o:3152 Deep Neural Network Model for Determination of Coal Cutting Resistance and Performance of Bucket-Wheel Excavator Based on the Environmental Properties and Excavation Parameters en Abstract: In the present paper, we develop a new model, based on the implementation of deep neural networks, for the estimation of a series of excavation parameters, depending on the main environmental and excavation properties. The developed model, with high statistical accuracy (R > 0.79) and small RMSE (<17% of the actual output values), enables the simultaneous assessment of the following excavation parameters: effective capacity Qef, maximum current consumption Imax, maximum power consumption Nmax, maximum force consumption Pmax, maximum energy consumption Emax, and maximum linear and areal cutting resistance, KLmax and KFmax, respectively, based on the impact of the following environmental properties and excavation parameters: coal unit weight, coal compression strength, coal cohesion, friction angle, excavator movement angle in the left and right direction, slice height and thickness, and wheel velocity. The data analyzed in the present paper were previously collected from three neighboring open-pit coal mines in Serbia: Tamnava Western Field, Tamnava Eastern Field, and Field D. These mines have similar geological conditions and coal properties. Additionally, for each output factor, a complex analysis is provided on the impact of the examined input factors, by applying the multiple linear regression method. As far as we are aware, this is the first time such a comprehensive estimation model has been suggested for the operation of a bucket-wheel excavator in the Neogene coal basins. The deep neural network (DNN) model, trained over 300 epochs, shows an MSE range of 6.7–16.5% across various input factors, with distinct evaluations for Imax due to its unique values (4.8–12.5%). coal surface mining; bucket-wheel excavator; interactions; deep neural network 1552101 2227-9717 1552102 https://doi.org/10.3390/pr11113067 2023-10-30T12:41:16.622Z 44 no 46 Srđan Kostić Institut za vodoprivredu "Jaroslav Černi" ; Jaroslav Černi Water Institute 0000-0002-3705-3080 Jelena Trivan Faculty of Mining, University of Banja Luka person Velibor Ilić The Institute for Artificial Intelligence Research and Development of Serbia person Milan Stojković The Institute for Artificial Intelligence Research and Development of Serbia person application/pdf 6183663 https://unilib.phaidrabg.rs/o:3152 no yes 16 70 92000001 71A04 Processes 11 3067 1 22 MDPI, Basel, Switzerland 2023 Received: 18 September 2023 Revised: 18 October 2023 Accepted: 24 October 2023 Published: 26 October 2023