o:3433 Smart mining: joint model for parametrization of coal excavation process based on artificial neural networks en ABSTRACT In the present paper we propose a new artificial neural network model for the estimation of coal cutting resistance and excavator performance as a nonlinear relationship between the examined input (excavator movement angle in the left and right direction, slice height and thickness, coal unit weight, compressive and shear strength) and output factors (excavator effective capacity, maximum current/power/force/energy consumption, linear and areal cutting resistance). We analyze the dataset collected from three open-pit coal mines in Serbia: Field D, Tamnava Eastern Field and Tamnava Western Field (all part of the Kolubara coal basin). The model is developed using a multilayer feed-forward neural network, with a Levenberg-Marquardt learning algorithm. Results of the preformed analysis indicate satisfying statistical accuracyof the developed model (R>0.9). Additionally, we analyze the individual effects of input factors on the properties of coal cutting resistance and performance of the excavator, by invokling the multiple linear regression. As a result, we single out the statististically significant and physically possible interactions between the individual controlling factors. artificial neural networks, surface coal mining, bucket wheel excavator 1552101 1840-4855 1552099 10.59456/afts.2023.1529.011T 2023-12-01T09:05:57.993Z 44 no 46 Jelena Trivan Faculty of Mining Prijedor, University of Banja Luka Srđan Kostić Jaroslav Černi Water Institute, Belgrade person 0000-0002-3705-3080 application/pdf 945182 https://unilib.phaidrabg.rs/o:3433 no yes 1 70 92000001 71A04 Archives for Technical Sciences 29 1 11 22 2023 Tehnički institut, Bjeljina Received September 2023 accepted November 2023