they were separated into training and test data (70%–30%) for the generation of the fuzzy rules of the model. Based on statistical functions, correlation coefficient (R2) and mean square error (RMSE), it was found that the ANFIS-PSO model (with R2 = 0.85 and RMSE = 0.78) can be used as a reliable and acceptable model in the field. prediction of rock fragmentation.
AB - The objective of this research is to generate a predictive model to estimate rock fragmentation size using the Neuro-Diffuse Inference System (ANFIS) in combination with Particle Swarm Optimization (PSO). To build the predictive model, 92 blasting events were investigated and the rock fragmentation values were chosen, as well as three effective parameters on rock fragmentation, that is, burden, burden / spacing ratio, overdrilling and power factor. Likewise, they were separated into training and test data (70%–30%) for the generation of the fuzzy rules of the model. Based on statistical functions, correlation coefficient (R2) and mean square error (RMSE), it was found that the ANFIS-PSO model (with R2 = 0.85 and RMSE = 0.78) can be used as a reliable and acceptable model in the field. prediction of rock fragmentation.
KW - ANFIS-PSO
KW - Open pit mining
KW - Prediction
KW - Rock fragmentation
UR - https://www.scopus.com/pages/publications/85122435505
U2 - 10.1007/978-3-030-80462-6_16
DO - 10.1007/978-3-030-80462-6_16
M3 - Contribución a la conferencia
AN - SCOPUS:85122435505
SN - 9783030804619
T3 - Lecture Notes in Networks and Systems
SP - 124
EP - 131
BT - Advances in Manufacturing, Production Management and Process Control - Proceedings of the AHFE 2021 Virtual Conferences on Human Aspects of Advanced Manufacturing, Advanced Production Management and Process Control, and Additive Manufacturing, Modeling Systems and 3D Prototyping, 2021
A2 - Trzcielinski, Stefan
A2 - Mrugalska, Beata
A2 - Karwowski, Waldemar
A2 - Rossi, Emilio
A2 - Di Nicolantonio, Massimo
PB - Springer Science and Business Media Deutschland GmbH
T2 - AHFE Conferences on Human Aspects of Advanced Manufacturing, Advanced Production Management and Process Control, and Additive Manufacturing, Modeling Systems and 3D Prototyping, 2021
Y2 - 25 July 2021 through 29 July 2021
ER -