Abstract
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.
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Vergara, B., Torres, M., Aramburu, V., Raymundo, C. (2021). 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. In: Trzcielinski, S., Mrugalska, B., Karwowski, W., Rossi, E., Di Nicolantonio, M. (eds) Advances in Manufacturing, Production Management and Process Control. AHFE 2021. Lecture Notes in Networks and Systems, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-030-80462-6_16
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DOI: https://doi.org/10.1007/978-3-030-80462-6_16
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